Delete app.py

app.py

@@ -1,570 +0,0 @@
-import gradio as gr
-import PyPDF2
-import fitz  # PyMuPDF
-import io
-import os
-from groq import Groq
-import tempfile
-import traceback
-import base64
-from io import BytesIO
-from PIL import Image
-
-def extract_text_from_pdf(pdf_file):
-    """Extract text from uploaded PDF file"""
-    try:
-        if pdf_file is None:
-            return None, "No PDF file provided"
-        
-        # Read the PDF file
-        with open(pdf_file.name, 'rb') as file:
-            pdf_reader = PyPDF2.PdfReader(file)
-            text = ""
-            
-            # Extract text from all pages
-            for page_num in range(len(pdf_reader.pages)):
-                page = pdf_reader.pages[page_num]
-                text += page.extract_text() + "\n"
-            
-            if not text.strip():
-                return None, "Could not extract text from PDF. The PDF might be image-based or encrypted."
-            
-            return text, None
-    
-    except Exception as e:
-        return None, f"Error reading PDF: {str(e)}"
-
-def extract_images_from_pdf(pdf_file_path):
-    """Extract all images from PDF and return them as PIL Images with page info"""
-    images = []
-    
-    # Open the PDF
-    pdf_document = fitz.open(pdf_file_path)
-    
-    for page_num in range(len(pdf_document)):
-        page = pdf_document.load_page(page_num)
-        
-        # Get image list from the page
-        image_list = page.get_images(full=True)
-        
-        for img_index, img in enumerate(image_list):
-            # Get the XREF of the image
-            xref = img[0]
-            
-            # Extract the image bytes
-            base_image = pdf_document.extract_image(xref)
-            image_bytes = base_image["image"]
-            
-            # Convert to PIL Image
-            pil_image = Image.open(BytesIO(image_bytes))
-            
-            images.append({
-                'image': pil_image,
-                'page': page_num + 1,
-                'index': img_index + 1,
-                'format': base_image["ext"]
-            })
-    
-    pdf_document.close()
-    return images
-
-def convert_pdf_pages_to_images(pdf_file_path, dpi=150):
-    """Convert each PDF page to an image for comprehensive analysis"""
-    images = []
-    
-    # Open the PDF
-    pdf_document = fitz.open(pdf_file_path)
-    
-    for page_num in range(len(pdf_document)):
-        page = pdf_document.load_page(page_num)
-        
-        # Convert page to image
-        mat = fitz.Matrix(dpi/72, dpi/72)  # zoom factor
-        pix = page.get_pixmap(matrix=mat)
-        
-        # Convert to PIL Image
-        img_data = pix.tobytes("png")
-        pil_image = Image.open(BytesIO(img_data))
-        
-        images.append({
-            'image': pil_image,
-            'page': page_num + 1,
-            'type': 'full_page'
-        })
-    
-    pdf_document.close()
-    return images
-
-def encode_image_to_base64(pil_image):
-    """Convert PIL Image to base64 string"""
-    buffered = BytesIO()
-    pil_image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode()
-    return f"data:image/png;base64,{img_str}"
-
-def summarize_with_groq(api_key, text):
-    """Generate summary using Groq API"""
-    try:
-        if not api_key or not api_key.strip():
-            return "Please enter your Groq API key"
-        
-        if not text or not text.strip():
-            return "No text to summarize"
-        
-        # Initialize Groq client
-        client = Groq(api_key=api_key.strip())
-        
-        # System prompt for summarization
-        system_prompt = """You are a highly capable language model specialized in document summarization. Your task is to read and understand the full content of a multi-page PDF document and generate a clear, accurate, and detailed summary of the entire document.
-Focus on capturing all main ideas, key points, arguments, findings, and conclusions presented throughout the document. If the document is technical, legal, academic, or contains structured sections (e.g., introduction, methods, results, discussion), maintain the logical flow and structure while expressing the content in a comprehensive and accessible manner.
