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"""
Agent functionality for interactive code generation with follow-up questions and task planning.
"""
import os
from typing import Dict, List, Optional, Tuple, Generator
import gradio as gr
from .models import (
get_inference_client, get_real_model_id, history_to_messages,
history_to_chatbot_messages, strip_thinking_tags
)
from .deploy import generation_code
def agent_generate_with_questions(
query: Optional[str],
setting: Dict[str, str],
history: List,
current_model: Dict,
language: str,
provider: str,
profile: Optional[gr.OAuthProfile] = None,
token: Optional[gr.OAuthToken] = None,
max_questions: int = 3
) -> Generator[Tuple[List, List], None, None]:
"""
Agent that asks follow-up questions, creates a task list, and generates code.
Args:
query: Initial user request
setting: System settings
history: Conversation history
current_model: Selected model configuration
language: Target programming language/framework
provider: Model provider
profile: User OAuth profile
token: User OAuth token
max_questions: Maximum number of follow-up questions to ask
Yields:
Tuples of (history, chatbot_messages) at each step
"""
if not query or not query.strip():
return
# Initialize history with user's initial query
current_history = history + [[query, ""]]
# Step 1: Agent analyzes the request and asks follow-up questions
agent_system_prompt = """You are a helpful coding assistant that helps users clarify their requirements before generating code.
Your task is to:
1. Analyze the user's request
2. Ask 1-3 clarifying questions to better understand their needs
3. Focus on important details like:
- Target audience and use case
- Specific features or functionality needed
- Design preferences (colors, layout, style)
- Data sources or APIs to integrate
- Performance or scalability requirements
Output ONLY the questions, numbered 1, 2, 3, etc. Keep questions concise and focused.
Do not generate code yet - just ask the questions."""
# Get LLM client
client = get_inference_client(current_model.get('model_id', 'Qwen/Qwen2.5-Coder-32B-Instruct'), provider)
model_id = get_real_model_id(current_model.get('model_id', 'Qwen/Qwen2.5-Coder-32B-Instruct'))
# Prepare messages for follow-up questions
messages = [
{'role': 'system', 'content': agent_system_prompt},
{'role': 'user', 'content': f"User wants to create: {query}\n\nLanguage/Framework: {language}\n\nAsk clarifying questions."}
]
# Generate follow-up questions
questions_response = ""
try:
# Try to use the client (works for both InferenceClient and OpenAI-compatible clients)
stream = client.chat.completions.create(
model=model_id,
messages=messages,
temperature=0.7,
max_tokens=500,
stream=True
)
for chunk in stream:
if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content:
questions_response += chunk.choices[0].delta.content
# Update display in real-time
temp_history = current_history[:-1] + [[query, f"🤔 **Analyzing your request...**\n\n{questions_response}"]]
yield (temp_history, history_to_chatbot_messages(temp_history))
except Exception as e:
error_msg = f"❌ Error asking follow-up questions: {str(e)}"
temp_history = current_history[:-1] + [[query, error_msg]]
yield (temp_history, history_to_chatbot_messages(temp_history))
return
# Update history with agent's questions
current_history[-1][1] = f"🤔 **Let me ask you a few questions to better understand your needs:**\n\n{questions_response}\n\n💬 Please answer these questions in your next message."
yield (current_history, history_to_chatbot_messages(current_history))
# Wait for user response (this will be handled by the UI)
# For now, we'll return and let the user respond, then continue in the next call
return
def agent_process_answers_and_generate(
user_answers: str,
original_query: str,
questions: str,
setting: Dict[str, str],
history: List,
current_model: Dict,
language: str,
provider: str,
profile: Optional[gr.OAuthProfile] = None,
token: Optional[gr.OAuthToken] = None,
code_output=None,
history_output=None,
history_state=None
) -> Generator:
"""
Process user's answers, create task list, and generate code.
Args:
user_answers: User's responses to the questions
original_query: Original user request
questions: Agent's questions
setting: System settings
history: Conversation history
current_model: Selected model configuration
language: Target programming language/framework
provider: Model provider
profile: User OAuth profile
token: User OAuth token
code_output: Code output component
history_output: History output component
history_state: History state
Yields:
Updates to code output and history
"""
# Step 2: Create task list based on answers
task_planning_prompt = f"""Based on the user's request and their answers, create a detailed task list for implementing the solution.
Original Request: {original_query}
Questions Asked:
{questions}
User's Answers:
{user_answers}
Create a numbered task list with 5-8 specific, actionable tasks. Each task should be clear and focused.
Start with "📋 **Task List:**" and then list the tasks."""
client = get_inference_client(current_model.get('model_id', 'Qwen/Qwen2.5-Coder-32B-Instruct'), provider)
model_id = get_real_model_id(current_model.get('model_id', 'Qwen/Qwen2.5-Coder-32B-Instruct'))
messages = [
{'role': 'system', 'content': 'You are a helpful coding assistant creating a task plan.'},
{'role': 'user', 'content': task_planning_prompt}
]
# Generate task list
task_list = ""
try:
stream = client.chat.completions.create(
model=model_id,
messages=messages,
temperature=0.7,
max_tokens=800,
stream=True
)
for chunk in stream:
if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content:
task_list += chunk.choices[0].delta.content
# Update display
temp_history = history + [[user_answers, f"📋 **Creating task list...**\n\n{task_list}"]]
yield {
history_state: temp_history,
history_output: history_to_chatbot_messages(temp_history)
}
except Exception as e:
error_msg = f"❌ Error creating task list: {str(e)}"
temp_history = history + [[user_answers, error_msg]]
yield {
history_state: temp_history,
history_output: history_to_chatbot_messages(temp_history)
}
return
# Update history with task list
updated_history = history + [[user_answers, task_list]]
yield {
history_state: updated_history,
history_output: history_to_chatbot_messages(updated_history)
}
# Step 3: Generate code based on refined requirements
refined_query = f"""{original_query}
Additional Requirements (based on follow-up):
{user_answers}
Task List:
{task_list}
Please implement the above requirements following the task list."""
# Add a message indicating code generation is starting
code_gen_start_history = updated_history + [["[System]", "🚀 **Starting code generation based on your requirements...**"]]
yield {
history_state: code_gen_start_history,
history_output: history_to_chatbot_messages(code_gen_start_history)
}
# Use the existing generation_code function for actual code generation
# We need to pass the refined query and updated history
for result in generation_code(
refined_query,
setting,
updated_history,
current_model,
language,
provider,
profile,
token,
code_output,
history_output,
history_state
):
yield result
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