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"""
Deployment utilities for publishing to HuggingFace Spaces.
Handles authentication, space creation, and code deployment.
"""
import os
import re
import json
import uuid
import tempfile
import shutil
from typing import Dict, List, Optional, Tuple
from urllib.parse import urlparse
import requests
from bs4 import BeautifulSoup
import html2text
import gradio as gr
from huggingface_hub import HfApi, InferenceClient
from openai import OpenAI
from .config import HF_TOKEN, get_gradio_language
from .parsers import (
parse_transformers_js_output, parse_multipage_html_output,
parse_multi_file_python_output, parse_react_output,
remove_code_block, is_streamlit_code, is_gradio_code,
clean_requirements_txt_content, History,
format_transformers_js_output, build_transformers_inline_html,
send_transformers_to_sandbox, validate_and_autofix_files,
inline_multipage_into_single_preview, apply_search_replace_changes,
apply_transformers_js_search_replace_changes, send_to_sandbox,
format_multi_file_python_output, send_streamlit_to_stlite,
send_gradio_to_lite, extract_html_document
)
from .models import (
get_inference_client, get_real_model_id, history_to_messages,
history_to_chatbot_messages, strip_placeholder_thinking,
is_placeholder_thinking_only, extract_last_thinking_line,
strip_thinking_tags
)
from . import prompts
from .prompts import (
HTML_SYSTEM_PROMPT,
TRANSFORMERS_JS_SYSTEM_PROMPT, STREAMLIT_SYSTEM_PROMPT,
REACT_SYSTEM_PROMPT, REACT_FOLLOW_UP_SYSTEM_PROMPT,
JSON_SYSTEM_PROMPT,
GENERIC_SYSTEM_PROMPT, MULTIPAGE_HTML_SYSTEM_PROMPT,
DYNAMIC_MULTIPAGE_HTML_SYSTEM_PROMPT,
FollowUpSystemPrompt, GradioFollowUpSystemPrompt,
TransformersJSFollowUpSystemPrompt
)
from .docs_manager import get_comfyui_system_prompt, update_gradio_system_prompts, update_json_system_prompts
def check_authentication(profile: Optional[gr.OAuthProfile] = None, token: Optional[gr.OAuthToken] = None) -> Tuple[bool, str]:
"""Check if user is authenticated and return status with message."""
if not profile or not token:
return False, "Please log in with your Hugging Face account to use AnyCoder."
if not token.token:
return False, "Authentication token is invalid. Please log in again."
return True, f"Authenticated as {profile.username}"
def update_ui_for_auth_status(profile: Optional[gr.OAuthProfile] = None, token: Optional[gr.OAuthToken] = None):
"""Update UI components based on authentication status."""
is_authenticated, auth_message = check_authentication(profile, token)
if is_authenticated:
# User is authenticated - enable all components
return (
gr.update(interactive=True, placeholder="Describe your application..."), # input
gr.update(interactive=True, variant="primary") # btn
)
else:
# User not authenticated - disable main components
return (
gr.update(
interactive=False,
placeholder="🔒 Click Sign in with Hugging Face button to use AnyCoder for free"
), # input
gr.update(interactive=False, variant="secondary") # btn
)
def generation_code(query: Optional[str], _setting: Dict[str, str], _history: Optional[History], _current_model: Dict, language: str = "html", provider: str = "auto", profile: Optional[gr.OAuthProfile] = None, token: Optional[gr.OAuthToken] = None, code_output=None, history_output=None, history=None):
# Check authentication first
is_authenticated, auth_message = check_authentication(profile, token)
if not is_authenticated:
error_message = f"🔒 Authentication Required\n\n{auth_message}\n\nPlease click the 'Sign in with Hugging Face' button in the sidebar to continue."
if code_output is not None and history_output is not None:
yield {
code_output: error_message,
history_output: history_to_chatbot_messages(_history or []),
}
else:
yield (error_message, _history or [], history_to_chatbot_messages(_history or []))
return
# CRITICAL: Catch any HuggingFace API errors for non-HF models like Gemini 3
try:
yield from _generation_code_impl(query, _setting, _history, _current_model, language, provider, profile, token, code_output, history_output, history)
except Exception as e:
import traceback
error_str = str(e)
if "Repository Not Found" in error_str and "inferenceProviderMapping" in error_str:
# This is a HuggingFace API error for a non-HF model
model_id = _current_model.get('id', 'unknown')
# Get full traceback to see where the call originated
tb = traceback.format_exc()
print(f"DEBUG: HuggingFace API error for model {model_id}")
print(f"DEBUG: Full traceback:\n{tb}")
error_message = f"""❌ Error: Attempted to validate model '{model_id}' against HuggingFace API, but this is not a HuggingFace model.
This error should not occur. Please check the server logs for the full traceback.
- Model: {model_id}
- Error: {error_str}
Try reloading the page and selecting the model again."""
if code_output is not None and history_output is not None:
yield {
code_output: error_message,
history_output: history_to_chatbot_messages(_history or []),
}
else:
yield (error_message, _history or [], history_to_chatbot_messages(_history or []))
return
else:
# Re-raise other errors
raise
def _generation_code_impl(query: Optional[str], _setting: Dict[str, str], _history: Optional[History], _current_model: Dict, language: str = "html", provider: str = "auto", profile: Optional[gr.OAuthProfile] = None, token: Optional[gr.OAuthToken] = None, code_output=None, history_output=None, history=None):
"""Internal implementation of generation_code"""
if query is None:
query = ''
if _history is None:
_history = []
# Ensure _history is always a list of lists with at least 2 elements per item
if not isinstance(_history, list):
_history = []
_history = [h for h in _history if isinstance(h, list) and len(h) == 2]
# Check if there's existing content in history to determine if this is a modification request
has_existing_content = False
last_assistant_msg = ""
if _history and len(_history[-1]) > 1:
last_assistant_msg = _history[-1][1]
# Check if this is imported model code (should NOT be treated as existing content to modify)
is_imported_model_code = (
"Imported model:" in _history[-1][0] or
"Imported inference provider code" in last_assistant_msg or
"Imported transformers/diffusers code" in last_assistant_msg or
"Switched code type" in _history[-1][0]
)
# Only treat as existing content if it's NOT imported model code
if not is_imported_model_code:
# Check for various content types that indicate an existing project
if ('<!DOCTYPE html>' in last_assistant_msg or
'<html' in last_assistant_msg or
'import gradio' in last_assistant_msg or
'import streamlit' in last_assistant_msg or
'def ' in last_assistant_msg and 'app' in last_assistant_msg or
'IMPORTED PROJECT FROM HUGGING FACE SPACE' in last_assistant_msg or
'=== index.html ===' in last_assistant_msg or
'=== index.js ===' in last_assistant_msg or
'=== style.css ===' in last_assistant_msg or
'=== app.py ===' in last_assistant_msg or
'=== requirements.txt ===' in last_assistant_msg):
has_existing_content = True
# If this is a modification request, try to apply search/replace first
if has_existing_content and query.strip():
# Skip search/replace for models that use native clients (non-OpenAI-compatible)
# These models need the full generation flow to work properly
native_client_models = [] # All models now use OpenAI-compatible APIs
if _current_model['id'] not in native_client_models:
try:
# Use the current model to generate search/replace instructions
client = get_inference_client(_current_model['id'], provider)
system_prompt = """You are a code editor assistant. Given existing code and modification instructions, generate EXACT search/replace blocks.
CRITICAL REQUIREMENTS:
1. Use EXACTLY these markers: <<<<<<< SEARCH, =======, >>>>>>> REPLACE
2. The SEARCH block must match the existing code EXACTLY (including whitespace, indentation, line breaks)
3. The REPLACE block should contain the modified version
4. Only include the specific lines that need to change, with enough context to make them unique
5. Generate multiple search/replace blocks if needed for different changes
6. Do NOT include any explanations or comments outside the blocks
Example format:
<<<<<<< SEARCH
function oldFunction() {
return "old";
}
=======
function newFunction() {
return "new";
}
>>>>>>> REPLACE"""
user_prompt = f"""Existing code:
{last_assistant_msg}
Modification instructions:
{query}
Generate the exact search/replace blocks needed to make these changes."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
]
# Generate search/replace instructions
if _current_model.get('type') == 'openai':
response = client.chat.completions.create(
model=get_real_model_id(_current_model['id']),
messages=messages,
max_tokens=4000,
temperature=0.1
)
changes_text = response.choices[0].message.content
elif _current_model.get('type') == 'mistral':
response = client.chat.complete(
model=get_real_model_id(_current_model['id']),
messages=messages,
max_tokens=4000,
temperature=0.1
)
changes_text = response.choices[0].message.content
else: # Hugging Face or other
completion = client.chat.completions.create(
model=get_real_model_id(_current_model['id']),
messages=messages,
max_tokens=4000,
temperature=0.1
)
changes_text = completion.choices[0].message.content
# Apply the search/replace changes
if language == "transformers.js" and ('=== index.html ===' in last_assistant_msg):
modified_content = apply_transformers_js_search_replace_changes(last_assistant_msg, changes_text)
else:
modified_content = apply_search_replace_changes(last_assistant_msg, changes_text)
# If changes were successfully applied, return the modified content
if modified_content != last_assistant_msg:
_history.append([query, modified_content])
# Generate deployment message instead of preview
deploy_message = f"""
<div style='padding: 1.5em; text-align: center; background: #f0f9ff; border: 2px solid #0ea5e9; border-radius: 10px; color: #0c4a6e;'>
<h3 style='margin-top: 0; color: #0ea5e9;'>✅ Code Updated Successfully!</h3>
<p style='margin: 0.5em 0; font-size: 1.1em;'>Your {language.upper()} code has been modified and is ready for deployment.</p>
<p style='margin: 0.5em 0; font-weight: bold;'>👉 Use the Deploy button in the sidebar to publish your app!</p>
</div>
"""
yield {
code_output: modified_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
return
except Exception as e:
print(f"Search/replace failed, falling back to normal generation: {e}")
# If search/replace fails, continue with normal generation
# Create/lookup a session id for temp-file tracking and cleanup
if _setting is not None and isinstance(_setting, dict):
session_id = _setting.get("__session_id__")
if not session_id:
session_id = str(uuid.uuid4())
_setting["__session_id__"] = session_id
else:
session_id = str(uuid.uuid4())
# Update system prompts if needed
if language == "gradio":
update_gradio_system_prompts()
print(f"[Generation] Updated Gradio system prompt (length: {len(prompts.GRADIO_SYSTEM_PROMPT)} chars)")
elif language == "json":
update_json_system_prompts()
print(f"[Generation] Updated JSON system prompt (length: {len(prompts.JSON_SYSTEM_PROMPT)} chars)")
# Choose system prompt based on context
# Special case: If user is asking about model identity, use neutral prompt
if query and any(phrase in query.lower() for phrase in ["what model are you", "who are you", "identify yourself", "what ai are you", "which model"]):
system_prompt = "You are a helpful AI assistant. Please respond truthfully about your identity and capabilities."
