app.py

"""
Dashboard Narrator - Powered by OpenRouter.ai
A tool to analyze dashboard PDFs and images and generate comprehensive reports.
"""

# Import required libraries
import os
import time
import threading
import io
import base64
import json
import requests
from PyPDF2 import PdfReader
from PIL import Image
import markdown
from weasyprint import HTML, CSS
from weasyprint.text.fonts import FontConfiguration
from pdf2image import convert_from_bytes
import gradio as gr

# Create a global progress tracker
class ProgressTracker:
    def __init__(self):
        self.progress = 0
        self.message = "Ready"
        self.is_processing = False
        self.lock = threading.Lock()
    
    def update(self, progress, message="Processing..."):
        with self.lock:
            self.progress = progress
            self.message = message
    
    def get_status(self):
        with self.lock:
            return f"{self.message} ({self.progress:.1f}%)"
    
    def start_processing(self):
        with self.lock:
            self.is_processing = True
            self.progress = 0
            self.message = "Starting..."
    
    def end_processing(self):
        with self.lock:
            self.is_processing = False
            self.progress = 100
            self.message = "Complete"

# Create a global instance
progress_tracker = ProgressTracker()
output_status = None

# Function to update the Gradio interface with progress
def update_progress():
    global output_status
    while progress_tracker.is_processing:
        status = progress_tracker.get_status()
        if output_status is not None:
            output_status.update(value=status)
        time.sleep(0.5)
    return

# OpenRouter Client for making API calls
class OpenRouterClient:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://openrouter.ai/api/v1"
    
    def messages_create(self, model, messages, system=None, temperature=0.7, max_tokens=None):
        """Send messages to the OpenRouter API and return the response"""
        url = f"{self.base_url}/chat/completions"
        
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        payload = {
            "model": model,
            "messages": messages,
            "temperature": temperature,
        }
        
        # Add system message if provided
        if system:
            payload["messages"].insert(0, {"role": "system", "content": system})
        
        # Add max_tokens if provided
        if max_tokens:
            payload["max_tokens"] = max_tokens
        
        try:
            response = requests.post(url, headers=headers, json=payload)
            response.raise_for_status()  # Raise an exception for HTTP errors
            
            result = response.json()
            
            # Format the response to match the expected structure
            formatted_response = type('obj', (object,), {
                'content': [
                    type('obj', (object,), {
                        'text': result['choices'][0]['message']['content']
                    })
                ]
            })
            
            return formatted_response
            
        except requests.exceptions.RequestException as e:
            print(f"API request error: {str(e)}")
            if hasattr(e, 'response') and e.response:
                print(f"Response: {e.response.text}")
            raise

# Supported languages configuration
SUPPORTED_LANGUAGES = {
    "italiano": {
        "code": "it",
        "name": "Italiano",
        "report_title": "Analisi Dashboard",
        "report_subtitle": "Report Dettagliato",
        "date_label": "Data",
        "system_prompt": "Sei un esperto analista di business intelligence specializzato nell'interpretazione di dashboard e dati visualizzati. Fornisci analisi in italiano approfondite e insight actionable basati sui dati forniti.",
        "section_title": "ANALISI SEZIONE",
        "multi_doc_title": "ANALISI DASHBOARD {index}"
    },
    "english": {
        "code": "en",
        "name": "English",
        "report_title": "Dashboard Analysis",
        "report_subtitle": "Detailed Report",
        "date_label": "Date",
        "system_prompt": "You are an expert business intelligence analyst specialized in interpreting dashboards and data visualizations. Provide in-depth analysis and actionable insights based on the data provided.",
        "section_title": "SECTION ANALYSIS",
        "multi_doc_title": "DASHBOARD {index} ANALYSIS"
    },
    "français": {
        "code": "fr",
        "name": "Français",
        "report_title": "Analyse de Tableau de Bord",
        "report_subtitle": "Rapport Détaillé",
        "date_label": "Date",
        "system_prompt": "Vous êtes un analyste expert en business intelligence spécialisé dans l'interprétation des tableaux de bord et des visualisations de données. Fournissez en français une analyse approfondie et des insights actionnables basés sur les données fournies.",
        "section_title": "ANALYSE DE SECTION",
        "multi_doc_title": "ANALYSE DU TABLEAU DE BORD {index}"
    },
    "español": {
        "code": "es",
        "name": "Español",
        "report_title": "Análisis de Dashboard",
        "report_subtitle": "Informe Detallado",
        "date_label": "Fecha",
        "system_prompt": "Eres un analista experto en inteligencia empresarial especializado en interpretar dashboards y visualizaciones de datos. Proporciona en español un análisis en profundidad e insights accionables basados en los datos proporcionados.",
        "section_title": "ANÁLISIS DE SECCIÓN",
        "multi_doc_title": "ANÁLISIS DEL DASHBOARD {index}"
    },
    "deutsch": {
        "code": "de",
        "name": "Deutsch",
        "report_title": "Dashboard-Analyse",
        "report_subtitle": "Detaillierter Bericht",
        "date_label": "Datum",
        "system_prompt": "Sie sind ein Experte für Business Intelligence-Analyse, der auf die Interpretation von Dashboards und Datenvisualisierungen spezialisiert ist. Bieten Sie auf Deutsch eine eingehende Analyse und umsetzbare Erkenntnisse auf Grundlage der bereitgestellten Daten.",
        "section_title": "ABSCHNITTSANALYSE",
        "multi_doc_title": "DASHBOARD-ANALYSE {index}"
    }
}

