app.py

import gradio as gr
import requests
import re
from duckduckgo_search import DDGS
import anthropic
import os
import json

# Initialize clients
anthropic_client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

# === 1. Simplified Search Workflow ===

def search_workflow(name: str, progress=gr.Progress()):
    """
    A simple function to search for articles, fetching exactly 8 news articles: 4 recent, 4 historical.
    """
    if not name or not name.strip():
        return "❌ Please enter a company name.", ""

    progress(0, desc="Starting search...")
    
    # Define search queries
    recent_keywords = f'"{name}" founder news'
    historical_keywords = f'"{name}" founder history origin'
    
    all_articles_markdown = []
    raw_text_for_ai = ""

    try:
        with DDGS(timeout=20) as ddgs:
            # --- Fetch 4 Recent Articles (past year) ---
            progress(0.1, desc="Searching for recent articles...")
            # The 'timelimit="y"' parameter is a reliable way to get recent results.
            recent_results = ddgs.text(keywords=recent_keywords, max_results=4, timelimit='y') or []
            
            for i, res in enumerate(recent_results):
                title = res.get('title', 'No Title')
                url = res.get('href', '#')
                body = res.get('body', 'No snippet available.')
                
                # Format for display
                markdown = f"### (Recent) {title}\n**Source**: [{url}]({url})\n\n{body}\n"
                all_articles_markdown.append(markdown)
                
                # Format for AI
                raw_text_for_ai += f"Article (Recent):\nTitle: {title}\nContent: {body}\n\n"

            # --- Fetch 4 Historical Articles ---
            progress(0.5, desc="Searching for historical articles...")
            historical_results = ddgs.text(keywords=historical_keywords, max_results=4) or []

            for i, res in enumerate(historical_results):
                title = res.get('title', 'No Title')
                url = res.get('href', '#')
                body = res.get('body', 'No snippet available.')
                
                # Format for display
                markdown = f"### (Historical) {title}\n**Source**: [{url}]({url})\n\n{body}\n"
                all_articles_markdown.append(markdown)
                
                # Format for AI
                raw_text_for_ai += f"Article (Historical):\nTitle: {title}\nContent: {body}\n\n"

    except Exception as e:
        return f"❌ An error occurred during search: {e}", ""

    if not all_articles_markdown:
        return "[INFO] No articles found for that company.", ""
    
    progress(1.0, desc="Search complete!")
    
    final_markdown = f"## Found {len(all_articles_markdown)} Articles\n\n" + "\n---\n".join(all_articles_markdown)
    
    return final_markdown, raw_text_for_ai


# === 2. Simplified Extraction Workflow ===

def extraction_workflow(raw_text: str, company_name: str, progress=gr.Progress()):
    """
    A simple and robust tool to extract founders from text using the AI model.
    """
    if not raw_text or not raw_text.strip():
        return "❌ Please run a search first to get text to analyze."

    progress(0, desc="Preparing prompt for AI...")
    
    prompt = f"""From the provided article snippets about "{company_name}", extract the names of individuals explicitly identified as a founder.
Return a single, valid JSON object with the structure: {{"founders": [{{"name": "Founder's Name", "evidence": "A brief quote or context."}}]}}
If no founders are mentioned, return an empty list: {{"founders": []}}.
Do not add any text outside the JSON object.

ARTICLES:
---
{raw_text[:20000]}
---
"""
    try:
        progress(0.5, desc="Sending request to AI model...")
        message = anthropic_client.messages.create(
            model="claude-sonnet-4-20250514", # As requested
            max_tokens=1024,
            temperature=0.0,
            messages=[{"role": "user", "content": prompt}]
        )

        # This robust check prevents the 'list index out of range' error.
        if message and message.content and isinstance(message.content, list) and len(message.content) > 0:
            text_block = message.content[0]
            if hasattr(text_block, 'text'):
                json_text = text_block.text
                
                # Clean the response to find the JSON object
                match = re.search(r'\{.*\}', json_text, re.DOTALL)
                if match:
                    clean_json = match.group(0)
                    try:
                        parsed_json = json.loads(clean_json)
                        formatted_json = json.dumps(parsed_json, indent=2)
                        progress(1.0, desc="Extraction complete!")
                        return f"```json\n{formatted_json}\n```"
                    except json.JSONDecodeError:
                        return f"⚠️ **AI Warning**: The model returned malformed JSON.\n\n{clean_json}"
                else:
                    return f"⚠️ **AI Warning**: The model did not return a JSON object.\n\n{json_text}"
            
        return "❌ **API Error**: The AI model returned an empty or invalid response."

    except Exception as e:
        return f"❌ **An unexpected error occurred during extraction**: {e}"


# === 3. Simplified Gradio UI ===

with gr.Blocks(title="Founder Name Extraction Tool", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🔎 Founder Name Extraction")
    gr.Markdown("A tool to find company founders. **Step 1:** Search for articles. **Step 2:** Extract founders' names from the results.")
    
    # Hidden state to pass text from search to extraction
    search_results_for_ai = gr.State("")

    with gr.Row():
        name_input = gr.Textbox(label="Company Name", placeholder="e.g., 'OpenAI', 'SpaceX'", scale=3)
        search_btn = gr.Button("1. 🔍 Search for Articles", variant="primary", scale=1)
    
    with gr.Row():
        extract_btn = gr.Button("2. 📊 Extract Founders from Search Results", variant="secondary")

    with gr.Tab("Search Results"):
        output_search = gr.Markdown()
    with gr.Tab("Founder Intelligence Report"):
        output_extract = gr.Markdown()

    # --- Event Wiring ---
    
    # Search button populates the search results tab and the hidden state
    search_btn.click(
        fn=search_workflow,
        inputs=[name_input],
        outputs=[output_search, search_results_for_ai],
        show_progress="full"
    )
    
    # Extract button uses the hidden state to populate the extraction tab
    extract_btn.click(
        fn=extraction_workflow,
        inputs=[search_results_for_ai, name_input],
        outputs=[output_extract],
        show_progress="full"
    )
    


demo.queue()

if __name__ == "__main__":
    demo.launch(show_error=True)