-Avoid unnecessary simplification. Include important details, supporting evidence, and nuanced insights that reflect the depth of the original material. Do not copy the text verbatim.
-Output only the summary. Do not explain your process. Use a neutral, professional, and informative tone. The summary should provide a full understanding of the document to someone who has not read it."""
-
-        # Create completion with optimal hyperparameters
-        completion = client.chat.completions.create(
-            model="llama-3.3-70b-versatile",
-            messages=[
-                {
-                    "role": "system",
-                    "content": system_prompt
-                },
-                {
-                    "role": "user", 
-                    "content": f"Please summarize the following document:\n\n{text}"
-                }
-            ],
-            temperature=0.3,        # Low randomness for factual, focused summaries
-            max_completion_tokens=2048,  # Increased to allow longer summaries
-            top_p=0.9,             # Allows some diversity while still grounded
-            stream=False,
-            stop=None,
-        )
-        
-        summary = completion.choices[0].message.content
-        return summary
-    
-    except Exception as e:
-        error_msg = f"Error generating summary: {str(e)}"
-        if "authentication" in str(e).lower() or "api" in str(e).lower():
-            error_msg += "\n\nPlease check your Groq API key and ensure it's valid."
-        return error_msg
-
-def analyze_images_with_groq(api_key, images):
-    """Analyze images using Groq API"""
-    if not api_key:
-        return "❌ Please enter your Groq API key."
-    
-    try:
-        client = Groq(api_key=api_key)
-        
-        results = []
-        
-        for idx, img_data in enumerate(images):
-            # Encode image to base64
-            base64_image = encode_image_to_base64(img_data['image'])
-            
-            # Prepare messages for the API call
-            messages = [
-                {
-                    "role": "system",
-                    "content": """You are an advanced language model with strong capabilities in visual and textual understanding. Your task is to analyze all images, diagrams, and flowcharts within a PDF document. This includes:
-
-1. Extracting and interpreting text from images and flowcharts.
-2. Understanding the visual structure, logic, and relationships depicted in diagrams.
-3. Summarizing the content and purpose of each visual element in a clear and informative manner.
-
-After processing, be ready to answer user questions about any of the images or flowcharts, including their meaning, structure, data, process flows, or relationships shown.
-
-Be accurate, concise, and visually aware. Clearly explain visual content in text form. Do not guess if visual data is unclear or ambiguous — instead, state what is observable.
-
-Use a neutral, helpful tone. Do not include irrelevant information or commentary unrelated to the visual content. When summarizing or answering questions, assume the user may not have access to the original image or diagram."""
-                },
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": f"Please analyze this image from page {img_data.get('page', 'unknown')} of the PDF document. Provide a detailed analysis of all visual elements, text, diagrams, flowcharts, and their relationships."
-                        },
-                        {
-                            "type": "image_url",
-                            "image_url": {
-                                "url": base64_image
-                            }
-                        }
-                    ]
-                }
-            ]
-            
-            # Make API call with optimal parameters
-            completion = client.chat.completions.create(
-                model="meta-llama/llama-4-scout-17b-16e-instruct",
-                messages=messages,
-                temperature=0.2,
-                max_completion_tokens=2048,
-                top_p=0.85,
-                stream=False
-            )
-            
-            analysis = completion.choices[0].message.content
-            
-            page_info = f"Page {img_data.get('page', 'N/A')}"
-            if 'index' in img_data:
-                page_info += f", Image {img_data['index']}"
-            elif 'type' in img_data and img_data['type'] == 'full_page':
-                page_info += " (Full Page)"
-            
-            results.append(f"## 📄 {page_info}\n\n{analysis}\n\n---\n")
-        
-        if not results:
-            return "⚠️ No images found in the PDF document."
-        
-        return "\n".join(results)
-        
-    except Exception as e:
-        return f"❌ Error analyzing images: {str(e)}"
-
-def process_pdf_text_summary(api_key, pdf_file, progress=gr.Progress()):
-    """Process PDF and generate text summary"""
-    try:
-        if not api_key or not api_key.strip():
-            return "❌ Please enter your Groq API key", "", ""
-        
-        if pdf_file is None:
-            return "❌ Please upload a PDF file", "", ""
-        
-        progress(0.1, desc="Reading PDF file...")