elif has_existing_content:
# Use follow-up prompt for modifying existing content
if language == "transformers.js":
system_prompt = TransformersJSFollowUpSystemPrompt
elif language == "gradio":
system_prompt = GradioFollowUpSystemPrompt
elif language == "react":
system_prompt = REACT_FOLLOW_UP_SYSTEM_PROMPT
else:
system_prompt = FollowUpSystemPrompt
else:
# Use language-specific prompt
if language == "html":
# Dynamic file selection always enabled
system_prompt = DYNAMIC_MULTIPAGE_HTML_SYSTEM_PROMPT
elif language == "transformers.js":
system_prompt = TRANSFORMERS_JS_SYSTEM_PROMPT
elif language == "react":
system_prompt = REACT_SYSTEM_PROMPT
elif language == "gradio":
# Access GRADIO_SYSTEM_PROMPT from prompts module to get updated value
system_prompt = prompts.GRADIO_SYSTEM_PROMPT
elif language == "streamlit":
system_prompt = STREAMLIT_SYSTEM_PROMPT
elif language == "json":
# Access JSON_SYSTEM_PROMPT from prompts module to get updated value
system_prompt = prompts.JSON_SYSTEM_PROMPT
elif language == "comfyui":
system_prompt = get_comfyui_system_prompt()
else:
system_prompt = GENERIC_SYSTEM_PROMPT.format(language=language)
# Debug: Log system prompt info
prompt_preview = system_prompt[:200] if system_prompt else "None"
print(f"[Generation] Using system prompt (first 200 chars): {prompt_preview}...")
print(f"[Generation] System prompt total length: {len(system_prompt) if system_prompt else 0} chars")
messages = history_to_messages(_history, system_prompt)
# Use the original query without any enhancements - let the system prompt handle everything
enhanced_query = query
# Check if this is GLM-4.5 model and handle with simple HuggingFace InferenceClient
if _current_model["id"] == "zai-org/GLM-4.5":
messages.append({'role': 'user', 'content': enhanced_query})
try:
client = InferenceClient(
provider="auto",
api_key=os.environ["HF_TOKEN"],
bill_to="huggingface",
)
stream = client.chat.completions.create(
model="zai-org/GLM-4.5",
messages=messages,
stream=True,
max_tokens=16384,
)
content = ""
for chunk in stream:
if chunk.choices[0].delta.content:
content += chunk.choices[0].delta.content
clean_code = remove_code_block(content)
# Show generation progress message
progress_message = f"""
<div style='padding: 1.5em; text-align: center; background: linear-gradient(135deg, #f59e0b 0%, #d97706 100%); color: white; border-radius: 10px;'>
<h3 style='margin-top: 0; color: white;'>⚡ Generating Your {language.upper()} App...</h3>
<p style='margin: 0.5em 0; opacity: 0.9;'>Code is being generated in real-time!</p>
<div style='background: rgba(255,255,255,0.2); padding: 1em; border-radius: 8px; margin: 1em 0;'>
<p style='margin: 0; font-size: 1.1em;'>Get ready to deploy once generation completes!</p>
</div>
</div>
"""
yield {
code_output: gr.update(value=clean_code, language=get_gradio_language(language)),
history_output: history_to_chatbot_messages(_history),
}
except Exception as e:
content = f"Error with GLM-4.5: {str(e)}\n\nPlease make sure HF_TOKEN environment variable is set."
clean_code = remove_code_block(content)
# Use clean code as final content without media generation
final_content = clean_code
_history.append([query, final_content])
if language == "transformers.js":
files = parse_transformers_js_output(clean_code)
if files['index.html'] and files['index.js'] and files['style.css']:
formatted_output = format_transformers_js_output(files)
yield {
code_output: formatted_output,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
yield {
code_output: clean_code,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
if has_existing_content and not (clean_code.strip().startswith("<!DOCTYPE html>") or clean_code.strip().startswith("<html")):
last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else ""
modified_content = apply_search_replace_changes(last_content, clean_code)
clean_content = remove_code_block(modified_content)
# Use clean content without media generation
yield {
code_output: clean_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
# Use clean code as final content without media generation
final_content = clean_code
# Generate deployment message instead of preview
deploy_message = f"""
<div style='padding: 2em; text-align: center; background: linear-gradient(135deg, #10b981 0%, #059669 100%); color: white; border-radius: 12px; box-shadow: 0 4px 20px rgba(16, 185, 129, 0.3);'>
<h2 style='margin-top: 0; font-size: 2em;'>🎉 Code Generated Successfully!</h2>
<p style='font-size: 1.2em; margin: 1em 0; opacity: 0.95;'>Your {language.upper()} application is ready to deploy!</p>
<div style='background: rgba(255,255,255,0.15); padding: 1.5em; border-radius: 10px; margin: 1.5em 0;'>
<h3 style='margin-top: 0; font-size: 1.3em;'>🚀 Next Steps:</h3>
<div style='text-align: left; max-width: 500px; margin: 0 auto;'>
<p style='margin: 0.8em 0; font-size: 1.1em; display: flex; align-items: center;'>
<span style='background: rgba(255,255,255,0.2); border-radius: 50%; width: 24px; height: 24px; display: inline-flex; align-items: center; justify-content: center; margin-right: 10px; font-weight: bold;'>1</span>
Use the <strong>Deploy button</strong> in the sidebar
</p>
<p style='margin: 0.8em 0; font-size: 1.1em; display: flex; align-items: center;'>
<span style='background: rgba(255,255,255,0.2); border-radius: 50%; width: 24px; height: 24px; display: inline-flex; align-items: center; justify-content: center; margin-right: 10px; font-weight: bold;'>2</span>
Enter your app name below
</p>
<p style='margin: 0.8em 0; font-size: 1.1em; display: flex; align-items: center;'>
<span style='background: rgba(255,255,255,0.2); border-radius: 50%; width: 24px; height: 24px; display: inline-flex; align-items: center; justify-content: center; margin-right: 10px; font-weight: bold;'>3</span>
Click <strong>"Publish"</strong>
</p>
<p style='margin: 0.8em 0; font-size: 1.1em; display: flex; align-items: center;'>
<span style='background: rgba(255,255,255,0.2); border-radius: 50%; width: 24px; height: 24px; display: inline-flex; align-items: center; justify-content: center; margin-right: 10px; font-weight: bold;'>4</span>
Share your creation! 🌍
</p>
</div>
</div>
<p style='font-size: 1em; opacity: 0.9; margin-bottom: 0;'>
💡 Your app will be live on Hugging Face Spaces in seconds!
</p>
</div>
"""
yield {
code_output: final_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
return
# Use dynamic client based on selected model
client = get_inference_client(_current_model["id"], provider)
messages.append({'role': 'user', 'content': enhanced_query})
try:
# Handle Mistral API method difference
if _current_model["id"] in ("codestral-2508", "mistral-medium-2508"):
completion = client.chat.stream(
model=get_real_model_id(_current_model["id"]),
messages=messages,
max_tokens=16384
)
else:
# Poe expects model id "GPT-5" and uses max_tokens
if _current_model["id"] == "gpt-5":
completion = client.chat.completions.create(
model="GPT-5",
messages=messages,
stream=True,
max_tokens=16384
)
elif _current_model["id"] == "grok-4":
completion = client.chat.completions.create(
model="Grok-4",
messages=messages,
stream=True,
max_tokens=16384
)
elif _current_model["id"] == "claude-opus-4.1":
completion = client.chat.completions.create(
model="Claude-Opus-4.1",
messages=messages,
stream=True,
max_tokens=16384
)
elif _current_model["id"] == "claude-sonnet-4.5":
completion = client.chat.completions.create(
model="Claude-Sonnet-4.5",
messages=messages,
stream=True,
max_tokens=16384
)
elif _current_model["id"] == "claude-haiku-4.5":
completion = client.chat.completions.create(
model="Claude-Haiku-4.5",
messages=messages,
stream=True,
max_tokens=16384
)
else:
completion = client.chat.completions.create(
model=get_real_model_id(_current_model["id"]),
messages=messages,
stream=True,
max_tokens=16384
)
content = ""
# For Poe/GPT-5, maintain a simple code-fence state machine to only accumulate code
poe_inside_code_block = False
poe_partial_buffer = ""
for chunk in completion:
# Handle different response formats for Mistral vs others
chunk_content = None
if _current_model["id"] in ("codestral-2508", "mistral-medium-2508"):
# Mistral format: chunk.data.choices[0].delta.content
if (
hasattr(chunk, "data") and chunk.data and
hasattr(chunk.data, "choices") and chunk.data.choices and
hasattr(chunk.data.choices[0], "delta") and
hasattr(chunk.data.choices[0].delta, "content") and
chunk.data.choices[0].delta.content is not None
):
chunk_content = chunk.data.choices[0].delta.content
else:
# OpenAI format: chunk.choices[0].delta.content
if (
hasattr(chunk, "choices") and chunk.choices and
hasattr(chunk.choices[0], "delta") and
hasattr(chunk.choices[0].delta, "content") and
chunk.choices[0].delta.content is not None
):
chunk_content = chunk.choices[0].delta.content
if chunk_content:
# Ensure chunk_content is always a string to avoid regex errors
if not isinstance(chunk_content, str):
# Handle structured thinking chunks (like ThinkChunk objects from magistral)
chunk_str = str(chunk_content) if chunk_content is not None else ""
if '[ThinkChunk(' in chunk_str:
# This is a structured thinking chunk, skip it to avoid polluting output
continue
chunk_content = chunk_str
# Strip thinking tags and tool call markers from all streaming chunks
chunk_content = strip_thinking_tags(chunk_content)
if _current_model["id"] == "gpt-5":
# If this chunk is only placeholder thinking, surface a status update without polluting content
if is_placeholder_thinking_only(chunk_content):
status_line = extract_last_thinking_line(chunk_content)
yield {
code_output: gr.update(value=(content or "") + "\n<!-- " + status_line + " -->", language="html"),
history_output: history_to_chatbot_messages(_history),
}
continue
# Filter placeholders
incoming = strip_placeholder_thinking(chunk_content)
# Process code fences incrementally, only keep content inside fences
s = poe_partial_buffer + incoming
append_text = ""
i = 0
# Find all triple backticks positions
for m in re.finditer(r"```", s):
if not poe_inside_code_block:
# Opening fence. Require a newline to confirm full opener so we can skip optional language line
nl = s.find("\n", m.end())
if nl == -1:
# Incomplete opener; buffer from this fence and wait for more
poe_partial_buffer = s[m.start():]
s = None
break
# Enter code, skip past newline after optional language token
poe_inside_code_block = True
i = nl + 1
else:
# Closing fence, append content inside and exit code
append_text += s[i:m.start()]
poe_inside_code_block = False
i = m.end()
if s is not None:
if poe_inside_code_block:
append_text += s[i:]
poe_partial_buffer = ""
else:
poe_partial_buffer = s[i:]
if append_text:
content += append_text
else:
# Append content, filtering out placeholder thinking lines
content += strip_placeholder_thinking(chunk_content)
search_status = ""
# Handle transformers.js output differently
if language == "transformers.js":
files = parse_transformers_js_output(content)
# Stream ALL code by merging current parts into a single HTML (inline CSS & JS)
has_any_part = any([files.get('index.html'), files.get('index.js'), files.get('style.css')])