# OpenRouter models - Updated with new models
DEFAULT_MODEL = "anthropic/claude-sonnet-4"
OPENROUTER_MODELS = [
    "anthropic/claude-sonnet-4",
    "anthropic/claude-3.7-sonnet",
    "openai/gpt-4.1",
    "openai/o4-mini-high",
    "openai/gpt-4.1-mini",
    "google/gemini-2.5-flash-preview-05-20",
    "google/gemini-2.5-pro-preview-03-25",
    "moonshotai/kimi-vl-a3b-thinking:free",
    "microsoft/phi-4-multimodal-instruct",
    "qwen/qwen2.5-vl-72b-instruct:free",
    "openrouter/optimus-alpha"
]

# Utility Functions
def extract_text_from_pdf(pdf_bytes):
    """Extract text from a PDF file."""
    try:
        pdf_reader = PdfReader(io.BytesIO(pdf_bytes))
        text = ""
        for page_num in range(len(pdf_reader.pages)):
            extracted = pdf_reader.pages[page_num].extract_text()
            if extracted:
                text += extracted + "\n"
        return text
    except Exception as e:
        print(f"Error extracting text from PDF: {str(e)}")
        return ""

def get_file_type(file_path):
    """Determine the file type based on file extension."""
    if file_path.lower().endswith('.pdf'):
        return 'pdf'
    elif file_path.lower().endswith(('.png', '.jpg', '.jpeg')):
        return 'image'
    else:
        return 'unknown'

def load_image_from_file(file_path):
    """Load an image from file path."""
    try:
        image = Image.open(file_path)
        # Convert to RGB if necessary
        if image.mode != 'RGB':
            image = image.convert('RGB')
        return image
    except Exception as e:
        print(f"Error loading image from {file_path}: {str(e)}")
        return None

def load_image_from_bytes(image_bytes):
    """Load an image from bytes."""
    try:
        image = Image.open(io.BytesIO(image_bytes))
        # Convert to RGB if necessary
        if image.mode != 'RGB':
            image = image.convert('RGB')
        return image
    except Exception as e:
        print(f"Error loading image from bytes: {str(e)}")
        return None

def divide_image_vertically(image, num_sections):
    """Divide an image vertically into sections."""
    width, height = image.size
    section_height = height // num_sections
    sections = []
    for i in range(num_sections):
        top = i * section_height
        bottom = height if i == num_sections - 1 else (i + 1) * section_height
        section = image.crop((0, top, width, bottom))
        sections.append(section)
        print(f"Section {i+1}: size {section.width}x{section.height} pixels")
    return sections

def encode_image_with_resize(image, max_size_mb=4.5):
    """Encode an image in base64, resizing if necessary."""
    max_bytes = max_size_mb * 1024 * 1024
    img_byte_arr = io.BytesIO()
    image.save(img_byte_arr, format='PNG')
    current_size = len(img_byte_arr.getvalue())
    if current_size > max_bytes:
        scale_factor = (max_bytes / current_size) ** 0.5
        new_width = int(image.width * scale_factor)
        new_height = int(image.height * scale_factor)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        img_byte_arr = io.BytesIO()
        resized_image.save(img_byte_arr, format='PNG', optimize=True)
        print(f"Image resized from {current_size/1024/1024:.2f}MB to {len(img_byte_arr.getvalue())/1024/1024:.2f}MB")
        image = resized_image
    else:
        print(f"Image size acceptable: {current_size/1024/1024:.2f}MB")
    buffer = io.BytesIO()
    image.save(buffer, format="PNG", optimize=True)
    return base64.b64encode(buffer.getvalue()).decode("utf-8")