-        
-        # Extract text from PDF
-        text, error = extract_text_from_pdf(pdf_file)
-        if error:
-            return f"❌ {error}", "", ""
-        
-        progress(0.4, desc="Text extracted successfully...")
-        
-        # Show preview of extracted text
-        text_preview = text[:500] + "..." if len(text) > 500 else text
-        
-        progress(0.6, desc="Generating summary with Groq AI...")
-        
-        # Generate summary
-        summary = summarize_with_groq(api_key, text)
-        
-        progress(1.0, desc="Summary generated successfully!")
-        
-        return "✅ Summary generated successfully!", text_preview, summary
-    
-    except Exception as e:
-        error_traceback = traceback.format_exc()
-        return f"❌ Unexpected error: {str(e)}\n\nTraceback:\n{error_traceback}", "", ""
-
-def process_pdf_image_analysis(api_key, pdf_file, analysis_method, progress=gr.Progress()):
-    """Process PDF and analyze images"""
-    if pdf_file is None:
-        return "⚠️ Please upload a PDF file."
-    
-    if not api_key or api_key.strip() == "":
-        return "⚠️ Please enter your Groq API key."
-    
-    try:
-        progress(0.1, desc="Processing PDF file...")
-        
-        # Create temporary file for PDF processing
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
-            # Write uploaded file content to temporary file
-            if hasattr(pdf_file, 'read'):
-                tmp_file.write(pdf_file.read())
-            else:
-                with open(pdf_file.name, 'rb') as f:
-                    tmp_file.write(f.read())
-            tmp_file_path = tmp_file.name
-        
-        progress(0.3, desc="Extracting images...")
-        
-        images_to_analyze = []
-        
-        if analysis_method == "Extract embedded images only":
-            # Extract only embedded images
-            images_to_analyze = extract_images_from_pdf(tmp_file_path)
-            if not images_to_analyze:
-                return "⚠️ No embedded images found in the PDF. Try 'Full page analysis' to analyze the entire content."
-        
-        elif analysis_method == "Full page analysis":
-            # Convert each page to image for comprehensive analysis
-            images_to_analyze = convert_pdf_pages_to_images(tmp_file_path)
-        
-        else:  # Both methods
-            # First try embedded images
-            embedded_images = extract_images_from_pdf(tmp_file_path)
-            # Then add full page analysis
-            page_images = convert_pdf_pages_to_images(tmp_file_path)
-            images_to_analyze = embedded_images + page_images
-        
-        # Clean up temporary file
-        os.unlink(tmp_file_path)
-        
-        if not images_to_analyze:
-            return "⚠️ No visual content found in the PDF document."
-        
-        progress(0.7, desc="Analyzing images with AI...")
-        
-        # Analyze images with Groq
-        analysis_result = analyze_images_with_groq(api_key, images_to_analyze)
-        
-        progress(1.0, desc="Analysis complete!")