if has_any_part:
merged_html = build_transformers_inline_html(files)
preview_val = None
if files['index.html'] and files['index.js'] and files['style.css']:
preview_val = send_transformers_to_sandbox(files)
yield {
code_output: gr.update(value=merged_html, language="html"),
history_output: history_to_chatbot_messages(_history),
}
elif has_existing_content:
# Model is returning search/replace changes for transformers.js - apply them
last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else ""
modified_content = apply_transformers_js_search_replace_changes(last_content, content)
_mf = parse_transformers_js_output(modified_content)
yield {
code_output: gr.update(value=modified_content, language="html"),
history_output: history_to_chatbot_messages(_history),
}
else:
# Still streaming, show partial content
yield {
code_output: gr.update(value=content, language="html"),
history_output: history_to_chatbot_messages(_history),
}
else:
clean_code = remove_code_block(content)
if has_existing_content:
# Handle modification of existing content
if clean_code.strip().startswith("<!DOCTYPE html>") or clean_code.strip().startswith("<html"):
# Model returned a complete HTML file
preview_val = None
if language == "html":
_mpc3 = parse_multipage_html_output(clean_code)
_mpc3 = validate_and_autofix_files(_mpc3)
preview_val = send_to_sandbox(inline_multipage_into_single_preview(_mpc3)) if _mpc3.get('index.html') else send_to_sandbox(clean_code)
elif language == "python" and is_streamlit_code(clean_code):
preview_val = send_streamlit_to_stlite(clean_code)
elif language == "gradio" or (language == "python" and is_gradio_code(clean_code)):
preview_val = send_gradio_to_lite(clean_code)
yield {
code_output: gr.update(value=clean_code, language=get_gradio_language(language)),
history_output: history_to_chatbot_messages(_history),
}
else:
# Model returned search/replace changes - apply them
last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else ""
modified_content = apply_search_replace_changes(last_content, clean_code)
clean_content = remove_code_block(modified_content)
preview_val = None
if language == "html":
_mpc4 = parse_multipage_html_output(clean_content)
_mpc4 = validate_and_autofix_files(_mpc4)
preview_val = send_to_sandbox(inline_multipage_into_single_preview(_mpc4)) if _mpc4.get('index.html') else send_to_sandbox(clean_content)
elif language == "python" and is_streamlit_code(clean_content):
preview_val = send_streamlit_to_stlite(clean_content)
elif language == "gradio" or (language == "python" and is_gradio_code(clean_content)):
preview_val = send_gradio_to_lite(clean_content)
yield {
code_output: gr.update(value=clean_content, language=get_gradio_language(language)),
history_output: history_to_chatbot_messages(_history),
}
else:
preview_val = None
if language == "html":
_mpc5 = parse_multipage_html_output(clean_code)
_mpc5 = validate_and_autofix_files(_mpc5)
preview_val = send_to_sandbox(inline_multipage_into_single_preview(_mpc5)) if _mpc5.get('index.html') else send_to_sandbox(clean_code)
elif language == "python" and is_streamlit_code(clean_code):
preview_val = send_streamlit_to_stlite(clean_code)
elif language == "gradio" or (language == "python" and is_gradio_code(clean_code)):
preview_val = send_gradio_to_lite(clean_code)
yield {
code_output: gr.update(value=clean_code, language=get_gradio_language(language)),
history_output: history_to_chatbot_messages(_history),
}
# Skip chunks with empty choices (end of stream)
# Do not treat as error
# Handle response based on whether this is a modification or new generation
if language == "transformers.js":
# Handle transformers.js output
files = parse_transformers_js_output(content)
if files['index.html'] and files['index.js'] and files['style.css']:
# Model returned complete transformers.js output
formatted_output = format_transformers_js_output(files)
_history.append([query, formatted_output])
yield {
code_output: formatted_output,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
elif has_existing_content:
# Model returned search/replace changes for transformers.js - apply them
last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else ""
modified_content = apply_transformers_js_search_replace_changes(last_content, content)
_history.append([query, modified_content])
_mf = parse_transformers_js_output(modified_content)
yield {
code_output: modified_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
# Fallback if parsing failed
_history.append([query, content])
yield {
code_output: content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
elif language == "gradio":
# Handle Gradio output - check if it's multi-file format or single file
if ('=== app.py ===' in content or '=== requirements.txt ===' in content):
# Model returned multi-file Gradio output - ensure requirements.txt is present
files = parse_multi_file_python_output(content)
if files and 'app.py' in files:
# Check if requirements.txt is missing and auto-generate it
if 'requirements.txt' not in files:
import_statements = extract_import_statements(files['app.py'])
requirements_content = generate_requirements_txt_with_llm(import_statements)
files['requirements.txt'] = requirements_content
# Reformat with the auto-generated requirements.txt
content = format_multi_file_python_output(files)
_history.append([query, content])
yield {
code_output: content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
elif has_existing_content:
# Check if this is a followup that should maintain multi-file structure
last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else ""
# If the original was multi-file but the response isn't, try to convert it
if ('=== app.py ===' in last_content or '=== requirements.txt ===' in last_content):
# Original was multi-file, but response is single block - need to convert
if not ('=== app.py ===' in content or '=== requirements.txt ===' in content):
# Try to parse as single-block Gradio code and convert to multi-file format
clean_content = remove_code_block(content)
if 'import gradio' in clean_content or 'from gradio' in clean_content:
# This looks like Gradio code, convert to multi-file format
files = parse_multi_file_python_output(clean_content)
if not files:
# Single file - create multi-file structure
files = {'app.py': clean_content}
# Extract requirements from imports
import_statements = extract_import_statements(clean_content)
requirements_content = generate_requirements_txt_with_llm(import_statements)
files['requirements.txt'] = requirements_content
# Format as multi-file output
formatted_content = format_multi_file_python_output(files)
_history.append([query, formatted_content])
yield {
code_output: formatted_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
# Not Gradio code, apply search/replace
modified_content = apply_search_replace_changes(last_content, content)
_history.append([query, modified_content])
yield {
code_output: modified_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
# Response is already multi-file format
_history.append([query, content])
yield {
code_output: content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
# Original was single file, apply search/replace
modified_content = apply_search_replace_changes(last_content, content)
_history.append([query, modified_content])
yield {
code_output: modified_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
# Fallback - treat as single file Gradio app
_history.append([query, content])
yield {
code_output: content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
elif has_existing_content:
# Handle modification of existing content
final_code = remove_code_block(content)
if final_code.strip().startswith("<!DOCTYPE html>") or final_code.strip().startswith("<html"):
# Model returned a complete HTML file
clean_content = final_code
else:
# Model returned search/replace changes - apply them
last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else ""
modified_content = apply_search_replace_changes(last_content, final_code)
clean_content = remove_code_block(modified_content)
# Use clean content without media generation
# Update history with the cleaned content
_history.append([query, clean_content])
yield {
code_output: clean_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
else:
# Regular generation - use the content as is
final_content = remove_code_block(content)
# Use final content without media generation
_history.append([query, final_content])
# Generate deployment message instead of preview
deploy_message = f"""
<div style='padding: 2em; text-align: center; background: linear-gradient(135deg, #10b981 0%, #059669 100%); color: white; border-radius: 12px; box-shadow: 0 4px 20px rgba(16, 185, 129, 0.3);'>
<h2 style='margin-top: 0; font-size: 2em;'>🎉 Code Generated Successfully!</h2>
<p style='font-size: 1.2em; margin: 1em 0; opacity: 0.95;'>Your {language.upper()} application is ready to deploy!</p>
<div style='background: rgba(255,255,255,0.15); padding: 1.5em; border-radius: 10px; margin: 1.5em 0;'>
<h3 style='margin-top: 0; font-size: 1.3em;'>🚀 Next Steps:</h3>
<div style='text-align: left; max-width: 500px; margin: 0 auto;'>
<p style='margin: 0.8em 0; font-size: 1.1em; display: flex; align-items: center;'>
<span style='background: rgba(255,255,255,0.2); border-radius: 50%; width: 24px; height: 24px; display: inline-flex; align-items: center; justify-content: center; margin-right: 10px; font-weight: bold;'>1</span>
Use the <strong>Deploy button</strong> in the sidebar
</p>
<p style='margin: 0.8em 0; font-size: 1.1em; display: flex; align-items: center;'>
<span style='background: rgba(255,255,255,0.2); border-radius: 50%; width: 24px; height: 24px; display: inline-flex; align-items: center; justify-content: center; margin-right: 10px; font-weight: bold;'>2</span>
Enter your app name below
</p>
<p style='margin: 0.8em 0; font-size: 1.1em; display: flex; align-items: center;'>
<span style='background: rgba(255,255,255,0.2); border-radius: 50%; width: 24px; height: 24px; display: inline-flex; align-items: center; justify-content: center; margin-right: 10px; font-weight: bold;'>3</span>
Click <strong>"Publish"</strong>
</p>
<p style='margin: 0.8em 0; font-size: 1.1em; display: flex; align-items: center;'>
<span style='background: rgba(255,255,255,0.2); border-radius: 50%; width: 24px; height: 24px; display: inline-flex; align-items: center; justify-content: center; margin-right: 10px; font-weight: bold;'>4</span>
Share your creation! 🌍
</p>
</div>
</div>
<p style='font-size: 1em; opacity: 0.9; margin-bottom: 0;'>
💡 Your app will be live on Hugging Face Spaces in seconds!
</p>
</div>
"""
yield {
code_output: final_content,
history: _history,
history_output: history_to_chatbot_messages(_history),
}
except Exception as e:
error_message = f"Error: {str(e)}"
yield {
code_output: error_message,
history_output: history_to_chatbot_messages(_history),
}
# Deploy to Spaces logic
def add_anycoder_tag_to_readme(api, repo_id, app_port=None):
"""Download existing README, add anycoder tag and app_port if needed, and upload back.