# Core Analysis Functions
def analyze_dashboard_section(client, model, section_number, total_sections, image_section, full_text, language, goal_description=None):
    """Analyze a vertical section of the dashboard in the specified language."""
    print(f"Analyzing section {section_number}/{total_sections} in {language['name']} using {model}...")
    try:
        encoded_image = encode_image_with_resize(image_section)
    except Exception as e:
        print(f"Error encoding section {section_number}: {str(e)}")
        return f"Error analyzing section {section_number}: {str(e)}"
    
    section_prompt = f"""
        Act as a senior data analyst examining this dashboard section for Customer Experience purpose.\n
    Your analysis will be shared with top executives to inform about Customer Experience improvements and customer satisfaction level.\n
    # Dashboard Analysis - Section {section_number} of {total_sections}\n
    You are analyzing section {section_number} of {total_sections} of a long vertical dashboard. This is part of a broader analysis.\n
    {f"The analysis objective is: {goal_description}" if goal_description else ""}\n\n
    For this specific section:\n
    1. Describe what these visualizations show, including their type (e.g., bar chart, line graph) and the data they represent\n
    2. Quantitatively analyze the data, noting specific values, percentages, and numeric trends\n
    3. Identify significant patterns, anomalies, or outliers visible in the data\n
    4. Provide 2-3 actionable insights based on this analysis, explaining their business implications\n
    5. Suggest possible reasons for any notable trends or unexpected findings\n
    Focus exclusively on the visible section. Don't reference or speculate about unseen dashboard elements.\n
    Answer completely in {language['name']}.\n\n
    # Text extracted from the complete dashboard:\n
    {full_text[:10000] if full_text else "No text available for this image."}
    
    # Image of this dashboard section:
    [BASE64 IMAGE: {encoded_image[:20]}...] 
    This is a dashboard visualization showing various metrics and charts. Please analyze the content visible in this image.
    """
    
    # Create message with image for vision models
    message_content = [
        {
            "type": "text",
            "text": section_prompt
        },
        {
            "type": "image_url",
            "image_url": {
                "url": f"data:image/png;base64,{encoded_image}"
            }
        }
    ]
    
    try:
        response = client.messages_create(
            model=model,
            messages=[{"role": "user", "content": message_content}],
            system=language['system_prompt'],
            temperature=0.1,
            max_tokens=10000
        )
        return response.content[0].text
    except Exception as e:
        print(f"Error analyzing section {section_number}: {str(e)}")
        return f"Error analyzing section {section_number}: {str(e)}"

def create_comprehensive_report(client, model, section_analyses, full_text, language, goal_description=None):
    """Create a unified comprehensive report based on individual section analyses."""
    print(f"Generating final comprehensive report in {language['name']} using {model}...")
    comprehensive_prompt = f"""
    # Comprehensive Dashboard Analysis Request
    You have analyzed a long vertical dashboard in multiple sections. Now you need to create a unified and coherent report based on all the partial analyses.\n
    {f"The analysis objective is: {goal_description}" if goal_description else ""}\n\n
    Here are the analyses of the individual dashboard sections:\n
    {section_analyses}\n\n
    Based on these partial analyses, generate a professional, structured, and coherent report that includes:\n
    1. Executive Summary - Include key metrics, major findings, and critical recommendations (limit to 1 page equivalent)\n
    2. Dashboard Performance Overview - Add a section that evaluates the overall health metrics before diving into categories\n
    3 Detailed Analysis by Category - Keep this, it's essential\n
    4 Trend Analysis - Broaden from just temporal to include cross-category patterns\n
    5 Critical Issues and Opportunities - Combine anomalies with positive outliers to provide balanced insights\n
    6 Strategic Implications and Recommendations - Consolidate your insights and recommendations into a single, stronger section\n
    7 Implementation Roadmap - Convert your conclusions into a prioritized action plan with timeframes\n
    8 Appendix: Monitoring Improvements - Move the monitoring suggestions to an appendix unless they're a primary focus\n\n
    Integrate information from all sections to create a coherent and complete report.\n\n
    # Text extracted from the complete dashboard:\n
    {full_text[:10000] if full_text else "No text available for this image."}
    """
    try:
        response = client.messages_create(
            model=model,
            messages=[{"role": "user", "content": comprehensive_prompt}],
            system=language['system_prompt'],
            temperature=0.1,
            max_tokens=10000
        )
        return response.content[0].text
    except Exception as e:
        print(f"Error creating comprehensive report: {str(e)}")
        return f"Error creating comprehensive report: {str(e)}"