-        
-        return analysis_result
-        
-    except Exception as e:
-        return f"❌ Error processing PDF: {str(e)}"
-
-def clear_all_text():
-    """Clear all text analysis fields"""
-    return "", None, "", "", ""
-
-def clear_all_image():
-    """Clear all image analysis fields"""
-    return "", None, "Full page analysis", ""
-
-# Custom CSS for better styling
-css = """
-.gradio-container {
-    max-width: 1400px !important;
-    margin: auto !important;
-}
-.main-header {
-    text-align: center;
-    margin-bottom: 2rem;
-}
-.status-success {
-    color: #28a745 !important;
-}
-.status-error {
-    color: #dc3545 !important;
-}
-.info-box {
-    background-color: #f8f9fa;
-    padding: 1rem;
-    border-radius: 0.5rem;
-    border-left: 4px solid #007bff;
-    margin: 1rem 0;
-}
-.feature-box {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    padding: 1.5rem;
-    border-radius: 1rem;
-    color: white;
-    margin: 1rem 0;
-}
-"""
-
-# Create Gradio interface
-with gr.Blocks(css=css, title="Advanced PDF Analyzer with Groq AI") as demo:
-    # Header
-    gr.HTML("""
-    <div class="main-header">
-        <h1>🚀 Advanced PDF Analyzer with Groq AI</h1>
-        <p>Comprehensive PDF analysis tool - Extract text summaries and analyze images/diagrams using state-of-the-art AI models</p>
-    </div>
-    """)
-    
-    # Feature overview
-    gr.HTML("""
-    <div class="feature-box">
-        <h3>✨ What this tool can do:</h3>
-        <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 1rem; margin-top: 1rem;">
-            <div>
-                <h4>📝 Text Analysis:</h4>
-                <ul>
-                    <li>Extract and summarize text content</li>
-                    <li>Generate comprehensive document summaries</li>
-                    <li>Maintain logical structure and key insights</li>
-                </ul>
-            </div>
-            <div>
-                <h4>🖼️ Visual Analysis:</h4>
-                <ul>
-                    <li>Analyze embedded images and diagrams</li>
-                    <li>Process flowcharts and technical drawings</li>
-                    <li>Extract text from images (OCR)</li>
-                </ul>
-            </div>
-        </div>
-    </div>
-    """)
-    
-    # API Key section
-    gr.HTML("""
-    <div class="info-box">
-        <strong>🔑 How to get your Groq API Key:</strong><br>
-        1. Visit <a href="https://console.groq.com/" target="_blank">console.groq.com</a><br>
-        2. Sign up or log in to your account<br>
-        3. Navigate to API Keys section<br>
-        4. Create a new API key and copy it<br>
-        5. Paste it in the field below
-    </div>
-    """)
-    
-    # Global API key input
-    api_key_input = gr.Textbox(
-        label="🔑 Groq API Key",
-        placeholder="Enter your Groq API key here (used for both text and image analysis)...",
-        type="password",
-        info="Your API key is not stored and only used for this session"
-    )
-    
-    # Tabs for different functionalities
-    with gr.Tabs():
-        # Text Summary Tab
-        with gr.TabItem("📝 Text Summary", elem_id="text-tab"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("## 📄 Text Analysis")
-                    
-                    pdf_file_text = gr.File(
-                        label="Upload PDF Document",
-                        file_types=[".pdf"]
-                    )
-                    
-                    with gr.Row():
-                        summarize_btn = gr.Button("📋 Generate Text Summary", variant="primary", size="lg")
-                        clear_text_btn = gr.Button("🗑️ Clear All", variant="secondary")
-                
-                with gr.Column(scale=2):
-                    gr.Markdown("## 📊 Text Analysis Results")
-                    status_text_output = gr.Textbox(
-                        label="Status",
-                        interactive=False,
-                        show_label=True
-                    )
-                    
-                    with gr.Tabs():
-                        with gr.TabItem("📝 Summary"):
-                            summary_output = gr.Textbox(
-                                label="AI Generated Summary",
-                                lines=15,
-                                interactive=False,
-                                placeholder="Your PDF summary will appear here...",
-                                show_copy_button=True
-                            )
-                        
-                        with gr.TabItem("📄 Extracted Text Preview"):
-                            text_preview_output = gr.Textbox(
-                                label="Extracted Text (First 500 characters)",
-                                lines=10,
-                                interactive=False,
-                                placeholder="Preview of extracted text will appear here...",
-                                show_copy_button=True
-                            )
-        
-        # Image Analysis Tab
-        with gr.TabItem("🖼️ Image Analysis", elem_id="image-tab"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("## 🔍 Visual Analysis")
-                    
-                    pdf_file_image = gr.File(
-                        label="Upload PDF Document",
-                        file_types=[".pdf"]
-                    )
-                    
-                    analysis_method = gr.Radio(
-                        choices=[
-                            "Extract embedded images only",
-                            "Full page analysis", 
-                            "Both (embedded + full pages)"
-                        ],
-                        value="Full page analysis",
-                        label="Analysis Method",
-                        info="Choose how to analyze the PDF content"
-                    )
-                    
-                    with gr.Row():
-                        analyze_images_btn = gr.Button("🔍 Analyze Images", variant="primary", size="lg")
-                        clear_image_btn = gr.Button("🗑️ Clear All", variant="secondary")
-                
-                with gr.Column(scale=2):
-                    gr.Markdown("## 📊 Image Analysis Results")
-                    image_analysis_output = gr.Textbox(
-                        label="Visual Analysis Results",
-                        lines=20,
-                        max_lines=50,
-                        show_copy_button=True,
-                        placeholder="Image analysis results will appear here..."
