Create app.py

app.py

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+import gradio as gr
+import requests
+import base64
+import os
+from pathlib import Path
+import tempfile
+import time
+
+# --- Configuration ---
+# NOTE: In a real production app, use environment variables for keys!
+OPENROUTER_KEY = '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'
+HYPERBOLIC_KEY = 'sk_live_vFjJESXnc96z6qdylQsaGAJUE1p6fgRosF0ULkaZXCoe5uO43H_8lqtCjJ_F79kuQ'
+
+class MediClearBackend:
+    """Handles the API logic to keep the UI code clean."""
+    
+    @staticmethod
+    def encode_image(image_path):
+        with open(image_path, "rb") as image_file:
+            return base64.b64encode(image_file.read()).decode('utf-8')
+
+    @staticmethod
+    def analyze_medical_image(image_path):
+        """Step 1: Extract technical info using OpenRouter (Nvidia)"""
+        try:
+            base64_image = MediClearBackend.encode_image(image_path)
+            url = "https://openrouter.ai/api/v1/chat/completions"
+            headers = {
+                "Authorization": f"Bearer {OPENROUTER_KEY}",
+                "Content-Type": "application/json"
+            }
+            
+            messages = [
+                {
+                    "role": "system",
+                    "content": "You are a highly experienced medical doctor. Extract every piece of information from the image given. Provide detailed observations."
+                },
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "Please extract every piece of information from the image given"},
+                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
+                    ]
+                }
+            ]
+
+            payload = {
+                "model": "nvidia/nemotron-nano-12b-v2-vl:free",
+                "messages": messages
+            }
+
+            response = requests.post(url, headers=headers, json=payload)
+            response.raise_for_status()
+            return response.json()['choices'][0]['message']['content']
+        except Exception as e:
+            raise Exception(f"Image Analysis Error: {str(e)}")
+
+    @staticmethod
+    def summarize_for_patient(technical_text):
+        """Step 2: Convert technical info to patient-friendly text using Hyperbolic"""
+        try:
+            url = "https://api.hyperbolic.xyz/v1/chat/completions"
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {HYPERBOLIC_KEY}"
+            }
+            
+            system_prompt = """You are a senior medical doctor with 50 years of experience. 
+            Your role is to explain medical reports to patients in simple, reassuring, everyday language.
+            Structure:
+            1. Warm greeting.
+            2. Overall assessment.
+            3. Key findings (compared to normal).
+            4. Next steps.
+            5. Reassuring closing.
+            Speak directly to the patient."""
+
+            data = {
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": technical_text}
+                ],
+                "model": "moonshotai/Kimi-K2-Instruct",
+                "max_tokens": 4096,
+                "temperature": 0.1,
+                "top_p": 0.9
+            }
+
+            response = requests.post(url, headers=headers, json=data)
+            response.raise_for_status()
+            return response.json()['choices'][0]['message']['content']
+        except Exception as e:
+            raise Exception(f"Summary Error: {str(e)}")
+
+def process_single_image(image_file):
+    """Process a single medical image and return the analysis"""
+    backend = MediClearBackend()
+    
+    if image_file is None:
+        return "❌ Please upload a medical image first."
+    
+    try:
+        # Create progress updates
+        progress_updates = []
+        
+        # Step 1: Technical analysis
+        progress_updates.append("🩺 Step 1/2: Scanning image for medical details...")
+        technical_data = backend.analyze_medical_image(image_file.name)
+        
+        # Step 2: Patient-friendly summary
+        progress_updates.append("📝 Step 2/2: Generating patient-friendly summary...")
+        final_report = backend.summarize_for_patient(technical_data)
+        
+        # Format the final report
+        filename = Path(image_file.name).name
+        formatted_report = f"""
+{'='*60}
+🩺 MEDICAL ANALYSIS REPORT: {filename}
+{'='*60}
+
+{final_report}
+
+{'='*60}
+✅ Analysis Complete - This tool provides AI-powered insights and is not a substitute for professional medical diagnosis.
