app.py

import os
import pandas as pd
import gradio as gr
import glob
import threading
import time
import queue
import numpy as np
from rank_bm25 import BM25Okapi
from dotenv import load_dotenv
from smolagents import CodeAgent, LiteLLMModel
from agent import create_web_agent, generate_prompt
from followup_agent import run_followup_analysis
from unidecode import unidecode
    
load_dotenv()

# Global variables for progress tracking
progress_queue = queue.Queue()
current_status = ""

# Initialize LLM translator and BM25
llm_translator = None
bm25_model = None
precomputed_titles = None

def initialize_models():
    """Initialize the LLM translator and BM25 model"""
    global llm_translator, bm25_model, precomputed_titles
    
    if llm_translator is None:
        # Initialize LLM for translation
        try:
            model = LiteLLMModel(
                model_id="gemini/gemini-2.5-flash-preview-05-20",
                api_key=os.getenv("GEMINI_API_KEY")
            )
            llm_translator = CodeAgent(tools=[], model=model, max_steps=1)
            print("✅ LLM translator initialized")
        except Exception as e:
            print(f"⚠️  Error initializing LLM translator: {e}")
    
    # Load pre-computed BM25 model if available
    if bm25_model is None:
        try:
            import pickle
            with open('bm25_data.pkl', 'rb') as f:
                bm25_data = pickle.load(f)
                bm25_model = bm25_data['bm25_model']
                precomputed_titles = bm25_data['titles']
                print(f"✅ Loaded pre-computed BM25 model for {len(precomputed_titles)} datasets")
        except FileNotFoundError:
            print("⚠️  Pre-computed BM25 model not found. Will compute at runtime.")
        except Exception as e:
            print(f"⚠️  Error loading pre-computed BM25 model: {e}")
            print("Will compute BM25 at runtime.")

def translate_query_llm(query, target_lang='fr'):
    """Translate query using LLM"""
    global llm_translator
    
    if llm_translator is None:
        initialize_models()
    
    if llm_translator is None:
        print("⚠️  LLM translator not available, returning original query")
        return query, 'unknown'
    
    try:
        # Create translation prompt
        if target_lang == 'fr':
            target_language = "French"
        elif target_lang == 'en':
            target_language = "English"
        else:
            target_language = target_lang
        
        translation_prompt = f"""
        Translate the following text to {target_language}. 
        If the text is already in {target_language}, return it as is.
        Only return the translated text, nothing else.
        
        Text to translate: "{query}"
        """
        
        # Get translation from LLM
        response = llm_translator.run(translation_prompt)
        translated_text = str(response).strip().strip('"').strip("'")
        
        # Simple language detection
        if query.lower() == translated_text.lower():
            source_lang = target_lang
        else:
            source_lang = 'en' if target_lang == 'fr' else 'fr'
        
        return translated_text, source_lang
    
    except Exception as e:
        print(f"LLM translation error: {e}")
        return query, 'unknown'

def simple_keyword_preprocessing(text):
    """Simple preprocessing for keyword matching - handles case, accents and basic plurals"""
    # Convert to lowercase and remove accents
    text = unidecode(str(text).lower())
    
    # Basic plural handling - just remove trailing 's' and 'x'
    words = text.split()
    processed_words = []
    
    for word in words:
        # Remove common plural endings
        if word.endswith('s') and len(word) > 3 and not word.endswith('ss'):
            word = word[:-1]
        elif word.endswith('x') and len(word) > 3:
            word = word[:-1]
        processed_words.append(word)
    
    return processed_words

def find_similar_dataset_bm25(query, df):
    """Find the most similar dataset using BM25 keyword matching"""
    global bm25_model, precomputed_titles
    
    # Translate query to French for better matching with French datasets
    translated_query, original_lang = translate_query_llm(query, target_lang='fr')
    
    # Combine original and translated queries for search
    search_queries = [query, translated_query] if query != translated_query else [query]
    