def create_multi_dashboard_comparative_report(client, model, individual_reports, language, goal_description=None):
    """Create a comparative report analyzing multiple dashboards together."""
    print(f"Generating comparative report for multiple dashboards in {language['name']} using {model}...")
    comparative_prompt = f"""
    # Multi-Dashboard Comparative Analysis Request
    You have analyzed multiple dashboards individually. Now you need to create a comparative analysis report that identifies patterns, similarities, differences, and insights across all dashboards.
    {f"The analysis objective is: {goal_description}" if goal_description else ""}
    Here are the analyses of the individual dashboards:
    {individual_reports}
    Based on these individual analyses, generate a professional, structured comparative report that includes:
    1. Executive Overview of All Dashboards
    2. Comparative Analysis of Key Metrics
    3. Cross-Dashboard Patterns and Trends
    4. Notable Differences Between Dashboards
    5. Integrated Insights from All Sources
    6. Comprehensive Strategic Recommendations
    7. Suggestions for Cross-Dashboard Monitoring Improvements
    8. Conclusions and Integrated Next Steps
    Integrate information from all dashboards to create a coherent comparative report.
    """
    try:
        response = client.messages_create(
            model=model,
            messages=[{"role": "user", "content": comparative_prompt}],
            system=language['system_prompt'],
            temperature=0.1,
            max_tokens=12000
        )
        return response.content[0].text
    except Exception as e:
        print(f"Error creating comparative report: {str(e)}")
        return f"Error creating comparative report: {str(e)}"

def markdown_to_pdf(markdown_content, output_filename, language):
    """Convert Markdown content to a well-formatted PDF."""
    print(f"Converting Markdown report to PDF in {language['name']}...")
    css = CSS(string='''
        @page { margin: 1.5cm; }
        body { font-family: Arial, sans-serif; line-height: 1.5; font-size: 11pt; }
        h1 { color: #2c3e50; font-size: 22pt; margin-top: 1cm; margin-bottom: 0.5cm; page-break-after: avoid; }
        h2 { color: #3498db; font-size: 16pt; margin-top: 0.8cm; margin-bottom: 0.3cm; page-break-after: avoid; }
        p { margin-bottom: 0.3cm; text-align: justify; }
    ''')
    today = time.strftime("%d/%m/%Y")
    cover_page = f"""
    <div style="text-align: center; height: 100vh; display: flex; flex-direction: column; justify-content: center; align-items: center;">
        <h1 style="font-size: 26pt; color: #2c3e50;">{language['report_title']}</h1>
        <h2 style="font-size: 14pt; color: #7f8c8d;">{language['report_subtitle']}</h2>
        <p style="font-size: 12pt; color: #7f8c8d;">{language['date_label']}: {today}</p>
    </div>
    <div style="page-break-after: always;"></div>
    """
    html_content = markdown.markdown(markdown_content, extensions=['tables', 'fenced_code'])
    full_html = f"""
    <!DOCTYPE html>
    <html lang="{language['code']}">
    <head><meta charset="UTF-8"><title>{language['report_title']}</title></head>
    <body>{cover_page}{html_content}</body>
    </html>
    """
    font_config = FontConfiguration()
    HTML(string=full_html).write_pdf(output_filename, stylesheets=[css], font_config=font_config)
    print(f"PDF created successfully: {output_filename}")
    return output_filename

def analyze_vertical_dashboard(client, model, file_data, file_type, language, goal_description=None, num_sections=4, dashboard_index=None):
    """Analyze a vertical dashboard by dividing it into sections. Supports both PDF and image files."""
    dashboard_marker = f" {dashboard_index}" if dashboard_index is not None else ""
    total_dashboards = progress_tracker.total_dashboards if hasattr(progress_tracker, 'total_dashboards') else 1
    dashboard_progress_base = ((dashboard_index - 1) / total_dashboards * 100) if dashboard_index is not None else 0
    dashboard_progress_step = (100 / total_dashboards) if total_dashboards > 0 else 100
    
    progress_tracker.update(dashboard_progress_base, f"🖼️ Analyzing dashboard{dashboard_marker}...")
    print(f"🖼️ Analyzing dashboard{dashboard_marker}...")
    