-                    )
-    
-    # Usage instructions
-    gr.HTML("""
-    <div class="info-box">
-        <strong>📋 Usage Instructions:</strong><br>
-        <h4>For Text Summary:</h4>
-        1. Enter your Groq API key above<br>
-        2. Go to "Text Summary" tab<br>
-        3. Upload a PDF document<br>
-        4. Click "Generate Text Summary"<br>
-        
-        <h4>For Image Analysis:</h4>
-        1. Enter your Groq API key above<br>
-        2. Go to "Image Analysis" tab<br>
-        3. Upload a PDF document<br>
-        4. Choose analysis method<br>
-        5. Click "Analyze Images"<br>
-        
-        <h4>Analysis Methods:</h4>
-        • <strong>Extract embedded images:</strong> Analyzes only images embedded within the PDF<br>
-        • <strong>Full page analysis:</strong> Converts each page to image for comprehensive analysis (recommended)<br>
-        • <strong>Both:</strong> Combines both methods for maximum coverage
-    </div>
-    """)
-    
-    # Model information
-    gr.HTML("""
-    <div style="margin-top: 2rem; padding: 1rem; background-color: #e9ecef; border-radius: 0.5rem;">
-        <strong>🤖 Model Information:</strong><br>
-        • <strong>Text Analysis:</strong> Llama-3.3-70B-Versatile (optimized for summarization)<br>
-        • <strong>Image Analysis:</strong> Llama-3.3-70B-Versatile (with vision capabilities)<br>
-        • <strong>Temperature:</strong> 0.2-0.3 (focused, factual analysis)<br>
-        • <strong>Max Tokens:</strong> 2048 (comprehensive outputs)
-    </div>
-    """)
-    
-    # Event handlers for text summary
-    summarize_btn.click(
-        fn=process_pdf_text_summary,
-        inputs=[api_key_input, pdf_file_text],
-        outputs=[status_text_output, text_preview_output, summary_output],
-        show_progress=True
-    )
-    
-    clear_text_btn.click(
-        fn=clear_all_text,
-        outputs=[api_key_input, pdf_file_text, status_text_output, text_preview_output, summary_output]
-    )
-    
-    # Event handlers for image analysis
-    analyze_images_btn.click(
-        fn=process_pdf_image_analysis,
-        inputs=[api_key_input, pdf_file_image, analysis_method],
-        outputs=[image_analysis_output],
-        show_progress=True
-    )
-    
-    clear_image_btn.click(
-        fn=clear_all_image,
-        outputs=[api_key_input, pdf_file_image, analysis_method, image_analysis_output]
-    )
-
-# Launch the app
-if __name__ == "__main__":
-    print("🚀 Starting Advanced PDF Analyzer App...")
-    print("📋 Make sure you have the required packages installed:")
-    print("   pip install -r requirements.txt")
-    print("\n🔑 Don't forget to get your Groq API key from: https://console.groq.com/")
-    
-    demo.launch(
-        server_name="0.0.0.0",  # Allow external connections
-        server_port=7860,       # Default port
-        share=True,             # Create shareable link
-        debug=True,             # Enable debug mode
-        show_error=True         # Show detailed errors
-    )