    # Get dataset titles
    dataset_titles = df['title'].fillna('').tolist()
    
    # Use pre-computed BM25 model if available and matches current dataset
    if (bm25_model is not None and precomputed_titles is not None and 
        len(dataset_titles) == len(precomputed_titles) and dataset_titles == precomputed_titles):
        print("🚀 Using pre-computed BM25 model for fast matching")
        bm25 = bm25_model
    else:
        # Build BM25 model at runtime
        print("⚠️  Computing BM25 model at runtime...")
        # Preprocess all dataset titles into tokenized form
        processed_titles = [simple_keyword_preprocessing(title) for title in dataset_titles]
        bm25 = BM25Okapi(processed_titles)
    
    best_score = -1
    best_idx = 0
    
    for search_query in search_queries:
        try:
            # Preprocess the search query
            processed_query = simple_keyword_preprocessing(search_query)
            
            # Get BM25 scores for all documents
            scores = bm25.get_scores(processed_query)
            
            max_score = scores.max()
            max_idx = scores.argmax()
            if max_score > best_score:
                best_score = max_score
                best_idx = max_idx
        except Exception as e:
            print(f"Error processing query '{search_query}': {e}")
            continue
    
    # Show top 5 matches for comparison
    if len(search_queries) > 0:
        processed_query = simple_keyword_preprocessing(search_queries[0])
        scores = bm25.get_scores(processed_query)
    return best_idx, best_score, translated_query, original_lang

def create_progress_callback():
    """Create a callback function for tracking agent progress"""
    
    def progress_callback(memory_step, agent=None):
        """Callback function called at each agent step"""
        step_number = memory_step.step_number
        
        # Extract information about the current step
        if hasattr(memory_step, 'action_input') and memory_step.action_input:
            action_content = memory_step.action_input
        elif hasattr(memory_step, 'action_output') and memory_step.action_output:
            action_content = str(memory_step.action_output)
        else:
            action_content = ""
        
        # Define progress based on step content and number
        progress_val = min(0.1 + (step_number * 0.03), 0.95)  # Progressive increase
        
        # Analyze the step content to provide meaningful status
        action_lower = action_content.lower() if action_content else ""
        
        if "visit_webpage" in action_lower or "examining" in action_lower:
            description = f"🔍 Step {step_number}: Examining webpage..."
        elif "get_all_links" in action_lower or "links" in action_lower:
            description = f"🔗 Step {step_number}: Extracting data links..."
        elif "read_file_from_url" in action_lower or "reading" in action_lower:
            description = f"📊 Step {step_number}: Loading dataset..."
        elif "get_dataset_description" in action_lower or "description" in action_lower:
            description = f"📋 Step {step_number}: Analyzing dataset structure..."
        elif "department" in action_lower or "region" in action_lower:
            description = f"🗺️ Step {step_number}: Processing geographic data..."
        elif "plot" in action_lower or "map" in action_lower or "france" in action_lower:
            description = f"🗺️ Step {step_number}: Creating France map..."
        elif "visualization" in action_lower or "chart" in action_lower:
            description = f"📈 Step {step_number}: Generating visualizations..."
        elif "save" in action_lower or "png" in action_lower:
            description = f"💾 Step {step_number}: Saving visualizations..."
        elif "docx" in action_lower or "report" in action_lower:
            description = f"📄 Step {step_number}: Creating DOCX report..."
        elif hasattr(memory_step, 'error') and memory_step.error:
            description = f"⚠️ Step {step_number}: Handling error..."
        else:
            description = f"🤖 Step {step_number}: Processing..."
        
        # Check if this is the final step
        if hasattr(memory_step, 'action_output') and memory_step.action_output and "final" in action_lower:
            progress_val = 1.0
            description = "✅ Analysis complete!"
        