+{'='*60}
+"""
+        return formatted_report
+        
+    except Exception as e:
+        return f"❌ Error processing image: {str(e)}"
+
+def process_multiple_images(image_files):
+    """Process multiple medical images and return combined analysis"""
+    backend = MediClearBackend()
+    
+    if not image_files:
+        return "❌ Please upload at least one medical image."
+    
+    full_report = f"🩺 SEHATSCAN - COMPREHENSIVE MEDICAL ANALYSIS\n{'='*70}\n\n"
+    total = len(image_files)
+    
+    for index, image_file in enumerate(image_files, 1):
+        try:
+            filename = Path(image_file.name).name
+            full_report += f"\n📄 IMAGE {index}/{total}: {filename}\n{'-'*50}\n"
+            full_report += "🔍 Analyzing medical image...\n"
+            
+            # Process the image
+            technical_data = backend.analyze_medical_image(image_file.name)
+            patient_summary = backend.summarize_for_patient(technical_data)
+            
+            full_report += f"{patient_summary}\n\n"
+            full_report += "✅ Analysis complete for this image.\n\n"
+            
+        except Exception as e:
+            full_report += f"❌ Failed to process {filename}: {str(e)}\n\n"
+    
+    full_report += f"{'='*70}\n"
+    full_report += "🏁 All analyses complete. Remember: This tool provides AI-powered insights and is not a substitute for professional medical diagnosis.\n"
+    full_report += f"{'='*70}"
+    
+    return full_report
+
+def create_gradio_interface():
+    """Create the Gradio interface"""
+    
+    # Custom CSS for better styling
+    custom_css = """
+    .gradio-container {
+        background: linear-gradient(135deg, #1A1A1A 0%, #2D2D2D 100%);
+    }
+    .dark {
+        background: transparent !important;
+    }
+    .panel {
+        background: #252525 !important;
+        border-radius: 12px !important;
+        border: 1px solid #404040 !important;
+        padding: 20px !important;
+    }
+    .header {
+        background: linear-gradient(135deg, #1A1A1A 0%, #252525 100%) !important;
+        border-radius: 12px !important;
+        padding: 20px !important;
+        margin-bottom: 20px !important;
+        border: 1px solid #404040 !important;
+    }
+    .warning {
+        background: #FF980020 !important;
+        border: 1px solid #FF9800 !important;
+        border-radius: 8px !important;
+        padding: 15px !important;
+        margin: 10px 0 !important;
+    }
+    .success {
+        background: #4CAF5020 !important;
+        border: 1px solid #4CAF50 !important;
+        border-radius: 8px !important;
+        padding: 15px !important;
+        margin: 10px 0 !important;
+    }
+    """
+    
+    with gr.Blocks(
+        title="SehatScan - Koshur AI", 
+        css=custom_css,
+        theme=gr.themes.Soft(
+            primary_hue="yellow",
+            neutral_hue="slate"
+        )
+    ) as demo:
+        
+        # Header Section
+        with gr.Column(elem_classes="header"):
+            gr.Markdown(
+                """
+                # 🩺 SehatScan - Koshur AI
+                ### Professional Medical Image Analysis
+                """
+            )
+        
+        # Main Content
+        with gr.Row():
+            # Left Panel - Image Input
+            with gr.Column(scale=1, min_width=400):
+                with gr.Group(elem_classes="panel"):
+                    gr.Markdown("### 📁 Upload Medical Images")
+                    
+                    # Single image upload
+                    single_image = gr.File(
+                        label="Single Image Analysis",
+                        file_types=[".jpg", ".jpeg", ".png", ".bmp"],
+                        file_count="single",
+                        type="filepath"
+                    )
+                    
+                    # Multiple image upload
+                    multiple_images = gr.File(
+                        label="Batch Image Analysis (Multiple)",
+                        file_types=[".jpg", ".jpeg", ".png", ".bmp"],
+                        file_count="multiple",
+                        type="filepath"
+                    )
+                    
+                    # Analysis buttons
+                    with gr.Row():
+                        analyze_single_btn = gr.Button(
+                            "🚀 Analyze Single Image",
+                            variant="primary",
+                            size="lg"
+                        )
+                        analyze_batch_btn = gr.Button(
+                            "📊 Analyze Multiple Images", 
+                            variant="secondary",
+                            size="lg"
+                        )
+                    
+                    # Clear button
+                    clear_btn = gr.Button("🗑️ Clear All", variant="stop")
+                
+                # Disclaimer
+                with gr.Group(elem_classes="warning"):
+                    gr.Markdown(
+                        """
+                        ⚠️ **Medical Disclaimer**
+                        This tool provides AI-powered insights and is not a substitute for professional medical diagnosis, 
+                        treatment, or medical advice. Always consult with qualified healthcare providers for medical decisions.
