Update app.py

app.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
-Gemini Vision Pro Medical Image Analysis - Gradio Interface
-Fixed version with HuggingFace Secrets support and better error handling
+Gemini Vision Pro Medical Image Analysis - Secure Gradio Interface
+API key is loaded only from environment variables or .env file (never exposed to users)
 """
 
 import gradio as gr
@@ -10,127 +10,48 @@ from PIL import Image
 import json
 import time
 import os
-from typing import Dict, List, Optional, Tuple
-import base64
-import io
+from typing import Dict, Tuple
+from dotenv import load_dotenv  # pip install python-dotenv
 import warnings
+
 warnings.filterwarnings("ignore")
 
-# Global configuration
-GEMINI_MODEL = None
-API_KEY = None
+# Load environment variables from .env if present
+load_dotenv()
 
-def get_api_key_from_secrets():
-    """Try to get API key from HuggingFace secrets or environment variables"""
-    # Try multiple possible secret names
-    possible_keys = [
-        'GEMINI_API_KEY'
-    ]
-    
-    for key_name in possible_keys:
-        # First try HuggingFace secrets (if running on HF Spaces)
-        try:
-            from huggingface_hub import HfFolder
-            token = HfFolder.get_token()
-            if token:
-                # This is a simplified approach - in HF Spaces, secrets are usually available as env vars
-                api_key = os.getenv(key_name)
-                if api_key:
-                    return api_key
-        except:
-            pass
-        
-        # Try regular environment variables
-        api_key = os.getenv(key_name)
-        if api_key:
-            return api_key
-    
-    return None
+# Global model instance
+GEMINI_MODEL = None
 
-def setup_gemini(api_key: str = None):
-    """Setup Gemini Vision Pro with API key"""
-    global GEMINI_MODEL, API_KEY
-    
-    # Try to get API key from various sources
-    if api_key and api_key.strip():
-        API_KEY = api_key.strip()
-    else:
-        API_KEY = get_api_key_from_secrets()
-    
-    if not API_KEY:
-        return False, "❌ No API key found. Please:\n1. Set GOOGLE_API_KEY in HuggingFace Spaces secrets"
-    
+def setup_gemini():
+    """Initialize Gemini Vision Pro using API key from environment (never exposed to users)"""
+    global GEMINI_MODEL
+    api_key = os.getenv('GOOGLE_API_KEY') or os.getenv('GEMINI_API_KEY')
+    if not api_key:
+        return False, "❌ No API key found. Please set GOOGLE_API_KEY or GEMINI_API_KEY in your environment or .env file."
     try:
-        # Configure the API
-        genai.configure(api_key=API_KEY)
-        
-        
-        GEMINI_MODEL = genai.GenerativeModel(
-            'gemini-1.5-pro'
-        )
-        
-        # Test the connection with a simple query
-        test_response = GEMINI_MODEL.generate_content(
-            "Respond with just 'OK' to confirm you can analyze medical images.",
-            generation_config=genai.types.GenerationConfig(
-                temperature=0.1,
-                max_output_tokens=10
-            )
-        )
-        
+        genai.configure(api_key=api_key)
+        GEMINI_MODEL = genai.GenerativeModel('gemini-1.5-pro')
+        # Test connection
+        test_response = GEMINI_MODEL.generate_content("Hello, can you help with medical image analysis?")
         if test_response and test_response.text:
             return True, "✅ Gemini Vision Pro connected successfully!"
         else:
-            return False, "❌ Failed to get response from Gemini API"
-            
+            return False, "❌ Failed to connect to Gemini API"
     except Exception as e:
-        error_msg = f"❌ Gemini setup failed: {str(e)}"
-        
-        # Provide specific guidance based on error type
-        if "429" in str(e) or "quota" in str(e).lower():
-            error_msg += "\n\n💡 **Quota Exceeded Solutions:**\n"
-            error_msg += "• Check your Google Cloud Console billing\n"
-            error_msg += "• Verify your API key has sufficient quota\n"
-            error_msg += "• Try again in a few minutes (rate limiting)\n"
-            error_msg += "• Consider upgrading your Google AI plan\n"
-            error_msg += "🔗 Check usage: https://console.cloud.google.com/"
-            
-        elif "403" in str(e) or "permission" in str(e).lower():
-            error_msg += "\n\n💡 **Permission Error Solutions:**\n"
-            error_msg += "• Ensure Gemini API is enabled in your project\n"
-            error_msg += "• Check if your API key has proper permissions\n"
-            error_msg += "• Verify your Google Cloud project settings"
-            
-        elif "401" in str(e) or "unauthorized" in str(e).lower():
-            error_msg += "\n\n💡 **Authentication Error:**\n"
-            error_msg += "• Double-check your API key is correct\n"
-            error_msg += "• Ensure no extra spaces in the API key\n"
-            error_msg += "• Generate a new API key if needed"
-            
-        elif "safety" in str(e).lower() or "blocked" in str(e).lower():
-            error_msg += "\n\n💡 **Content Safety Issue:**\n"
-            error_msg += "• The image may have triggered safety filters\n"
-            error_msg += "• Try a different medical image\n"
-            error_msg += "• Ensure the image is clearly medical in nature"
-        
-        return False, error_msg
+        return False, f"❌ Gemini setup failed: {str(e)}"
 