        # Put the progress update in the queue
        try:
            progress_queue.put((progress_val, description))
        except:
            pass
    
    return progress_callback

def run_agent_analysis_with_progress(query, progress_callback, df=None, page_url_callback=None, data_gouv_page=None, most_similar_idx=None):
    """
    Run the agent analysis with progress tracking using smolagents callbacks.
    """
    try:
        # Clean up previous results
        if os.path.exists('generated_data'):
            for file in glob.glob('generated_data/*'):
                try:
                    os.remove(file)
                except:
                    pass
        else:
            os.makedirs('generated_data', exist_ok=True)
        
        # If dataset info not provided, find it (fallback)
        if data_gouv_page is None or most_similar_idx is None:
            progress_callback(0.02, "🤖 Initializing LLM translator and BM25...")
            initialize_models()
            
            progress_callback(0.05, "🔍 Searching for relevant datasets (using BM25 keyword matching)...")
            
            # Read the filtered dataset if not provided
            if df is None:
                df = pd.read_csv('filtered_dataset.csv')
            
            # Find the most similar dataset using BM25 keyword matching
            most_similar_idx, similarity_score, translated_query, original_lang = find_similar_dataset_bm25(query, df)
            data_gouv_page = df.iloc[most_similar_idx]['url']
            
            # Immediately show the page URL via callback
            if page_url_callback:
                page_url_callback(data_gouv_page)
            
            progress_callback(0.08, "🤖 Initializing agent...")
        else:
            # Dataset already found, continue from where we left off
            progress_callback(0.09, "🤖 Initializing agent...")

        step_callback = create_progress_callback()
        
        progress_callback(0.1, "🤖 Starting agent analysis...")
        
        # Create the agent with progress callback
        web_agent = create_web_agent(step_callback)
        prompt = generate_prompt(data_gouv_page)
        
        # Run the agent - the step_callbacks will automatically update progress
        answer = web_agent.run(prompt)
        
        # Check if the agent found no processable data
        answer_lower = str(answer).lower() if answer else ""
        if ("no processable data" in answer_lower or 
            "no csv nor json" in answer_lower or 
            "cannot find csv" in answer_lower or 
            "cannot find json" in answer_lower or
            "no data to process" in answer_lower):
            progress_callback(1.0, "❌ No CSV/JSON files found in the dataset")
            return "❌ No CSV/JSON files found in the selected dataset. This dataset cannot be processed automatically.", [], data_gouv_page
        
        # Check if files were generated
        generated_files = glob.glob('generated_data/*')
        
        if generated_files:
            progress_callback(1.0, "✅ Analysis completed successfully!")
            return "Analysis completed successfully!", generated_files, data_gouv_page
        else:
            progress_callback(1.0, "⚠️ Analysis completed but no files were generated.")
            return "Analysis completed but no files were generated.", [], data_gouv_page
            
    except Exception as e:
        progress_callback(1.0, f"❌ Error: {str(e)}")
        return f"Error during analysis: {str(e)}", [], None

def search_and_analyze(query, progress=gr.Progress()):
    """
    Unified function that does initial search then lets agent analyze with full autonomy.
    Uses Gradio's progress bar for visual feedback.
    """
    # Clear the progress queue
    while not progress_queue.empty():
        try:
            progress_queue.get_nowait()
        except queue.Empty:
            break
    
    # Initialize outputs
    docx_file = None
    images_output = [gr.Image(visible=False)] * 4
    status = "🚀 Starting agent-driven analysis..."
    
    # Initial progress
    progress(0.05, desc="🚀 Initializing agent...")
    
    def progress_callback(progress_val, description):
        """Callback function to update progress - puts updates in queue"""
        try:
            progress_queue.put((progress_val, description))
        except:
            pass
    
    # Run analysis in a separate thread
    result_queue = queue.Queue()
    
    def run_analysis():
        try:
            # Clean up previous results
            if os.path.exists('generated_data'):
                for file in glob.glob('generated_data/*'):
                    try:
                        os.remove(file)
                    except:
                        pass
            else:
                os.makedirs('generated_data', exist_ok=True)
            