    # Extract text if it's a PDF
    full_text = ""
    if file_type == 'pdf':
        progress_tracker.update(dashboard_progress_base + dashboard_progress_step * 0.1, f"📄 Extracting text from dashboard{dashboard_marker}...")
        print(f"📄 Extracting full text from PDF...")
        full_text = extract_text_from_pdf(file_data)
        if not full_text or len(full_text.strip()) < 100:
            print("⚠️ Limited text extracted from PDF. Analysis will rely primarily on images.")
        else:
            print(f"✅ Extracted {len(full_text)} characters of text from PDF.")
    else:
        print("📄 Image file detected - no text extraction needed.")
    
    # Convert to image(s)
    progress_tracker.update(dashboard_progress_base + dashboard_progress_step * 0.2, f"🖼️ Converting dashboard{dashboard_marker} to images...")
    print("🖼️ Processing image...")
    
    try:
        if file_type == 'pdf':
            # Convert PDF to images
            pdf_images = convert_from_bytes(file_data, dpi=150)
            if not pdf_images:
                print("❌ Unable to convert PDF to images.")
                return None, "Error: Unable to convert PDF to images."
            print(f"✅ PDF converted to {len(pdf_images)} image pages.")
            main_image = pdf_images[0]
        else:
            # Load image directly
            main_image = load_image_from_bytes(file_data)
            if main_image is None:
                print("❌ Unable to load image.")
                return None, "Error: Unable to load image."
            print(f"✅ Image loaded successfully.")
        
        print(f"Main image size: {main_image.width}x{main_image.height} pixels")
        
        progress_tracker.update(dashboard_progress_base + dashboard_progress_step * 0.3, f"Dividing dashboard{dashboard_marker} into {num_sections} sections...")
        print(f"Dividing image into {num_sections} vertical sections...")
        image_sections = divide_image_vertically(main_image, num_sections)
        print(f"✅ Image divided into {len(image_sections)} sections.")
    except Exception as e:
        print(f"❌ Error processing image: {str(e)}")
        return None, f"Error: {str(e)}"
    
    section_analyses = []
    section_progress_step = dashboard_progress_step * 0.4 / len(image_sections)
    
    for i, section in enumerate(image_sections):
        section_progress = dashboard_progress_base + dashboard_progress_step * 0.3 + section_progress_step * i
        progress_tracker.update(section_progress, f"Analyzing section {i+1}/{len(image_sections)} of dashboard{dashboard_marker}...")
        
        print(f"\n{'='*50}")
        print(f"Processing section {i+1}/{len(image_sections)}...")
        section_result = analyze_dashboard_section(
            client, 
            model,
            i+1, 
            len(image_sections), 
            section, 
            full_text, 
            language,
            goal_description
        )
        if section_result:
            section_analyses.append(f"\n## {language['section_title']} {i+1}\n{section_result}")
            print(f"✅ Analysis of section {i+1} completed.")
        else:
            section_analyses.append(f"\n## {language['section_title']} {i+1}\nAnalysis not available for this section.")
            print(f"⚠️ Analysis of section {i+1} not available.")
    
    progress_tracker.update(dashboard_progress_base + dashboard_progress_step * 0.7, f"Generating final report for dashboard{dashboard_marker}...")
    print("\n" + "="*50)
    print(f"All section analyses completed. Generating report...")
    combined_sections = "\n".join(section_analyses)
    
    # If dashboard index is provided, add a header for the dashboard
    if dashboard_index is not None:
        dashboard_header = f"# {language['multi_doc_title'].format(index=dashboard_index)}\n\n"
        combined_sections = dashboard_header + combined_sections
    
    final_report = create_comprehensive_report(client, model, combined_sections, full_text, language, goal_description)
    
