app.py

import fitz  # PyMuPDF
import gradio as gr
import json
from langchain_text_splitters import RecursiveCharacterTextSplitter
from groq import Groq
import os

# Load API key
API_KEY = os.getenv('Groq')
if not API_KEY:
    raise ValueError("API Key is missing! Set the environment variable 'GROQ_API_KEY'.")

# Initialize Groq Client
client = Groq(api_key=API_KEY)

# Prompt Template
PROMPT_TEMPLATE = """
You are an expert screenplay analyst. Convert the following film script text into the JSON structure below:
{json_structure}

Script Text:
{text}

Provide only the JSON response.
""".strip()

# Define the JSON structure to be extracted
JSON_STRUCTURE = {
    "scenes": [
        {
            "scene_heading": "",
            "location": "",
            "time_of_day": "",
            "characters": [],
            "emotions": [],
            "summary": "",
            "dialogues": [
                {
                    "character": "",
                    "dialogue_text": "",
                    "tone": ""
                }
            ]
        }
    ],
    "overall_emotional_arc": [],
    "story_beats": {
        "setup": "",
        "inciting_incident": "",
        "climax": "",
        "resolution": ""
    }
}

# Function to extract text from PDF
def extract_text_from_pdf(pdf_file):
    text = ""
    try:
        with open(pdf_file.name, 'rb') as f:
            doc = fitz.open(stream=f.read(), filetype="pdf")
            for page in doc:
                text += page.get_text() + "\n"
    except Exception as e:
        return f"Error reading PDF: {e}"
    return text.strip()

# Function to split text into chunks
def split_text_into_chunks(text, chunk_size=2000):
    splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=100)
    return splitter.split_text(text)

# Function to call Groq API
def call_llm_api(text):
    prompt = PROMPT_TEMPLATE.format(json_structure=json.dumps(JSON_STRUCTURE, indent=2), text=text)
    
    try:
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model="llama-3-3-70b-vision",  # You can also test llama-3-70b-versatile
        )

        raw_content = response.choices[0].message.content.strip()
        
        # Clean JSON formatting
        if raw_content.startswith("```json") and raw_content.endswith("```"):
            raw_content = raw_content[7:-3].strip()
        
        return json.loads(raw_content)
    except Exception as e:
        return {"error": f"API call failed: {e}"}

# Function to merge JSON chunks
def merge_json_chunks(chunks):
    combined_result = JSON_STRUCTURE.copy()
    combined_result["scenes"] = []
    combined_result["overall_emotional_arc"] = []
    
    for chunk in chunks:
        result = call_llm_api(chunk)
        if "error" in result:
            continue
        
        # Merge scenes
        if "scenes" in result:
            combined_result["scenes"].extend(result["scenes"])
        
        # Merge emotional arc
        if "overall_emotional_arc" in result:
            combined_result["overall_emotional_arc"].extend(result["overall_emotional_arc"])
        
        # Merge story beats only once (first time we encounter valid values)
        for beat in combined_result["story_beats"].keys():
            if result.get("story_beats", {}).get(beat) and not combined_result["story_beats"][beat]:
                combined_result["story_beats"][beat] = result["story_beats"][beat]
    
    return combined_result

# Gradio interface function
def gradio_interface(file):
    pdf_text = extract_text_from_pdf(file)
    if pdf_text.startswith("Error"):
        return {"error": pdf_text}
    
    chunks = split_text_into_chunks(pdf_text)
    extracted_data = merge_json_chunks(chunks)
    return extracted_data

# Gradio UI
iface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.File(label="Upload Film Script PDF"),
    outputs="json",
    title="ScriptWhisper - Screenplay Structure & Emotion Extractor",
    description="Upload a screenplay PDF to extract scene structure, emotional arc, and story beats."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()