            # Do initial search if query provided
            initial_search_results = None
            if query.strip():
                progress_callback(0.06, f"🔍 Initial search for: {query[:50]}...")
                try:
                    # Import search function from tools
                    from tools.retrieval_tools import search_datasets
                    initial_search_results = search_datasets(query, top_k=5)
                    progress_callback(0.08, "🤖 Starting agent with search results...")
                except Exception as e:
                    print(f"Initial search failed: {e}")
                    progress_callback(0.08, "🤖 Starting agent without initial results...")
            else:
                progress_callback(0.08, "🤖 Starting agent for random selection...")
            
            step_callback = create_progress_callback()
            
            # Create the agent with progress callback
            web_agent = create_web_agent(step_callback)
            
            # Generate unified prompt with initial search results
            prompt = generate_prompt(user_query=query, initial_search_results=initial_search_results)
            progress_callback(0.1, "🤖 Agent analyzing datasets...")
            
            # Run the agent - the step_callbacks will automatically update progress
            answer = web_agent.run(prompt)
            
            # Check if the agent found no processable data
            answer_lower = str(answer).lower() if answer else ""
            if ("no processable data" in answer_lower or 
                "no csv nor json" in answer_lower or 
                "cannot find csv" in answer_lower or 
                "cannot find json" in answer_lower or
                "no data to process" in answer_lower):
                progress_callback(1.0, "❌ No CSV/JSON files found in the dataset")
                result_queue.put(("❌ No CSV/JSON files found in the selected dataset. This dataset cannot be processed automatically.", [], None))
                return
            
            # Check if files were generated
            generated_files = glob.glob('generated_data/*')
            
            if generated_files:
                progress_callback(1.0, "✅ Analysis completed successfully!")
                result_queue.put(("Analysis completed successfully!", generated_files, "Agent-selected dataset"))
            else:
                progress_callback(1.0, "⚠️ Analysis completed but no files were generated.")
                result_queue.put(("Analysis completed but no files were generated.", [], None))
                
        except Exception as e:
            progress_callback(1.0, f"❌ Error: {str(e)}")
            result_queue.put((f"Error during analysis: {str(e)}", [], None))
    
    analysis_thread = threading.Thread(target=run_analysis)
    analysis_thread.start()
    
    # Show initial status
    current_status = "🤖 Agent is finding relevant datasets..."
    progress(0.08, desc=current_status)
    
    # Monitor progress while analysis runs
    last_progress = 0.08
    
    while analysis_thread.is_alive() or not result_queue.empty():
        try:
            # Check for progress updates from queue
            try:
                progress_val, description = progress_queue.get(timeout=0.1)
                if progress_val > last_progress:
                    last_progress = progress_val
                    current_status = description
                    progress(progress_val, desc=description)
            except queue.Empty:
                pass
            
            # Check if analysis is complete
            try:
                final_status, files, page_url = result_queue.get(timeout=0.1)
                
                # Check if this is a "no data" case
                if "❌ No CSV/JSON files found" in final_status:
                    progress(1.0, desc="❌ No processable data found")
                    return (gr.Textbox(value="Agent-selected dataset", visible=True), 
                           final_status, 
                           gr.File(visible=False),
                           gr.Image(visible=False), gr.Image(visible=False), 
                           gr.Image(visible=False), gr.Image(visible=False),
                           gr.Markdown(visible=False),  # keep follow-up hidden
                           gr.HTML(visible=False),      
                           gr.Row(visible=False),       
                           gr.Row(visible=False),       
                           gr.Row(visible=False),       
                           gr.Row(visible=False),       
                           gr.Row(visible=False))
                
                # Final progress update
                progress(1.0, desc="✅ Processing results...")
                
                # Process results
                docx_file = None
                png_files = []
                
                for file in files:
                    if file.endswith('.docx'):
                        docx_file = file
                    elif file.endswith('.png'):
                        png_files.append(file)
                
                # Prepare final outputs
                download_button = gr.File(value=docx_file, visible=True) if docx_file else None
                
                # Prepare images for display (up to 4 images)
                images = []
                for i in range(4):
                    if i < len(png_files):
                        images.append(gr.Image(value=png_files[i], visible=True))
                    else:
                        images.append(gr.Image(visible=False))
                