Args:
api: HuggingFace API client
repo_id: Repository ID
app_port: Optional port number to set for Docker spaces (e.g., 7860 for React apps)
"""
try:
import tempfile
import re
# Download the existing README
readme_path = api.hf_hub_download(
repo_id=repo_id,
filename="README.md",
repo_type="space"
)
# Read the existing README content
with open(readme_path, 'r', encoding='utf-8') as f:
content = f.read()
# Parse frontmatter and content
if content.startswith('---'):
# Split frontmatter and body
parts = content.split('---', 2)
if len(parts) >= 3:
frontmatter = parts[1].strip()
body = parts[2] if len(parts) > 2 else ""
# Check if tags already exist
if 'tags:' in frontmatter:
# Add anycoder to existing tags if not present
if '- anycoder' not in frontmatter:
frontmatter = re.sub(r'(tags:\s*\n(?:\s*-\s*[^\n]+\n)*)', r'\1- anycoder\n', frontmatter)
else:
# Add tags section with anycoder
frontmatter += '\ntags:\n- anycoder'
# Add app_port if specified and not already present
if app_port is not None and 'app_port:' not in frontmatter:
frontmatter += f'\napp_port: {app_port}'
# Reconstruct the README
new_content = f"---\n{frontmatter}\n---{body}"
else:
# Malformed frontmatter, just add tags at the end of frontmatter
new_content = content.replace('---', '---\ntags:\n- anycoder\n---', 1)
else:
# No frontmatter, add it at the beginning
app_port_line = f'\napp_port: {app_port}' if app_port else ''
new_content = f"---\ntags:\n- anycoder{app_port_line}\n---\n\n{content}"
# Upload the modified README
with tempfile.NamedTemporaryFile("w", suffix=".md", delete=False, encoding='utf-8') as f:
f.write(new_content)
temp_path = f.name
api.upload_file(
path_or_fileobj=temp_path,
path_in_repo="README.md",
repo_id=repo_id,
repo_type="space"
)
import os
os.unlink(temp_path)
except Exception as e:
print(f"Warning: Could not modify README.md to add anycoder tag: {e}")
def extract_import_statements(code):
"""Extract import statements from generated code."""
import ast
import re
import_statements = []
# Built-in Python modules to exclude
builtin_modules = {
'os', 'sys', 'json', 'time', 'datetime', 'random', 'math', 're', 'collections',
'itertools', 'functools', 'pathlib', 'urllib', 'http', 'email', 'html', 'xml',
'csv', 'tempfile', 'shutil', 'subprocess', 'threading', 'multiprocessing',
'asyncio', 'logging', 'typing', 'base64', 'hashlib', 'secrets', 'uuid',
'copy', 'pickle', 'io', 'contextlib', 'warnings', 'sqlite3', 'gzip', 'zipfile',
'tarfile', 'socket', 'ssl', 'platform', 'getpass', 'pwd', 'grp', 'stat',
'glob', 'fnmatch', 'linecache', 'traceback', 'inspect', 'keyword', 'token',
'tokenize', 'ast', 'code', 'codeop', 'dis', 'py_compile', 'compileall',
'importlib', 'pkgutil', 'modulefinder', 'runpy', 'site', 'sysconfig'
}
try:
# Try to parse as Python AST
tree = ast.parse(code)
for node in ast.walk(tree):
if isinstance(node, ast.Import):
for alias in node.names:
module_name = alias.name.split('.')[0]
if module_name not in builtin_modules and not module_name.startswith('_'):
import_statements.append(f"import {alias.name}")
elif isinstance(node, ast.ImportFrom):
if node.module:
module_name = node.module.split('.')[0]
if module_name not in builtin_modules and not module_name.startswith('_'):
names = [alias.name for alias in node.names]
import_statements.append(f"from {node.module} import {', '.join(names)}")
except SyntaxError:
# Fallback: use regex to find import statements
for line in code.split('\n'):
line = line.strip()
if line.startswith('import ') or line.startswith('from '):
# Check if it's not a builtin module
if line.startswith('import '):
module_name = line.split()[1].split('.')[0]
elif line.startswith('from '):
module_name = line.split()[1].split('.')[0]
if module_name not in builtin_modules and not module_name.startswith('_'):
import_statements.append(line)
return list(set(import_statements)) # Remove duplicates
def generate_requirements_txt_with_llm(import_statements):
"""Generate requirements.txt content using LLM based on import statements."""
if not import_statements:
return "# No additional dependencies required\n"
# Use a lightweight model for this task
try:
client = get_inference_client("zai-org/GLM-4.6", "auto")
imports_text = '\n'.join(import_statements)
prompt = f"""Based on the following Python import statements, generate a comprehensive requirements.txt file with all necessary and commonly used related packages:
{imports_text}
Instructions:
- Include the direct packages needed for the imports
- Include commonly used companion packages and dependencies for better functionality
- Use correct PyPI package names (e.g., PIL -> Pillow, sklearn -> scikit-learn)
- IMPORTANT: For diffusers, ALWAYS use: git+https://github.com/huggingface/diffusers
- IMPORTANT: For transformers, ALWAYS use: git+https://github.com/huggingface/transformers
- IMPORTANT: If diffusers is installed, also include transformers and sentencepiece as they usually go together
- Examples of comprehensive dependencies:
* diffusers often needs: git+https://github.com/huggingface/transformers, sentencepiece, accelerate, torch, tokenizers
* transformers often needs: accelerate, torch, tokenizers, datasets
* gradio often needs: requests, Pillow for image handling
* pandas often needs: numpy, openpyxl for Excel files
* matplotlib often needs: numpy, pillow for image saving
* sklearn often needs: numpy, scipy, joblib
* streamlit often needs: pandas, numpy, requests
* opencv-python often needs: numpy, pillow
* fastapi often needs: uvicorn, pydantic
* torch often needs: torchvision, torchaudio (if doing computer vision/audio)
- Include packages for common file formats if relevant (openpyxl, python-docx, PyPDF2)
- Do not include Python built-in modules
- Do not specify versions unless there are known compatibility issues
- One package per line
- If no external packages are needed, return "# No additional dependencies required"
🚨 CRITICAL OUTPUT FORMAT:
- Output ONLY the package names, one per line (plain text format)
- Do NOT use markdown formatting (no ```, no bold, no headings, no lists)
- Do NOT add any explanatory text before or after the package list
- Do NOT wrap the output in code blocks
- Just output raw package names as they would appear in requirements.txt
Generate a comprehensive requirements.txt that ensures the application will work smoothly:"""
messages = [
{"role": "system", "content": "You are a Python packaging expert specializing in creating comprehensive, production-ready requirements.txt files. Output ONLY plain text package names without any markdown formatting, code blocks, or explanatory text. Your goal is to ensure applications work smoothly by including not just direct dependencies but also commonly needed companion packages, popular extensions, and supporting libraries that developers typically need together."},
{"role": "user", "content": prompt}
]
response = client.chat.completions.create(
model="zai-org/GLM-4.6",
messages=messages,
max_tokens=1024,
temperature=0.1
)
requirements_content = response.choices[0].message.content.strip()
# Clean up the response in case it includes extra formatting
if '```' in requirements_content:
# Use the existing remove_code_block function for consistent cleaning
requirements_content = remove_code_block(requirements_content)
# Enhanced cleanup for markdown and formatting
lines = requirements_content.split('\n')
clean_lines = []
for line in lines:
stripped_line = line.strip()
# Skip lines that are markdown formatting
if (stripped_line == '```' or
stripped_line.startswith('```') or
stripped_line.startswith('#') and not stripped_line.startswith('# ') or # Skip markdown headers but keep comments
stripped_line.startswith('**') or # Skip bold text
stripped_line.startswith('*') and not stripped_line[1:2].isalnum() or # Skip markdown lists but keep package names starting with *
stripped_line.startswith('-') and not stripped_line[1:2].isalnum() or # Skip markdown lists but keep package names starting with -
stripped_line.startswith('===') or # Skip section dividers
stripped_line.startswith('---') or # Skip horizontal rules
stripped_line.lower().startswith('here') or # Skip explanatory text
stripped_line.lower().startswith('this') or # Skip explanatory text
stripped_line.lower().startswith('the') or # Skip explanatory text
stripped_line.lower().startswith('based on') or # Skip explanatory text
stripped_line == ''): # Skip empty lines unless they're at natural boundaries
continue
# Keep lines that look like valid package specifications
# Valid lines: package names, git+https://, comments starting with "# "
if (stripped_line.startswith('# ') or # Valid comments
stripped_line.startswith('git+') or # Git dependencies
stripped_line[0].isalnum() or # Package names start with alphanumeric
'==' in stripped_line or # Version specifications
'>=' in stripped_line or # Version specifications
'<=' in stripped_line): # Version specifications
clean_lines.append(line)
requirements_content = '\n'.join(clean_lines).strip()
# Ensure it ends with a newline
if requirements_content and not requirements_content.endswith('\n'):
requirements_content += '\n'
return requirements_content if requirements_content else "# No additional dependencies required\n"
except Exception as e:
# Fallback: simple extraction with basic mapping
dependencies = set()
special_cases = {
'PIL': 'Pillow',
'sklearn': 'scikit-learn',
'skimage': 'scikit-image',