    # If dashboard index is provided, prepend it to the report
    if dashboard_index is not None and dashboard_index > 1:
        # Only add header if it doesn't already exist (might have been added by Claude)
        if not final_report.startswith(f"# {language['multi_doc_title'].format(index=dashboard_index)}"):
            final_report = f"# {language['multi_doc_title'].format(index=dashboard_index)}\n\n{final_report}"
    
    progress_tracker.update(dashboard_progress_base + dashboard_progress_step * 0.9, f"Finalizing dashboard{dashboard_marker} analysis...")
    return final_report, combined_sections


def get_available_models(api_key):
    """Get available models from OpenRouter API."""
    try:
        headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        response = requests.get("https://openrouter.ai/api/v1/models", headers=headers)
        
        if response.status_code == 200:
            models_data = response.json()
            available_models = [model["id"] for model in models_data.get("data", [])]
            
            # First add our preferred models at the top if they're available
            sorted_models = [model for model in OPENROUTER_MODELS if model in available_models]
            
            # Then add any additional models not in our predefined list
            additional_models = [model for model in available_models if model not in OPENROUTER_MODELS]
            additional_models.sort()
            
            all_models = ["custom"] + sorted_models + additional_models
            return all_models
        else:
            print(f"Error fetching models: {response.status_code}")
            return ["custom"] + OPENROUTER_MODELS
    except Exception as e:
        print(f"Error fetching models: {str(e)}")
        return ["custom"] + OPENROUTER_MODELS

def process_multiple_dashboards(api_key, files, language_code="it", goal_description=None, num_sections=4, model_name=DEFAULT_MODEL, custom_model=None):
    """Process multiple dashboard files (PDF/images) and create individual and comparative reports."""
    # Start progress tracking
    progress_tracker.start_processing()
    progress_tracker.total_dashboards = len(files)
    
    # Step 1: Initialize language settings and API client
    progress_tracker.update(1, "Initializing analysis...")
    language = None
    for lang_key, lang_data in SUPPORTED_LANGUAGES.items():
        if lang_data['code'] == language_code:
            language = lang_data
            break
    if not language:
        print(f"⚠️ Language '{language_code}' not supported. Using Italian as fallback.")
        language = SUPPORTED_LANGUAGES['italiano']
    print(f"🌐 Selected language: {language['name']}")
    
    if not api_key:
        progress_tracker.update(100, "⚠️ Error: API key not provided.")
        progress_tracker.end_processing()
        print("⚠️ Error: API key not provided.")
        return None, None, "Error: API key not provided."
    
    try:
        client = OpenRouterClient(api_key=api_key)
        print("✅ OpenRouter client initialized successfully.")
    except Exception as e:
        progress_tracker.update(100, f"❌ Error initializing client: {str(e)}")
        progress_tracker.end_processing()
        print(f"❌ Error initializing client: {str(e)}")
        return None, None, f"Error: {str(e)}"
    
    # Determine which model to use
    model = custom_model if model_name == "custom" and custom_model else model_name
    print(f"🤖 Using model: {model}")
    
    # Step 2: Process each dashboard individually
    individual_reports = []
    individual_analyses = []
    
    for i, (file_data, file_type) in enumerate(files):
        dashboard_progress_base = (i / len(files) * 80)  # 80% of progress for dashboard analysis
        progress_tracker.update(dashboard_progress_base, f"Processing dashboard {i+1}/{len(files)}...")
        print(f"\n{'#'*60}")
        print(f"Processing dashboard {i+1}/{len(files)} (Type: {file_type})...")
        
        report, analysis = analyze_vertical_dashboard(
            client=client,
            model=model,
            file_data=file_data,
            file_type=file_type,
            language=language,
            goal_description=goal_description,
            num_sections=num_sections,
            dashboard_index=i+1
        )
        
        if report:
            individual_reports.append(report)
            individual_analyses.append(analysis)
            print(f"✅ Analysis of dashboard {i+1} completed.")
        else:
            print(f"❌ Analysis of dashboard {i+1} failed.")
    
    # For Hugging Face Space: use tmp directory for file output
    tmp_dir = "/tmp"
    if not os.path.exists(tmp_dir):
        os.makedirs(tmp_dir, exist_ok=True)
    
    # Step 3: Generate output files
    progress_tracker.update(80, "Generating output files...")
    timestamp = time.strftime("%Y%m%d_%H%M%S")
    output_files = []
    
    # Create individual report files
    for i, report in enumerate(individual_reports):
        file_progress = 80 + (i / len(individual_reports) * 10)  # 10% for creating files
        progress_tracker.update(file_progress, f"Creating files for dashboard {i+1}...")
        