                # final progress completion
                progress(1.0, desc="🎉 Complete!")
                
                # Show follow-up section after successful completion
                return (gr.Textbox(value=page_url if page_url else "Agent-selected dataset", visible=True), 
                       final_status, download_button, *images,
                       gr.Markdown(visible=True),  # followup_section_divider
                       gr.HTML(visible=True),      # followup_section_header  
                       gr.Row(visible=True),       # followup_input_row
                       gr.Row(visible=True),       # followup_result_row
                       gr.Row(visible=True),       # followup_image_row
                       gr.Row(visible=True),       # followup_examples_header_row
                       gr.Row(visible=True))       # followup_examples_row
                
            except queue.Empty:
                pass
                
            time.sleep(0.5)  # Small delay to prevent excessive updates
            
        except Exception as e:
            progress(1.0, desc=f"❌ Error: {str(e)}")
            return (gr.Textbox(value="Error", visible=True), f"❌ Error: {str(e)}", None, *images_output,
                   gr.Markdown(visible=False),  # keep follow-up hidden on error
                   gr.HTML(visible=False),      
                   gr.Row(visible=False),       
                   gr.Row(visible=False),       
                   gr.Row(visible=False),       
                   gr.Row(visible=False),       
                   gr.Row(visible=False))
    
    # Ensure thread completes
    analysis_thread.join(timeout=1)
    
    # Fallback return
    progress(1.0, desc="🏁 Finished")
    return (gr.Textbox(value="Completed", visible=True), current_status, docx_file, *images_output,
           gr.Markdown(visible=False),  # keep follow-up hidden
           gr.HTML(visible=False),      
           gr.Row(visible=False),       
           gr.Row(visible=False),       
           gr.Row(visible=False),       
           gr.Row(visible=False),       
           gr.Row(visible=False))

def run_followup_question(question, progress=gr.Progress()):
    """
    Run a follow-up analysis based on user's question about the previous report.
    """
    if not question.strip():
        return "Please enter a follow-up question.", gr.Image(visible=False)
    
    progress(0.1, desc="🤖 Starting follow-up analysis...")
    
    try:
        # Check if there are previous results
        if not os.path.exists('generated_data') or not os.listdir('generated_data'):
            return "No previous analysis found. Please run an analysis first.", gr.Image(visible=False)
        
        progress(0.3, desc="🔍 Analyzing previous report and dataset...")
        
        # Run the follow-up analysis
        result = run_followup_analysis(question)
        
        progress(0.9, desc="📊 Processing results...")
        
        # Look for new visualizations created by the follow-up analysis
        import glob
        
        # Get all images that were created after the analysis started
        all_images = glob.glob('generated_data/*.png')
        
        # Get recent images (created in the last few seconds)
        import time
        current_time = time.time()
        recent_images = []
        
        for img_path in all_images:
            img_time = os.path.getctime(img_path)
            if current_time - img_time < 120:  # Images created in last 2 minutes
                recent_images.append(img_path)
        
        # Get the most recent image if any
        latest_image = None
        if recent_images:
            latest_image = max(recent_images, key=os.path.getctime)
        
        progress(1.0, desc="✅ Follow-up analysis complete!")
        
        # Enhanced result formatting
        final_result = result
        if latest_image:
            final_result += f"\n\n📊 **Visualization Created:** {os.path.basename(latest_image)}"
            if len(recent_images) > 1:
                final_result += f"\n📈 **Total new visualizations:** {len(recent_images)}"
            return final_result, gr.Image(value=latest_image, visible=True)
        else:
            return final_result, gr.Image(visible=False)
        
    except Exception as e:
        progress(1.0, desc="❌ Error in follow-up analysis")
        return f"Error: {str(e)}", gr.Image(visible=False)