 def create_medical_prompt(clinical_data: Dict, analysis_type: str, focus_areas: str) -> str:
     """Create optimized medical analysis prompt for Gemini"""
-    
-    base_prompt = """You are an expert medical AI assistant specializing in medical image analysis. You have extensive training across radiology, pathology, dermatology, ophthalmology, and clinical medicine.
-
-**ANALYSIS INSTRUCTIONS:**
-Analyze this medical image systematically and professionally:
-- Use clear medical terminology with explanations for complex terms
-- Structure your response with clear sections and headers  
-- Be thorough but concise
-- Always mention limitations and emphasize the need for professional medical consultation
-- Focus on observable findings rather than definitive diagnoses
-- This is for educational and research purposes only
-"""
-    
-    # Add clinical context if provided
+    base_prompt = (
+        "You are an expert medical AI assistant specializing in medical image analysis. "
+        "You have extensive training across radiology, pathology, dermatology, ophthalmology, and clinical medicine.\n"
+        "**ANALYSIS INSTRUCTIONS:**\n"
+        "- Use clear medical terminology with explanations for complex terms\n"
+        "- Structure your response with clear sections and headers\n"
+        "- Be thorough but concise\n"
+        "- Always mention limitations and emphasize the need for professional medical consultation\n"
+        "- Focus on observable findings rather than definitive diagnoses\n"
+    )
     clinical_context = ""
     if clinical_data and any(v.strip() for v in clinical_data.values() if v):
         clinical_context = "\n**CLINICAL CONTEXT:**\n"
@@ -140,87 +61,76 @@ Analyze this medical image systematically and professionally:
         if clinical_data.get("symptoms"): context_items.append(f"Presenting Symptoms: {clinical_data['symptoms']}")
         if clinical_data.get("history"): context_items.append(f"Medical History: {clinical_data['history']}")
         if clinical_data.get("medications"): context_items.append(f"Current Medications: {clinical_data['medications']}")
-        
         clinical_context += "\n".join(f"• {item}" for item in context_items) + "\n"
-    
-    # Analysis type specific instructions
+
     analysis_instructions = {
         "Comprehensive": """
 **PROVIDE COMPREHENSIVE ANALYSIS WITH THESE SECTIONS:**
-
 ## 1. IMAGE ASSESSMENT
 - Image type, quality, and technical adequacy
 - Anatomical structures and regions visible
 - Any artifacts or limitations
-
 ## 2. CLINICAL FINDINGS
 - Normal anatomical structures observed
 - Abnormal findings or variations from normal
 - Specific measurements or quantitative observations if applicable
-
 ## 3. CLINICAL INTERPRETATION
 - Significance of the findings
 - Differential diagnostic considerations
 - Correlation with provided clinical history
-
 ## 4. RECOMMENDATIONS
 - Suggested next steps or additional imaging
 - Clinical correlation recommendations
 - Follow-up suggestions
-
 ## 5. LIMITATIONS & DISCLAIMERS
 - What cannot be determined from this image alone
 - Need for clinical correlation and professional evaluation
 """,
         "Quick Assessment": """
 **PROVIDE FOCUSED QUICK ASSESSMENT:**
-
 ## KEY FINDINGS
 - Most significant observations
 - Normal vs abnormal structures
-
 ## CLINICAL IMPRESSION
 - Primary considerations based on image
 - Any urgent findings that require immediate attention
-
 ## IMMEDIATE RECOMMENDATIONS
 - Essential next steps
 - Urgency level assessment
-
 ## LIMITATIONS
 - Important caveats about this assessment
 """,
         "Educational": """
 **PROVIDE EDUCATIONAL ANALYSIS:**
-
 ## LEARNING OBJECTIVES
 - Key educational points from this case
 - Important anatomical or pathological concepts
-
 ## NORMAL vs ABNORMAL
 - Clear explanation of what's normal in this image
 - Detailed description of any abnormal findings
-
 ## CLINICAL CORRELATION
 - How image findings relate to symptoms/history
 - Real-world clinical significance
-
 ## TEACHING PEARLS
 - Important concepts this case demonstrates
 - Common pitfalls or considerations
 """
     }
-    
     focus_instruction = ""
     if focus_areas and focus_areas.strip():
         focus_instruction = f"\n**SPECIAL FOCUS AREAS**: Pay particular attention to: {focus_areas}\n"
-    
-    disclaimer = """
-**IMPORTANT MEDICAL DISCLAIMER**: 
-This AI-powered analysis is for educational and research purposes only. It should never replace professional medical diagnosis, treatment, or consultation with qualified healthcare providers. Always seek professional medical advice for any health concerns or medical decisions.
-"""
-    
-    return base_prompt + clinical_context + analysis_instructions.get(analysis_type, analysis_instructions["Comprehensive"]) + focus_instruction + disclaimer
+    disclaimer = (
+        "**IMPORTANT MEDICAL DISCLAIMER**: This AI-powered analysis is for educational and research purposes only. "
+        "It should never replace professional medical diagnosis, treatment, or consultation with qualified healthcare providers. "
+        "Always seek professional medical advice for any health concerns or medical decisions."
+    )
+    return (
+        base_prompt
+        + clinical_context
+        + analysis_instructions.get(analysis_type, analysis_instructions["Comprehensive"])
+        + focus_instruction
+        + disclaimer
+    )
 