        md_filename = os.path.join(tmp_dir, f"dashboard_{i+1}_{language['code']}_{timestamp}.md")
        pdf_filename = os.path.join(tmp_dir, f"dashboard_{i+1}_{language['code']}_{timestamp}.pdf")
        
        with open(md_filename, 'w', encoding='utf-8') as f:
            f.write(report)
        output_files.append(md_filename)
        
        try:
            pdf_path = markdown_to_pdf(report, pdf_filename, language)
            output_files.append(pdf_filename)
        except Exception as e:
            print(f"⚠️ Error converting dashboard {i+1} to PDF: {str(e)}")
    
    # If there are multiple dashboards, create a comparative report
    comparative_report = None
    if len(individual_reports) > 1:
        progress_tracker.update(90, "Creating comparative analysis...")
        print("\n" + "#"*60)
        print("Creating comparative analysis of all dashboards...")
        
        # Combined report content
        all_reports_content = "\n\n".join(individual_reports)
        
        # Generate comparative analysis
        comparative_report = create_multi_dashboard_comparative_report(
            client=client,
            model=model,
            individual_reports=all_reports_content,
            language=language,
            goal_description=goal_description
        )
        
        # Save comparative report
        progress_tracker.update(95, "Saving comparative analysis files...")
        comparative_md = os.path.join(tmp_dir, f"comparative_analysis_{language['code']}_{timestamp}.md")
        comparative_pdf = os.path.join(tmp_dir, f"comparative_analysis_{language['code']}_{timestamp}.pdf")
        
        with open(comparative_md, 'w', encoding='utf-8') as f:
            f.write(comparative_report)
        output_files.append(comparative_md)
        
        try:
            pdf_path = markdown_to_pdf(comparative_report, comparative_pdf, language)
            output_files.append(comparative_pdf)
        except Exception as e:
            print(f"⚠️ Error converting comparative report to PDF: {str(e)}")
    
    # Complete progress tracking
    progress_tracker.update(100, "✅ Analysis completed successfully!")
    progress_tracker.end_processing()
    
   # Return the combined report content and all output files
    combined_content = "\n\n---\n\n".join(individual_reports)
    if len(individual_reports) > 1 and comparative_report:
        combined_content += f"\n\n{'='*80}\n\n# COMPARATIVE ANALYSIS\n\n{comparative_report}"
    
    return combined_content, output_files, "✅ Analysis completed successfully!"

# Wrapper function for Gradio interface
def process_dashboard(api_key, files, language_name, goal_description=None, num_sections=4, model_name=DEFAULT_MODEL, custom_model=None):
    """Process dashboard files (PDF/images) and generate reports (wrapper function for Gradio interface)."""
    # Start a thread to update progress
    progress_thread = threading.Thread(target=update_progress)
    progress_thread.daemon = True
    progress_thread.start()
    
    # Convert language name to language code
    language_code = "en"  # Default to English
    for lang_key, lang_data in SUPPORTED_LANGUAGES.items():
        if lang_data['name'].lower() == language_name.lower():
            language_code = lang_data['code']
            break
    
    # Process the uploaded files
    processed_files = []
    if files is not None:
        for file in files:
            try:
                # Handle different Gradio file formats
                file_path = None
                if isinstance(file, dict) and 'name' in file:
                    # Newer Gradio File component format
                    file_path = file['name']
                elif isinstance(file, str):
                    # Older Gradio File component format
                    file_path = file
                else:
                    # Try to get the path from the file object
                    if hasattr(file, 'name'):
                        file_path = file.name
                    elif hasattr(file, 'path'):
                        file_path = file.path
                
                if file_path:
                    # Determine file type
                    file_type = get_file_type(file_path)
                    
                    if file_type == 'unknown':
                        print(f"⚠️ Unsupported file type for {file_path}")
                        continue
                    
                    # Read file data
                    with open(file_path, 'rb') as f:
                        file_data = f.read()
                    
                    processed_files.append((file_data, file_type))
                    print(f"✅ Processed {file_path} as {file_type}")
                else:
                    print(f"⚠️ Could not determine file path for uploaded file")
                    
            except Exception as e:
                print(f"❌ Error processing uploaded file: {str(e)}")
                continue
    
    if not processed_files:
        error_message = "No valid files were uploaded or processed."
        progress_tracker.update(100, error_message)
        progress_tracker.end_processing()
        return None, None, error_message
    