# Create the Gradio interface
with gr.Blocks(title="🤖 French Public Data Analysis Agent", theme=gr.themes.Soft(), css="""
    .gradio-container {
        max-width: 1200px !important;
        margin: auto;
        width: 100% !important;
    }
    .main-header {
        text-align: center;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        padding: 2rem;
        border-radius: 15px;
        margin-bottom: 2rem;
        box-shadow: 0 8px 32px rgba(0,0,0,0.1);
    }
    .accordion-content {
        overflow: hidden !important;
        width: 100% !important;
    }
    .gr-accordion {
        width: 100% !important;
        max-width: 100% !important;
    }
    .gr-accordion .gr-row {
        width: 100% !important;
        max-width: 100% !important;
        margin: 0 !important;
    }
    .gr-accordion .gr-column {
        min-width: 0 !important;
        flex: 1 !important;
        max-width: 50% !important;
        padding-right: 1rem !important;
    }
    .gr-accordion .gr-column:last-child {
        padding-right: 0 !important;
        padding-left: 1rem !important;
    }
""") as demo:
    
    # Main header with better styling
    gr.HTML("""
    <div class="main-header">
        <h1 style="margin: 0; font-size: 2.5rem; font-weight: bold;">
            🤖 French Public Data Analysis Agent
        </h1>
        <p style="font-size: 1.2rem; opacity: 0.9;">
            Intelligent analysis of French public datasets with AI-powered insights
        </p>
    </div>
    """)
    
    # What this agent does
    gr.HTML("""
    <div style="text-align: center; background: #f8fafc; padding: 1.5rem; border-radius: 10px; margin: 1rem 0;">
        <p style="font-size: 1.1rem; color: #374151; margin: 0;">
            🌐 <strong>Search in French or English</strong> • 🤖 <strong>AI Agent finds & analyzes datasets</strong> • 🗺️ <strong>Generate Reports with visualizations</strong>
        </p>
        <p style="font-size: 0.9rem; color: #6b7280; margin-top: 0.5rem;">
            Initial search results guide the agent, but it can search for different datasets if needed
        </p>
    </div>
    """)
    
    # Tips & Information accordion - moved to the top
    with gr.Accordion("💡 Tips & Information", open=False):
        with gr.Row():
            with gr.Column():
                gr.Markdown("""
                🎯 **How to Use:**
                - Enter search terms related to French public data
                - Leave empty for random high-quality dataset selection
                - System provides initial search results to guide the agent
                - Agent can use provided results or search for different datasets
                - Results include visualizations and downloadable reports
                
                ⏱️ **Processing Time:**
                - Analysis takes 7-15 minutes depending on dataset complexity
                - Agent has full autonomy to find the best datasets
                """)
            with gr.Column():
                gr.Markdown("""
                ⚠️ **Important Notes:**
                - Agent gets initial search results but has full autonomy to make decisions
                - Agent can choose from initial results or search for different datasets
                - Some datasets may not contain processable CSV/JSON files
                - All visualizations are automatically generated
                - Maps focus on France when geographic data is available
                
                🌐 **Language Support:**
                - Search in French or English - queries are automatically translated
                """)
    
    with gr.Row():
        query_input = gr.Textbox(
            label="Search Query", 
            placeholder="e.g., road traffic accidents, education, housing (or leave empty for random selection)",
            scale=4
        )
        search_button = gr.Button(
            "🚀 Analyze Dataset", 
            variant="primary", 
            scale=1,
            size="lg"
        )
    
    # Quick Start Examples row
    with gr.Row():
        gr.HTML("""
        <div>
            <h3 style="color: #374151">🚀 Quick Start Examples</h3>
            <p style="color: #6b7280">Click any example below to get started</p>
        </div>
        """)
    
    with gr.Row():
        examples = [
            ("🚗 Road Traffic Accidents 2023", "road traffic accidents 2023"),
            ("🎓 Education Directory", "education directory"), 
            ("🏠 French Vacant Housing Private Park", "French vacant housing private park"),
        ]
        
        for emoji_text, query_text in examples:
            gr.Button(
                emoji_text, 
                variant="secondary",
                size="sm"
            ).click(
                lambda x=query_text: x,
                outputs=query_input
            )
    
    # Page info and analysis status with progress bar
    with gr.Group():
        page_url_display = gr.Textbox(label="🔗 Page Started On", interactive=False, visible=False)
        with gr.Row():
            status_output = gr.Textbox(label="📊 Analysis Status", interactive=False, scale=1)
    