'bs4': 'beautifulsoup4'
}
for stmt in import_statements:
if stmt.startswith('import '):
module_name = stmt.split()[1].split('.')[0]
package_name = special_cases.get(module_name, module_name)
dependencies.add(package_name)
elif stmt.startswith('from '):
module_name = stmt.split()[1].split('.')[0]
package_name = special_cases.get(module_name, module_name)
dependencies.add(package_name)
if dependencies:
return '\n'.join(sorted(dependencies)) + '\n'
else:
return "# No additional dependencies required\n"
def wrap_html_in_gradio_app(html_code):
# Escape triple quotes for safe embedding
safe_html = html_code.replace('"""', r'\"\"\"')
# Extract import statements and generate requirements.txt with LLM
import_statements = extract_import_statements(html_code)
requirements_comment = ""
if import_statements:
requirements_content = generate_requirements_txt_with_llm(import_statements)
requirements_comment = (
"# Generated requirements.txt content (create this file manually if needed):\n"
+ '\n'.join(f"# {line}" for line in requirements_content.strip().split('\n')) + '\n\n'
)
return (
f'{requirements_comment}'
'import gradio as gr\n\n'
'def show_html():\n'
f' return """{safe_html}"""\n\n'
'demo = gr.Interface(fn=show_html, inputs=None, outputs=gr.HTML())\n\n'
'if __name__ == "__main__":\n'
' demo.launch()\n'
)
def deploy_to_spaces(code):
if not code or not code.strip():
return # Do nothing if code is empty
# Wrap the HTML code in a Gradio app
app_py = wrap_html_in_gradio_app(code.strip())
base_url = "https://huggingface.co/new-space"
params = urllib.parse.urlencode({
"name": "new-space",
"sdk": "gradio"
})
# Use urlencode for file params
files_params = urllib.parse.urlencode({
"files[0][path]": "app.py",
"files[0][content]": app_py
})
full_url = f"{base_url}?{params}&{files_params}"
webbrowser.open_new_tab(full_url)
def wrap_html_in_static_app(html_code):
# For static Spaces, just use the HTML code as-is
return html_code
def prettify_comfyui_json_for_html(json_content: str) -> str:
"""Convert ComfyUI JSON to prettified HTML display"""
try:
import json
# Parse and prettify the JSON
parsed_json = json.loads(json_content)
prettified_json = json.dumps(parsed_json, indent=2, ensure_ascii=False)
# Create HTML wrapper with syntax highlighting
html_content = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>ComfyUI Workflow</title>
<style>
body {{
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
background-color: #1e1e1e;
color: #d4d4d4;
margin: 0;
padding: 20px;
line-height: 1.4;
}}
.header {{
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 20px;
border-radius: 10px;
margin-bottom: 20px;
text-align: center;
}}
.header h1 {{
margin: 0;
font-size: 2em;
}}
.header a {{
color: #ffffff;
text-decoration: none;
font-weight: bold;
opacity: 0.9;
}}
.header a:hover {{
opacity: 1;
text-decoration: underline;
}}
.json-container {{
background-color: #2d2d30;
border-radius: 8px;
padding: 20px;
overflow-x: auto;
border: 1px solid #3e3e42;
}}
pre {{
margin: 0;
white-space: pre-wrap;
word-wrap: break-word;
}}
.json-key {{
color: #9cdcfe;
}}
.json-string {{
color: #ce9178;
}}
.json-number {{
color: #b5cea8;
}}
.json-boolean {{
color: #569cd6;
}}
.json-null {{
color: #569cd6;
}}
.copy-btn {{
background: #007acc;
color: white;
border: none;
padding: 10px 20px;
border-radius: 5px;
cursor: pointer;
margin-bottom: 10px;
font-family: inherit;
}}
.copy-btn:hover {{
background: #005a9e;
}}
.download-btn {{
background: #28a745;
color: white;
border: none;
padding: 10px 20px;
border-radius: 5px;
cursor: pointer;
margin-bottom: 10px;
margin-left: 10px;
font-family: inherit;
}}
.download-btn:hover {{
background: #218838;
}}
</style>
</head>
<body>
<div class="header">
<h1>ComfyUI Workflow</h1>
<p>Built with <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank">anycoder</a></p>
</div>
<button class="copy-btn" onclick="copyToClipboard()">📋 Copy JSON</button>
<button class="download-btn" onclick="downloadJSON()">💾 Download JSON</button>
<div class="json-container">
<pre id="json-content">{prettified_json}</pre>
</div>
<script>
function copyToClipboard() {{
const jsonContent = document.getElementById('json-content').textContent;
navigator.clipboard.writeText(jsonContent).then(() => {{
const btn = document.querySelector('.copy-btn');
const originalText = btn.textContent;
btn.textContent = '✅ Copied!';
setTimeout(() => {{
btn.textContent = originalText;
}}, 2000);
}});
}}
function downloadJSON() {{
const jsonContent = document.getElementById('json-content').textContent;
const blob = new Blob([jsonContent], {{ type: 'application/json' }});
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = 'comfyui_workflow.json';
document.body.appendChild(a);
a.click();
document.body.removeChild(a);
URL.revokeObjectURL(url);
}}
// Add syntax highlighting
function highlightJSON() {{
const content = document.getElementById('json-content');
let html = content.innerHTML;
// Highlight different JSON elements
html = html.replace(/"([^"]+)":/g, '<span class="json-key">"$1":</span>');
html = html.replace(/: "([^"]*)"/g, ': <span class="json-string">"$1"</span>');
html = html.replace(/: (-?\d+\.?\d*)/g, ': <span class="json-number">$1</span>');
html = html.replace(/: (true|false)/g, ': <span class="json-boolean">$1</span>');
html = html.replace(/: null/g, ': <span class="json-null">null</span>');
content.innerHTML = html;
}}
// Apply syntax highlighting after page load
window.addEventListener('load', highlightJSON);
</script>
</body>
</html>"""
return html_content
except json.JSONDecodeError:
# If it's not valid JSON, return as-is
return json_content
except Exception as e:
print(f"Error prettifying ComfyUI JSON: {e}")
return json_content
def check_hf_space_url(url: str) -> Tuple[bool, Optional[str], Optional[str]]:
"""Check if URL is a valid Hugging Face Spaces URL and extract username/project"""
import re
# Pattern to match HF Spaces URLs (allows dots in space names)
url_pattern = re.compile(
r'^(https?://)?(huggingface\.co|hf\.co)/spaces/([\w.-]+)/([\w.-]+)$',
re.IGNORECASE
)
match = url_pattern.match(url.strip())
if match:
username = match.group(3)
project_name = match.group(4)
return True, username, project_name
return False, None, None
def detect_transformers_js_space(api, username: str, project_name: str) -> bool:
"""Check if a space is a transformers.js app by looking for the three key files"""
try:
from huggingface_hub import list_repo_files
files = list_repo_files(repo_id=f"{username}/{project_name}", repo_type="space")
# Check for the three transformers.js files
has_index_html = any('index.html' in f for f in files)
has_index_js = any('index.js' in f for f in files)
has_style_css = any('style.css' in f for f in files)
return has_index_html and has_index_js and has_style_css
except:
return False
def fetch_transformers_js_files(api, username: str, project_name: str) -> dict:
"""Fetch all three transformers.js files from a space"""
files = {}
file_names = ['index.html', 'index.js', 'style.css']
for file_name in file_names:
try:
content_path = api.hf_hub_download(
repo_id=f"{username}/{project_name}",
filename=file_name,
repo_type="space"
)
with open(content_path, 'r', encoding='utf-8') as f:
files[file_name] = f.read()
except:
files[file_name] = ""
return files
def combine_transformers_js_files(files: dict, username: str, project_name: str) -> str:
"""Combine transformers.js files into the expected format for the LLM"""
combined = f"""IMPORTED PROJECT FROM HUGGING FACE SPACE
==============================================
Space: {username}/{project_name}
SDK: static (transformers.js)
Type: Transformers.js Application
"""
if files.get('index.html'):
combined += f"=== index.html ===\n{files['index.html']}\n\n"
if files.get('index.js'):
combined += f"=== index.js ===\n{files['index.js']}\n\n"
if files.get('style.css'):
combined += f"=== style.css ===\n{files['style.css']}\n\n"
return combined
def fetch_all_space_files(api, username: str, project_name: str, sdk: str) -> dict:
"""Fetch all relevant files from a Hugging Face Space"""
files = {}
try:
from huggingface_hub import list_repo_files
all_files = list_repo_files(repo_id=f"{username}/{project_name}", repo_type="space")
# Filter out unwanted files
relevant_files = []
for file in all_files:
# Skip hidden files, git files, and certain extensions
if (file.startswith('.') or
file.endswith('.md') or
(file.endswith('.txt') and file not in ['requirements.txt', 'packages.txt']) or
file.endswith('.log') or
file.endswith('.pyc') or
'__pycache__' in file):
continue
relevant_files.append(file)
# Define priority files based on SDK
priority_files = []
if sdk == "gradio":
priority_files = ["app.py", "main.py", "gradio_app.py", "requirements.txt", "packages.txt"]
elif sdk == "streamlit":
priority_files = ["streamlit_app.py", "app.py", "main.py", "requirements.txt", "packages.txt"]
elif sdk == "static":
priority_files = ["index.html", "index.js", "style.css", "script.js"]
# Add priority files first, then other Python files, then other files
files_to_fetch = []
# Add priority files that exist
for pfile in priority_files:
if pfile in relevant_files:
files_to_fetch.append(pfile)
relevant_files.remove(pfile)
# Add other Python files
python_files = [f for f in relevant_files if f.endswith('.py')]
files_to_fetch.extend(python_files)
for pf in python_files:
if pf in relevant_files:
relevant_files.remove(pf)