    # Call the actual processing function
    try:
        combined_content, output_files, status = process_multiple_dashboards(
            api_key=api_key,
            files=processed_files,
            language_code=language_code,
            goal_description=goal_description,
            num_sections=num_sections,
            model_name=model_name,
            custom_model=custom_model
        )
        return combined_content, output_files, status
    except Exception as e:
        error_message = f"Error processing dashboards: {str(e)}"
        print(error_message)
        progress_tracker.update(100, error_message)
        progress_tracker.end_processing()
        return None, None, error_message

# Gradio Interface Functions
def toggle_custom_model(choice):
    """Toggle visibility of custom model input based on selection."""
    return {"visible": choice == "custom"}

def refresh_models(api_key):
    """Refresh the list of available models based on the API key."""
    if not api_key:
        return gr.Dropdown(choices=["custom"] + OPENROUTER_MODELS, value=DEFAULT_MODEL)
    
    try:
        available_models = get_available_models(api_key)
        return gr.Dropdown(choices=available_models, value=DEFAULT_MODEL)
    except Exception as e:
        print(f"Error refreshing models: {str(e)}")
        return gr.Dropdown(choices=["custom"] + OPENROUTER_MODELS, value=DEFAULT_MODEL)

# Define the Gradio interface
with gr.Blocks(title="Dashboard Narrator - Powered by OpenRouter.ai", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 📊 Dashboard Narrator - Powered by OpenRouter.ai
    Unlock the hidden stories in your dashboards!<br>
    Dashboard Narrator leverages advanced AI models through OpenRouter.ai to dissect your PDF reports and images,<br> 
    analyze each segment with expert precision, and craft comprehensive insights in your preferred language.<br><br>
    Turn complex data visualizations into clear, strategic recommendations and uncover trends you might have missed.<br>
    From executive summaries to detailed breakdowns, get the full narrative behind your numbers in just a few clicks.<br><br>
    **✨ New Features:**
    - Support for PNG and JPG image analysis
    - Enhanced with Claude Sonnet 4 and Gemini 2.5 Flash models
    - Multi-format dashboard analysis capabilities
    """)
    with gr.Row():
        with gr.Column(scale=1):
            api_key = gr.Textbox(label="OpenRouter API Key", placeholder="Enter your OpenRouter API key...", type="password")
            refresh_btn = gr.Button("🔄 Refresh Available Models", size="sm")
            
            model_choice = gr.Dropdown(
                choices=["custom"] + OPENROUTER_MODELS,
                value=DEFAULT_MODEL,
                label="Select Model"
            )
            
            custom_model = gr.Textbox(
                label="Custom Model ID", 
                placeholder="Enter custom model ID (e.g., anthropic/claude-3-opus:latest)...",
                visible=False
            )
            
            language = gr.Dropdown(
                choices=["Italiano", "English", "Français", "Español", "Deutsch"], 
                value="English", 
                label="Report Language"
            )
            
            num_sections = gr.Slider(
                minimum=2, 
                maximum=10, 
                value=4, 
                step=1, 
                label="Number of Vertical Sections per Dashboard"
            )
            
            goal = gr.Textbox(
                label="Analysis Goal (optional)", 
                placeholder="E.g., Analyze Q1 2024 sales KPIs..."
            )
            
            files = gr.File(
                label="Upload Dashboards (PDF, PNG, JPG)", 
                file_types=[".pdf", ".png", ".jpg", ".jpeg"], 
                file_count="multiple"
            )
            
            analyze_btn = gr.Button("🔍 Analyze Dashboards", variant="primary")
            
        with gr.Column(scale=2):
            with gr.Tab("Report"):
                output_md = gr.Markdown(label="Analysis Report", value="")
            with gr.Tab("Output Files"):
                output_files = gr.File(label="Download Files")
            output_status = gr.Textbox(label="Progress", placeholder="Upload dashboards and press Analyze...", interactive=False)
            # Progress component doesn't accept label in Gradio 5.21.0
            progress_bar = gr.Progress()
    
    # Handle model dropdown change
    model_choice.change(
        fn=toggle_custom_model,
        inputs=model_choice,
        outputs=custom_model,
    )
    
    # Handle refresh models button
    refresh_btn.click(
        fn=refresh_models,
        inputs=api_key,
        outputs=model_choice,
    )
    
    # Handle analyze button
    analyze_btn.click(
        fn=process_dashboard,
        inputs=[api_key, files, language, goal, num_sections, model_choice, custom_model],
        outputs=[output_md, output_files, output_status]
    )

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