    # Download section
    with gr.Row():
        download_button = gr.File(
            label="📄 Download DOCX Report", 
            visible=False
        )
    
    gr.Markdown("---")
    gr.HTML("""
    <div style="text-align: center; margin: 2rem 0;">
        <h2 style="color: #374151; margin-bottom: 0.5rem;">📊 Generated Visualizations</h2>
        <p style="color: #6b7280; margin: 0;">Automatically generated charts and maps will appear below</p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column():
            image1 = gr.Image(label="📈 Chart 1", visible=False, height=400)
            image2 = gr.Image(label="📊 Chart 2", visible=False, height=400)
        with gr.Column():
            image3 = gr.Image(label="🗺️ Map/Chart 3", visible=False, height=400)
            image4 = gr.Image(label="📉 Chart 4", visible=False, height=400)
    
    # Follow-up Analysis Section (initially hidden)
    followup_section_divider = gr.Markdown("---", visible=False)
    followup_section_header = gr.HTML("""
    <div style="text-align: center; margin: 2rem 0;">
        <h2 style="color: #374151; margin-bottom: 0.5rem;">🤖 Follow-up Analysis</h2>
        <p style="color: #6b7280; margin: 0;">Ask about report findings, request data analysis, or get contextual information</p>
    </div>
    """, visible=False)
    
    with gr.Row(visible=False) as followup_input_row:
        followup_input = gr.Textbox(
            label="Follow-up Question",
            placeholder="e.g., What are the main findings?, Show me correlation between columns, What is road safety policy in France?",
            scale=4
        )
        followup_button = gr.Button(
            "🔍 Analyze",
            variant="secondary",
            scale=1,
            size="lg"
        )
    
    with gr.Row(visible=False) as followup_result_row:
        followup_result = gr.Textbox(
            label="📊 Follow-up Analysis Results",
            interactive=False,
            lines=10,
            visible=True
        )
    
    with gr.Row(visible=False) as followup_image_row:
        followup_image = gr.Image(
            label="📈 Follow-up Visualization",
            visible=False,
            height=500
        )
    
    # Follow-up Examples (initially hidden)
    with gr.Row(visible=False) as followup_examples_header_row:
        gr.HTML("""
        <div>
            <h4 style="color: #374151">💡 Example Follow-up Questions</h4>
            <p style="color: #6b7280">Click any example below to try it out</p>
        </div>
        """)
    
    with gr.Row(visible=False) as followup_examples_row:
        followup_examples = [
            ("📋 Report Summary", "What were the main findings from the analysis?"),
            ("🌐 Context Info", "What is the policy context for this data in France?"),
            ("📊 Create Chart", "Show me the correlation between two numerical columns with a scatter plot"),
            ("📈 Data Statistics", "Give me statistical summary for a specific column"),
            ("🎯 Filter Data", "Filter the data by specific criteria and show results"),
            ("🔍 General Question", "Tell me more about this topic and its importance"),
        ]
        
        for emoji_text, query_text in followup_examples:
            gr.Button(
                emoji_text, 
                variant="secondary",
                size="sm"
            ).click(
                lambda x=query_text: x,
                outputs=followup_input
            )
    
    # Set up the search button click event with progress bar
    search_button.click(
        fn=search_and_analyze,
        inputs=[query_input],
        outputs=[page_url_display, status_output, download_button, image1, image2, image3, image4,
                followup_section_divider, followup_section_header, followup_input_row, 
                followup_result_row, followup_image_row, followup_examples_header_row, followup_examples_row],
        show_progress="full"  # Show the built-in progress bar
    )
    
    # Set up the follow-up button click event
    followup_button.click(
        fn=run_followup_question,
        inputs=[followup_input],
        outputs=[followup_result, followup_image],
        show_progress="full"
    )
    


if __name__ == "__main__":
    demo.queue()  # Enable queuing for real-time updates
    demo.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True 
    )