# Add other important files (JS, CSS, JSON, etc.)
other_important = [f for f in relevant_files if any(f.endswith(ext) for ext in ['.js', '.css', '.json', '.html', '.yml', '.yaml'])]
files_to_fetch.extend(other_important)
# Limit to reasonable number of files to avoid overwhelming
files_to_fetch = files_to_fetch[:20] # Max 20 files
# Download each file
for file_name in files_to_fetch:
try:
content_path = api.hf_hub_download(
repo_id=f"{username}/{project_name}",
filename=file_name,
repo_type="space"
)
# Read file content with appropriate encoding
try:
with open(content_path, 'r', encoding='utf-8') as f:
files[file_name] = f.read()
except UnicodeDecodeError:
# For binary files or files with different encoding
with open(content_path, 'rb') as f:
content = f.read()
# Skip binary files that are too large or not text
if len(content) > 100000: # Skip files > 100KB
files[file_name] = f"[Binary file: {file_name} - {len(content)} bytes]"
else:
try:
files[file_name] = content.decode('utf-8')
except:
files[file_name] = f"[Binary file: {file_name} - {len(content)} bytes]"
except Exception as e:
files[file_name] = f"[Error loading {file_name}: {str(e)}]"
except Exception as e:
# Fallback to single file loading
return {}
return files
def format_multi_file_space(files: dict, username: str, project_name: str, sdk: str) -> str:
"""Format multiple files from a space into a readable format"""
if not files:
return ""
header = f"""IMPORTED PROJECT FROM HUGGING FACE SPACE
==============================================
Space: {username}/{project_name}
SDK: {sdk}
Files: {len(files)} files loaded
"""
# Sort files to show main files first
main_files = []
other_files = []
priority_order = ["app.py", "main.py", "streamlit_app.py", "gradio_app.py", "index.html", "requirements.txt"]
for priority_file in priority_order:
if priority_file in files:
main_files.append(priority_file)
for file_name in sorted(files.keys()):
if file_name not in main_files:
other_files.append(file_name)
content = header
# Add main files first
for file_name in main_files:
content += f"=== {file_name} ===\n{files[file_name]}\n\n"
# Add other files
for file_name in other_files:
content += f"=== {file_name} ===\n{files[file_name]}\n\n"
return content
def fetch_hf_space_content(username: str, project_name: str) -> str:
"""Fetch content from a Hugging Face Space"""
try:
import requests
from huggingface_hub import HfApi
# Try to get space info first
api = HfApi()
space_info = api.space_info(f"{username}/{project_name}")
# Check if this is a transformers.js space first
if space_info.sdk == "static" and detect_transformers_js_space(api, username, project_name):
files = fetch_transformers_js_files(api, username, project_name)
return combine_transformers_js_files(files, username, project_name)
# Use the new multi-file loading approach for all space types
sdk = space_info.sdk
files = fetch_all_space_files(api, username, project_name, sdk)
if files:
# Use the multi-file format
return format_multi_file_space(files, username, project_name, sdk)
else:
# Fallback to single file loading for compatibility
main_file = None
# Define file patterns to try based on SDK
if sdk == "static":
file_patterns = ["index.html"]
elif sdk == "gradio":
file_patterns = ["app.py", "main.py", "gradio_app.py"]
elif sdk == "streamlit":
file_patterns = ["streamlit_app.py", "src/streamlit_app.py", "app.py", "src/app.py", "main.py", "src/main.py", "Home.py", "src/Home.py", "🏠_Home.py", "src/🏠_Home.py", "1_🏠_Home.py", "src/1_🏠_Home.py"]
else:
# Try common files for unknown SDKs
file_patterns = ["app.py", "src/app.py", "index.html", "streamlit_app.py", "src/streamlit_app.py", "main.py", "src/main.py", "Home.py", "src/Home.py"]
# Try to find and download the main file
for file in file_patterns:
try:
content = api.hf_hub_download(
repo_id=f"{username}/{project_name}",
filename=file,
repo_type="space"
)
main_file = file
break
except:
continue
if main_file:
content = api.hf_hub_download(
repo_id=f"{username}/{project_name}",
filename=main_file,
repo_type="space"
)
# Read the file content
with open(content, 'r', encoding='utf-8') as f:
file_content = f.read()
return f"""IMPORTED PROJECT FROM HUGGING FACE SPACE
==============================================
Space: {username}/{project_name}
SDK: {sdk}
Main File: {main_file}
{file_content}"""
else:
# Try to get more information about available files for debugging
try:
from huggingface_hub import list_repo_files
files_list = list_repo_files(repo_id=f"{username}/{project_name}", repo_type="space")
available_files = [f for f in files_list if not f.startswith('.') and not f.endswith('.md')]
return f"Error: Could not find main file in space {username}/{project_name}.\n\nSDK: {sdk}\nAvailable files: {', '.join(available_files[:10])}{'...' if len(available_files) > 10 else ''}\n\nTried looking for: {', '.join(file_patterns)}"
except:
return f"Error: Could not find main file in space {username}/{project_name}. Expected files for {sdk} SDK: {', '.join(file_patterns) if 'file_patterns' in locals() else 'standard files'}"
except Exception as e:
return f"Error fetching space content: {str(e)}"
def load_project_from_url(url: str) -> Tuple[str, str]:
"""Load project from Hugging Face Space URL"""
# Validate URL
is_valid, username, project_name = check_hf_space_url(url)
if not is_valid:
return "Error: Please enter a valid Hugging Face Spaces URL.\n\nExpected format: https://huggingface.co/spaces/username/project", ""
# Fetch content
content = fetch_hf_space_content(username, project_name)
if content.startswith("Error:"):
return content, ""
# Extract the actual code content by removing metadata
lines = content.split('\n')
code_start = 0
for i, line in enumerate(lines):
# Skip metadata lines and find the start of actual code
if (line.strip() and
not line.startswith('=') and
not line.startswith('IMPORTED PROJECT') and
not line.startswith('Space:') and
not line.startswith('SDK:') and
not line.startswith('Main File:')):
code_start = i
break
code_content = '\n'.join(lines[code_start:])
return f"✅ Successfully imported project from {username}/{project_name}", code_content
# -------- Repo/Model Import (GitHub & Hugging Face model) --------
def _parse_repo_or_model_url(url: str) -> Tuple[str, Optional[dict]]:
"""Parse a URL and detect if it's a GitHub repo, HF Space, or HF Model.
Returns a tuple of (kind, meta) where kind in {"github", "hf_space", "hf_model", "unknown"}
Meta contains parsed identifiers.
"""
try:
parsed = urlparse(url.strip())
netloc = (parsed.netloc or "").lower()
path = (parsed.path or "").strip("/")
# Hugging Face spaces
if ("huggingface.co" in netloc or netloc.endswith("hf.co")) and path.startswith("spaces/"):
parts = path.split("/")
if len(parts) >= 3:
return "hf_space", {"username": parts[1], "project": parts[2]}
# Hugging Face model repo (default)
if ("huggingface.co" in netloc or netloc.endswith("hf.co")) and not path.startswith(("spaces/", "datasets/", "organizations/")):
parts = path.split("/")
if len(parts) >= 2:
repo_id = f"{parts[0]}/{parts[1]}"
return "hf_model", {"repo_id": repo_id}
# GitHub repo
if "github.com" in netloc:
parts = path.split("/")
if len(parts) >= 2:
return "github", {"owner": parts[0], "repo": parts[1]}
except Exception:
pass
return "unknown", None
def _fetch_hf_model_readme(repo_id: str) -> Optional[str]:
"""Fetch README.md (model card) for a Hugging Face model repo."""
try:
api = HfApi()
# Try direct README.md first
try:
local_path = api.hf_hub_download(repo_id=repo_id, filename="README.md", repo_type="model")
with open(local_path, "r", encoding="utf-8") as f:
return f.read()
except Exception:
# Some repos use README at root without explicit type
local_path = api.hf_hub_download(repo_id=repo_id, filename="README.md")
with open(local_path, "r", encoding="utf-8") as f:
return f.read()
except Exception:
return None
def _fetch_github_readme(owner: str, repo: str) -> Optional[str]:
"""Fetch README.md from a GitHub repo via raw URLs, trying HEAD/main/master."""
bases = [
f"https://raw.githubusercontent.com/{owner}/{repo}/HEAD/README.md",
f"https://raw.githubusercontent.com/{owner}/{repo}/main/README.md",
f"https://raw.githubusercontent.com/{owner}/{repo}/master/README.md",
]
for url in bases:
try:
resp = requests.get(url, timeout=10)
if resp.status_code == 200 and resp.text:
return resp.text
except Exception:
continue
return None
def _extract_transformers_or_diffusers_snippet(markdown_text: str) -> Tuple[Optional[str], Optional[str]]:
"""Extract the most relevant Python code block referencing transformers/diffusers from markdown.
Returns (language, code). If not found, returns (None, None).
"""
if not markdown_text:
return None, None
# Find fenced code blocks
code_blocks = []
import re as _re
for match in _re.finditer(r"```([\w+-]+)?\s*\n([\s\S]*?)```", markdown_text, _re.IGNORECASE):
lang = (match.group(1) or "").lower()
code = match.group(2) or ""
code_blocks.append((lang, code.strip()))
# Filter for transformers/diffusers relevance
def score_block(code: str) -> int:
score = 0
kws = [
"from transformers", "import transformers", "pipeline(",
"AutoModel", "AutoTokenizer", "text-generation",
"from diffusers", "import diffusers", "DiffusionPipeline",
"StableDiffusion", "UNet", "EulerDiscreteScheduler"
]
for kw in kws:
if kw in code:
score += 1
# Prefer longer, self-contained snippets
score += min(len(code) // 200, 5)
return score
scored = sorted(
[cb for cb in code_blocks if any(kw in cb[1] for kw in ["transformers", "diffusers", "pipeline(", "StableDiffusion"])],
key=lambda x: score_block(x[1]),
reverse=True,
)
if scored:
return scored[0][0] or None, scored[0][1]
return None, None
def _infer_task_from_context(snippet: Optional[str], pipeline_tag: Optional[str]) -> str:
"""Infer a task string for transformers pipeline; fall back to provided pipeline_tag or 'text-generation'."""
if pipeline_tag:
return pipeline_tag
if not snippet:
return "text-generation"
lowered = snippet.lower()
task_hints = {
"text-generation": ["text-generation", "automodelforcausallm"],
"text2text-generation": ["text2text-generation", "t5forconditionalgeneration"],
"fill-mask": ["fill-mask", "automodelformaskedlm"],
"summarization": ["summarization"],
"translation": ["translation"],
"text-classification": ["text-classification", "sequenceclassification"],
"automatic-speech-recognition": ["speechrecognition", "automatic-speech-recognition", "asr"],
"image-classification": ["image-classification"],
"zero-shot-image-classification": ["zero-shot-image-classification"],
}
for task, hints in task_hints.items():
if any(h in lowered for h in hints):
return task
# Inspect explicit pipeline("task")
import re as _re
m = _re.search(r"pipeline\(\s*['\"]([\w\-]+)['\"]", snippet)
if m:
return m.group(1)
return "text-generation"
def _generate_gradio_app_from_transformers(repo_id: str, task: str) -> str:
"""Build a minimal Gradio app using transformers.pipeline for a given model and task."""