+                        """
+                    )
+            
+            # Right Panel - Output
+            with gr.Column(scale=2, min_width=600):
+                with gr.Group(elem_classes="panel"):
+                    gr.Markdown("### 📋 Doctor's Analysis Report")
+                    
+                    output_text = gr.Textbox(
+                        label="Analysis Results",
+                        lines=25,
+                        max_lines=50,
+                        show_copy_button=True,
+                        container=False
+                    )
+                    
+                    # Output actions
+                    with gr.Row():
+                        copy_btn = gr.Button("📋 Copy to Clipboard", size="sm")
+                        export_btn = gr.Button("💾 Export Report", size="sm")
+        
+        # Footer
+        with gr.Column(elem_classes="panel"):
+            with gr.Row():
+                gr.Markdown(
+                    """
+                    **❤️ Built by Koshur AI • An initiative for Kashmir**  
+                    [Visit our website](https://koshur-ai.github.io)
+                    """
+                )
+        
+        # Event handlers for single image analysis
+        analyze_single_btn.click(
+            fn=process_single_image,
+            inputs=[single_image],
+            outputs=[output_text],
+            api_name="analyze_single"
+        )
+        
+        # Event handlers for multiple image analysis
+        analyze_batch_btn.click(
+            fn=process_multiple_images,
+            inputs=[multiple_images],
+            outputs=[output_text],
+            api_name="analyze_batch"
+        )
+        
+        # Clear functionality
+        def clear_all():
+            return None, None, ""
+        
+        clear_btn.click(
+            fn=clear_all,
+            outputs=[single_image, multiple_images, output_text]
+        )
+        
+        # Copy functionality
+        def copy_to_clipboard(text):
+            return gr.update(value=text)
+        
+        copy_btn.click(
+            fn=copy_to_clipboard,
+            inputs=[output_text],
+            outputs=[output_text]
+        )
+        
+        # Export functionality (simulated - in Gradio, users can copy from textbox)
+        def export_report(text):
+            if text:
+                timestamp = time.strftime("%Y%m%d-%H%M%S")
+                filename = f"sehatscan_report_{timestamp}.txt"
+                return f"✅ Report ready! You can copy the text above and save it as '{filename}'"
+            return "❌ No analysis to export."
+        
+        export_btn.click(
+            fn=export_report,
+            inputs=[output_text],
+            outputs=[output_text]
+        )
+        
+        # Examples
+        gr.Markdown("### 🎯 Try it out!")
+        gr.Examples(
+            examples=[
+                # You can add example image paths here for demo purposes
+                # ["examples/xray1.jpg"],
+                # ["examples/mri1.jpg"],
+            ],
+            inputs=[single_image],
+            outputs=[output_text],
+            fn=process_single_image,
+            cache_examples=False,
+            label="Click on any example below to try:"
+        )
+    
+    return demo
+
+# Create and launch the interface
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    
+    # For Hugging Face Spaces, use this launch method
+    demo.launch(
+        server_name="0.0.0.0" if os.getenv("SPACE_ID") else None,
+        share=False,
+        show_error=True,
+        debug=False
+    )