# Map simple UI per task; default to text in/out
if task in {"text-generation", "text2text-generation", "summarization", "translation", "fill-mask"}:
return (
"import gradio as gr\n"
"from transformers import pipeline\n\n"
f"pipe = pipeline(task='{task}', model='{repo_id}')\n\n"
"def infer(prompt, max_new_tokens=256, temperature=0.7, top_p=0.95):\n"
" if '\u2047' in prompt:\n"
" # Fill-mask often uses [MASK]; keep generic handling\n"
" pass\n"
" out = pipe(prompt, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, top_p=top_p)\n"
" if isinstance(out, list):\n"
" if isinstance(out[0], dict):\n"
" return next(iter(out[0].values())) if out[0] else str(out)\n"
" return str(out[0])\n"
" return str(out)\n\n"
"demo = gr.Interface(\n"
" fn=infer,\n"
" inputs=[gr.Textbox(label='Input', lines=8), gr.Slider(1, 2048, value=256, label='max_new_tokens'), gr.Slider(0.0, 1.5, value=0.7, step=0.01, label='temperature'), gr.Slider(0.0, 1.0, value=0.95, step=0.01, label='top_p')],\n"
" outputs=gr.Textbox(label='Output', lines=8),\n"
" title='Transformers Demo'\n"
")\n\n"
"if __name__ == '__main__':\n"
" demo.launch()\n"
)
elif task in {"text-classification"}:
return (
"import gradio as gr\n"
"from transformers import pipeline\n\n"
f"pipe = pipeline(task='{task}', model='{repo_id}')\n\n"
"def infer(text):\n"
" out = pipe(text)\n"
" # Expect list of dicts with label/score\n"
" return {o['label']: float(o['score']) for o in out}\n\n"
"demo = gr.Interface(fn=infer, inputs=gr.Textbox(lines=6), outputs=gr.Label(), title='Text Classification')\n\n"
"if __name__ == '__main__':\n"
" demo.launch()\n"
)
else:
# Fallback generic text pipeline (pipeline infers task from model config)
return (
"import gradio as gr\n"
"from transformers import pipeline\n\n"
f"pipe = pipeline(model='{repo_id}')\n\n"
"def infer(prompt):\n"
" out = pipe(prompt)\n"
" if isinstance(out, list):\n"
" if isinstance(out[0], dict):\n"
" return next(iter(out[0].values())) if out[0] else str(out)\n"
" return str(out[0])\n"
" return str(out)\n\n"
"demo = gr.Interface(fn=infer, inputs=gr.Textbox(lines=8), outputs=gr.Textbox(lines=8), title='Transformers Demo')\n\n"
"if __name__ == '__main__':\n"
" demo.launch()\n"
)
def _generate_gradio_app_from_diffusers(repo_id: str) -> str:
"""Build a minimal Gradio app for text-to-image using diffusers."""
return (
"import gradio as gr\n"
"import torch\n"
"from diffusers import DiffusionPipeline\n\n"
f"pipe = DiffusionPipeline.from_pretrained('{repo_id}')\n"
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n"
"pipe = pipe.to(device)\n\n"
"def infer(prompt, guidance_scale=7.0, num_inference_steps=30, seed=0):\n"
" generator = None if seed == 0 else torch.Generator(device=device).manual_seed(int(seed))\n"
" image = pipe(prompt, guidance_scale=float(guidance_scale), num_inference_steps=int(num_inference_steps), generator=generator).images[0]\n"
" return image\n\n"
"demo = gr.Interface(\n"
" fn=infer,\n"
" inputs=[gr.Textbox(label='Prompt'), gr.Slider(0.0, 15.0, value=7.0, step=0.1, label='guidance_scale'), gr.Slider(1, 100, value=30, step=1, label='num_inference_steps'), gr.Slider(0, 2**32-1, value=0, step=1, label='seed')],\n"
" outputs=gr.Image(type='pil'),\n"
" title='Diffusers Text-to-Image'\n"
")\n\n"
"if __name__ == '__main__':\n"
" demo.launch()\n"
)
def get_trending_models(limit: int = 10) -> List[Tuple[str, str]]:
"""
Fetch top trending models from HuggingFace Hub.
Returns a list of tuples: (display_name, model_id)
Display name format: "model_name (task)"
"""
try:
# Use the HuggingFace trending API endpoint directly
response = requests.get("https://huggingface.co/api/trending")
if response.status_code != 200:
print(f"Failed to fetch trending models: HTTP {response.status_code}")
return [("Unable to load trending models", "")]
trending_data = response.json()
# The API returns {"recentlyTrending": [...]}
recently_trending = trending_data.get("recentlyTrending", [])
if not recently_trending:
print("No trending items found in API response")
return [("No trending models available", "")]
trending_list = []
count = 0
# Process trending items, filter for models only
for item in recently_trending:
if count >= limit:
break
try:
# Check if this is a model (not a space or dataset)
repo_type = item.get("repoType")
if repo_type != "model":
continue
# Extract model data
repo_data = item.get("repoData", {})
model_id = repo_data.get("id")
if not model_id:
continue
# Get pipeline tag (task type)
pipeline_tag = repo_data.get("pipeline_tag")
# Default to "general" if no task found
task = pipeline_tag or "general"
# Clean up task name for display
task_display = task.replace("-", " ").title() if task != "general" else "General"
# Create display name: "model_name (Task)"
display_name = f"{model_id} ({task_display})"
trending_list.append((display_name, model_id))
count += 1
except Exception as model_error:
print(f"Error processing trending item: {model_error}")
continue
if not trending_list:
print("No models found in trending list, using fallback")
# Fallback: use list_models with downloads sort
try:
api = HfApi()
models = api.list_models(sort="downloads", limit=limit)
for model in models:
model_id = model.id
task = getattr(model, "pipeline_tag", None) or "general"
task_display = task.replace("-", " ").title() if task != "general" else "General"
display_name = f"{model_id} ({task_display})"
trending_list.append((display_name, model_id))
except Exception as fallback_error:
print(f"Fallback also failed: {fallback_error}")
return [("No models available", "")]
return trending_list
except Exception as e:
print(f"Error fetching trending models: {e}")
# Fallback to most downloaded models
try:
api = HfApi()
models = api.list_models(sort="downloads", limit=limit)
trending_list = []
for model in models:
model_id = model.id
task = getattr(model, "pipeline_tag", None) or "general"
task_display = task.replace("-", " ").title() if task != "general" else "General"
display_name = f"{model_id} ({task_display})"
trending_list.append((display_name, model_id))
return trending_list
except Exception:
return [("Error loading models", "")]
def get_trending_spaces(limit: int = 10) -> List[Tuple[str, str]]:
"""
Fetch top trending spaces from HuggingFace Hub.
Returns a list of tuples: (display_name, space_id)
Display name format: "space_name (category)"
"""
try:
# Use the HuggingFace trending API endpoint for spaces
response = requests.get("https://huggingface.co/api/trending?type=space")
if response.status_code != 200:
print(f"Failed to fetch trending spaces: HTTP {response.status_code}")
return [("Unable to load trending spaces", "")]
trending_data = response.json()
# The API returns {"recentlyTrending": [...]}
recently_trending = trending_data.get("recentlyTrending", [])
if not recently_trending:
print("No trending spaces found in API response")
return [("No trending spaces available", "")]
trending_list = []
count = 0
# Process trending items
for item in recently_trending:
if count >= limit:
break
try:
# Check if this is a space
repo_type = item.get("repoType")
if repo_type != "space":
continue
# Extract space data
repo_data = item.get("repoData", {})
space_id = repo_data.get("id")
if not space_id:
continue
# Get title and category
title = repo_data.get("title") or space_id
category = repo_data.get("ai_category") or repo_data.get("shortDescription", "Space")
# Create display name: "title (category)"
# Truncate long titles
if len(title) > 40:
title = title[:37] + "..."
display_name = f"{title} ({category})"
trending_list.append((display_name, space_id))
count += 1
except Exception as space_error:
print(f"Error processing trending space: {space_error}")
continue
if not trending_list:
return [("No spaces available", "")]
return trending_list
except Exception as e:
print(f"Error fetching trending spaces: {e}")
return [("Error loading spaces", "")]
def import_space_from_hf(space_id: str) -> Tuple[str, str, str, str]:
"""
Import a HuggingFace space by ID and extract its code.
Returns: (status, code, language, space_url)
"""
if not space_id or space_id == "":
return "Please select a space.", "", "html", ""
# Build space URL
space_url = f"https://huggingface.co/spaces/{space_id}"
# Use existing load_project_from_url function
status, code = load_project_from_url(space_url)
# Determine language based on code content
code_lang = "html" # default
language = "html" # for language dropdown
# Check imports to determine framework for Python code
if is_streamlit_code(code):
code_lang = "python"
language = "streamlit"
elif is_gradio_code(code):
code_lang = "python"
language = "gradio"
elif "=== index.html ===" in code and "=== index.js ===" in code:
code_lang = "html"
language = "transformers.js"
elif ("import " in code or "def " in code) and not ("<!DOCTYPE html>" in code or "<html" in code):
code_lang = "python"
language = "gradio" # Default to Gradio for Python spaces
return status, code, language, space_url
def _generate_inference_code_template(model_id: str, pipeline_tag: Optional[str], has_inference_providers: bool) -> Optional[str]:
"""
Generate inference provider code template based on model's pipeline tag.
Args:
model_id: The HuggingFace model ID
pipeline_tag: The model's pipeline tag (e.g., "text-generation", "text-to-image")
has_inference_providers: Whether the model has inference providers available
Returns:
Generated code snippet or None
"""
if not has_inference_providers:
return None
# Map pipeline tags to code templates based on HuggingFace Inference Providers docs
# https://huggingface.co/docs/inference-providers
# Chat Completion / Text Generation models
if pipeline_tag in ["text-generation", "conversational"]:
return f'''import os
from huggingface_hub import InferenceClient
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
completion = client.chat.completions.create(
model="{model_id}",
messages=[
{{
"role": "user",
"content": "What is the capital of France?"
}}
],
)
print(completion.choices[0].message)'''
# Vision-Language Models (Image-Text to Text)
elif pipeline_tag in ["image-text-to-text", "visual-question-answering"]:
return f'''import os
from huggingface_hub import InferenceClient
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
completion = client.chat.completions.create(
model="{model_id}",
messages=[
{{
"role": "user",
"content": [
{{
"type": "text",
"text": "Describe this image in one sentence."
}},
{{
"type": "image_url",
"image_url": {{
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}}
}}
]
}}
],
)
print(completion.choices[0].message)'''
# Text to Image models
elif pipeline_tag == "text-to-image":
return f'''import os
from huggingface_hub import InferenceClient
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
# output is a PIL.Image object
image = client.text_to_image(
"Astronaut riding a horse",
model="{model_id}",
)
# Save the image
image.save("output.png")'''
# Text to Video models
elif pipeline_tag == "text-to-video":
return f'''import os
from huggingface_hub import InferenceClient
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
video = client.text_to_video(
"A young man walking on the street",
model="{model_id}",
)
# Save the video
with open("output.mp4", "wb") as f:
f.write(video)'''
# Image to Image models
elif pipeline_tag == "image-to-image":
return f'''import os
from huggingface_hub import InferenceClient
from PIL import Image
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
# Load input image
input_image = Image.open("input.jpg")
# output is a PIL.Image object
output_image = client.image_to_image(
input_image,
model="{model_id}",
prompt="Make it more vibrant"
)
# Save the output
output_image.save("output.png")'''
# Text to Speech models
elif pipeline_tag == "text-to-speech":
return f'''import os
from huggingface_hub import InferenceClient
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
audio = client.text_to_speech(
"Hello world",
model="{model_id}",
)
# Save the audio
with open("output.mp3", "wb") as f:
f.write(audio)'''
# Automatic Speech Recognition
elif pipeline_tag == "automatic-speech-recognition":
return f'''import os
from huggingface_hub import InferenceClient
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
with open("audio.mp3", "rb") as f:
audio_data = f.read()
result = client.automatic_speech_recognition(
audio_data,
model="{model_id}",
)
print(result)'''
# Feature Extraction / Embeddings
elif pipeline_tag == "feature-extraction":
return f'''import os
from huggingface_hub import InferenceClient
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
embeddings = client.feature_extraction(
"Hello world",
model="{model_id}",
)
print(embeddings)'''
# Default: try chat completion for conversational models
else:
# If it has inference providers but unknown task, try chat completion
return f'''import os
from huggingface_hub import InferenceClient
client = InferenceClient(
api_key=os.environ["HF_TOKEN"],
)
completion = client.chat.completions.create(
model="{model_id}",
messages=[
{{
"role": "user",
"content": "Hello, how are you?"
}}
],
)
print(completion.choices[0].message)'''
def _fetch_inference_provider_code(model_id: str) -> Optional[str]:
"""
Fetch inference provider information from HuggingFace API and generate code template.
Args:
model_id: The HuggingFace model ID (e.g., "moonshotai/Kimi-K2-Thinking")
Returns:
The code snippet if model has inference providers, None otherwise
"""
# Skip non-HuggingFace models (external APIs)
non_hf_models = [
"gemini-3.0-pro", "gemini-2.5-flash", "gemini-2.5-pro",
"gemini-flash-latest", "gemini-flash-lite-latest",
"gpt-5", "gpt-5.1", "gpt-5.1-instant", "gpt-5.1-codex", "gpt-5.1-codex-mini",
"grok-4", "Grok-Code-Fast-1",
"claude-opus-4.1", "claude-sonnet-4.5", "claude-haiku-4.5",
"qwen3-30b-a3b-instruct-2507", "qwen3-30b-a3b-thinking-2507",
"qwen3-coder-30b-a3b-instruct", "qwen3-max-preview",
"kimi-k2-turbo-preview", "step-3",
"codestral-2508", "mistral-medium-2508",
"stealth-model-1",
"openrouter/sonoma-dusk-alpha", "openrouter/sonoma-sky-alpha",
"openrouter/sherlock-dash-alpha", "openrouter/sherlock-think-alpha"
]
if model_id in non_hf_models:
return None
try:
# Fetch trending models data from HuggingFace API
response = requests.get("https://huggingface.co/api/trending", timeout=10)
if response.status_code != 200:
print(f"Failed to fetch trending models API: HTTP {response.status_code}")
return None
trending_data = response.json()
recently_trending = trending_data.get("recentlyTrending", [])
# Find the specific model in trending data
model_info = None
for item in recently_trending:
repo_data = item.get("repoData", {})
if repo_data.get("id") == model_id:
model_info = repo_data
break
# If not found in trending, try to get model info directly from API
if not model_info:
try:
api = HfApi()
info = api.model_info(model_id)
pipeline_tag = getattr(info, "pipeline_tag", None)
# Check if model has inference providers via model info
# Note: The direct API might not have availableInferenceProviders
# In this case, we'll generate a generic template
has_inference = pipeline_tag is not None
if has_inference:
return _generate_inference_code_template(model_id, pipeline_tag, True)
except Exception as e:
print(f"Could not fetch model info for {model_id}: {e}")
return None
else:
# Extract pipeline tag and inference providers info
pipeline_tag = model_info.get("pipeline_tag")
inference_providers = model_info.get("availableInferenceProviders", [])
has_inference_providers = len(inference_providers) > 0
# Generate code template based on pipeline tag
return _generate_inference_code_template(model_id, pipeline_tag, has_inference_providers)
return None
except Exception as e:
print(f"Error fetching inference provider code: {e}")
return None
# Global storage for code alternatives (used when both inference and local code are available)
_model_code_alternatives = {}
def store_model_code_alternatives(model_id: str, inference_code: Optional[str], local_code: Optional[str]):
"""Store both code alternatives for a model for later retrieval."""
global _model_code_alternatives
_model_code_alternatives[model_id] = {
'inference': inference_code,
'local': local_code
}
def get_model_code_alternatives(model_id: str) -> Dict[str, Optional[str]]:
"""Retrieve stored code alternatives for a model."""
global _model_code_alternatives
return _model_code_alternatives.get(model_id, {'inference': None, 'local': None})
def import_model_from_hf(model_id: str, prefer_local: bool = False) -> Tuple[str, str, str, str]:
"""
Import a HuggingFace model by ID and extract code snippet.
Tries to fetch both inference provider code and transformers/diffusers code from README.
Args:
model_id: The HuggingFace model ID
prefer_local: If True and both options available, return local code instead of inference code
Returns: (status, code, language, model_url)
"""
if not model_id or model_id == "":
return "Please select a model.", "", "python", ""
# Skip non-HuggingFace models (external APIs) - these are not importable
non_hf_models = [
"gemini-3.0-pro", "gemini-2.5-flash", "gemini-2.5-pro",
"gemini-flash-latest", "gemini-flash-lite-latest",
"gpt-5", "gpt-5.1", "gpt-5.1-instant", "gpt-5.1-codex", "gpt-5.1-codex-mini",
"grok-4", "Grok-Code-Fast-1",
"claude-opus-4.1", "claude-sonnet-4.5", "claude-haiku-4.5",
"qwen3-30b-a3b-instruct-2507", "qwen3-30b-a3b-thinking-2507",
"qwen3-coder-30b-a3b-instruct", "qwen3-max-preview",
"kimi-k2-turbo-preview", "step-3",
"codestral-2508", "mistral-medium-2508",
"stealth-model-1",
"openrouter/sonoma-dusk-alpha", "openrouter/sonoma-sky-alpha",
"openrouter/sherlock-dash-alpha", "openrouter/sherlock-think-alpha"
]
if model_id in non_hf_models:
return f"❌ `{model_id}` is not a HuggingFace model and cannot be imported. This model is accessed via external API.", "", "python", ""
# Build model URL
model_url = f"https://huggingface.co/{model_id}"
# Try to fetch both types of code
inference_code = _fetch_inference_provider_code(model_id)
# Also try to extract transformers/diffusers code from README
readme_status, readme_code, _ = import_repo_to_app(model_url)
has_readme_code = readme_code and ("transformers" in readme_code or "diffusers" in readme_code)
# Store both alternatives for later switching
store_model_code_alternatives(model_id, inference_code, readme_code if has_readme_code else None)
# Build status message and code based on what's available
if inference_code and has_readme_code:
# Both available - provide choice
if prefer_local:
status = f"""✅ **Found multiple code options for `{model_id}`**
**Currently showing:** Local Transformers/Diffusers Code (Option 2) 💻
**Option 1: Inference Provider Code (Serverless)** ⚡
- Uses HuggingFace Inference API (serverless, pay-per-use)
- No GPU required, instant startup
- Requires `HF_TOKEN` environment variable
**Option 2: Local Transformers/Diffusers Code (Currently Active)** 💻
- Runs locally on your hardware
- Requires GPU for optimal performance
- Full control over model parameters
---
To switch to inference provider code, click the button below or ask: "Show me the inference provider code instead"
"""
code = readme_code
else:
status = f"""✅ **Found multiple code options for `{model_id}`**
**Currently showing:** Inference Provider Code (Option 1) ⚡ *Recommended*
**Option 1: Inference Provider Code (Serverless - Currently Active)** ⚡
- Uses HuggingFace Inference API (serverless, pay-per-use)
- No GPU required, instant startup
- Requires `HF_TOKEN` environment variable
**Option 2: Local Transformers/Diffusers Code** 💻
- Runs locally on your hardware
- Requires GPU for optimal performance
- Full control over model parameters
---
To switch to local transformers/diffusers code, click the button below or ask: "Show me the local transformers code instead"
"""
code = inference_code
language = "gradio"
return status, code, language, model_url
elif inference_code:
# Only inference provider code available
status = f"✅ Imported inference provider code for `{model_id}` (serverless inference)"
language = "gradio"
return status, inference_code, language, model_url
elif has_readme_code:
# Only README code available
status = f"✅ Imported transformers/diffusers code from README for `{model_id}` (local inference)"
language = "gradio"
return status, readme_code, language, model_url
else:
# No code found
status = f"⚠️ No inference provider or transformers/diffusers code found for `{model_id}`"
return status, "", "python", model_url
def switch_model_code_type(model_id: str, current_code: str) -> Tuple[str, str]:
"""
Switch between inference provider code and local transformers/diffusers code.
Args:
model_id: The model ID
current_code: The currently displayed code
Returns: (status_message, new_code)
"""
alternatives = get_model_code_alternatives(model_id)
inference_code = alternatives['inference']
local_code = alternatives['local']
if not inference_code and not local_code:
return "⚠️ No alternative code available for this model.", current_code
# Determine which code is currently shown
is_showing_inference = current_code == inference_code
if is_showing_inference and local_code:
# Switch to local code
status = f"✅ Switched to **Local Transformers/Diffusers Code** for `{model_id}` 💻\n\nThis code runs locally on your hardware."
return status, local_code
elif not is_showing_inference and inference_code:
# Switch to inference provider code
status = f"✅ Switched to **Inference Provider Code** for `{model_id}` ⚡\n\nThis code uses serverless HuggingFace Inference API."
return status, inference_code
else:
return "⚠️ Alternative code type not available for this model.", current_code
def import_repo_to_app(url: str, framework: str = "Gradio") -> Tuple[str, str, str]:
"""Import a GitHub or HF model repo and return the raw code snippet from README/model card.
Returns (status_markdown, code_snippet, preview_html). Preview left empty; UI will decide.
"""
if not url or not url.strip():
return "Please enter a repository URL.", "", ""
kind, meta = _parse_repo_or_model_url(url)
if kind == "hf_space" and meta:
# Spaces already contain runnable apps; keep existing behavior to fetch main file raw
status, code = load_project_from_url(url)
return status, code, ""
# Fetch markdown
markdown = None
repo_id = None
pipeline_tag = None
library_name = None
if kind == "hf_model" and meta:
repo_id = meta.get("repo_id")
# Try model info to get pipeline tag/library
try:
api = HfApi()
info = api.model_info(repo_id)
pipeline_tag = getattr(info, "pipeline_tag", None)
library_name = getattr(info, "library_name", None)
except Exception:
pass
markdown = _fetch_hf_model_readme(repo_id)
elif kind == "github" and meta:
markdown = _fetch_github_readme(meta.get("owner"), meta.get("repo"))
else:
return "Error: Unsupported or invalid URL. Provide a GitHub repo or Hugging Face model URL.", "", ""
if not markdown:
return "Error: Could not fetch README/model card.", "", ""
lang, snippet = _extract_transformers_or_diffusers_snippet(markdown)
if not snippet:
return "Error: No relevant transformers/diffusers code block found in README/model card.", "", ""
status = "✅ Imported code snippet from README/model card. Use it as a starting point."
return status, snippet, ""
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