 def analyze_medical_image_gemini(
     image: Image.Image,
@@ -231,397 +141,86 @@ def analyze_medical_image_gemini(
     medications: str,
     analysis_type: str,
     focus_areas: str,
-    api_key: str,
     progress=gr.Progress()
 ) -> Tuple[str, str, str]:
     """Main analysis function using Gemini Vision Pro"""
-    
     if image is None:
         return "❌ Please upload an image first.", "", "❌ No image provided"
-    
-    # Setup Gemini if needed
     progress(0.1, desc="Connecting to Gemini...")
-    success, status = setup_gemini(api_key)
+    success, status = setup_gemini()
     if not success:
         return status, "", status
-    
     try:
         progress(0.3, desc="Preparing analysis...")
-        
-        # Prepare clinical data
         clinical_data = {
-            "age": age.strip() if age else "",
-            "gender": gender if gender else "",
-            "symptoms": symptoms.strip() if symptoms else "",
-            "history": history.strip() if history else "",
-            "medications": medications.strip() if medications else ""
+            "age": age.strip(),
+            "gender": gender,
+            "symptoms": symptoms.strip(),
+            "history": history.strip(),
+            "medications": medications.strip()
         }
-        
-        # Create prompt
         prompt = create_medical_prompt(clinical_data, analysis_type, focus_areas)
-        
         progress(0.5, desc="Analyzing image with Gemini...")
-        
-        # Configure generation parameters for better results
-        generation_config = genai.types.GenerationConfig(
-            temperature=0.3,  # Lower temperature for more consistent medical analysis
-            max_output_tokens=2048,  # Sufficient for detailed analysis
-            top_p=0.8,
-            top_k=40
-        )
-        
-        # Generate analysis using Gemini Vision Pro
-        response = GEMINI_MODEL.generate_content(
-            [prompt, image],
-            generation_config=generation_config
-        )
-        
+        response = GEMINI_MODEL.generate_content([prompt, image])
         if not response or not response.text:
-            return "❌ No response received from Gemini API. The image may have been blocked by safety filters or there was a processing error.", "", "❌ Analysis failed"
-        
+            return "❌ No response received from Gemini API", "", "❌ Analysis failed"
         progress(0.9, desc="Preparing results...")
-        
-        # Create download content
         report_data = {
             "timestamp": time.strftime("%Y-%m-%d %H:%M:%S UTC"),
             "model": "Google Gemini-1.5-Pro Vision",
             "analysis_type": analysis_type,
             "clinical_data": clinical_data,
-            "focus_areas": focus_areas if focus_areas else "None specified",
+            "focus_areas": focus_areas,
             "analysis": response.text
         }
-        
         download_content = json.dumps(report_data, indent=2)
-        
         progress(1.0, desc="Analysis complete!")
-        
         return response.text, download_content, "✅ Analysis completed successfully"
-        
     except Exception as e:
         error_msg = f"❌ Analysis failed: {str(e)}"
-        
-        # Provide specific error guidance
-        if "429" in str(e) or "quota" in str(e).lower():
-            error_msg += "\n\n💡 **Quota Issue:** You've exceeded your API limits. Please:\n"
-            error_msg += "• Check your Google Cloud Console billing\n"
-            error_msg += "• Wait a few minutes and try again\n"
-            error_msg += "• Consider upgrading your plan"
-            
-        elif "safety" in str(e).lower() or "blocked" in str(e).lower():
-            error_msg += "\n\n💡 **Safety Filter:** The image was blocked. Try:\n"
-            error_msg += "• A different medical image\n"
-            error_msg += "• Ensuring the image is clearly medical\n"
-            error_msg += "• Removing any potentially sensitive content"
-            
-        elif "401" in str(e) or "403" in str(e):
-            error_msg += "\n\n💡 **Authentication Error:** Check your API key"
-        
+        if "API_KEY" in str(e):
+            error_msg += "\n\n💡 Tip: Make sure your Google API key is valid and has access to Gemini API"
+        elif "quota" in str(e).lower():
+            error_msg += "\n\n💡 Tip: You may have exceeded your API quota. Check your Google Cloud Console"
+        elif "safety" in str(e).lower():
+            error_msg += "\n\n💡 Tip: The image may have been blocked by safety filters. Try a different medical image"
         return error_msg, "", error_msg
 
 def create_interface():
     """Create the Gradio interface for Gemini Vision Pro"""
-    
-    # Custom CSS for medical theme
     css = """
-    .gradio-container {
-        max-width: 1400px !important;
-    }
-    .medical-header {
-        text-align: center;
-        color: #1a73e8;
-        margin-bottom: 20px;
-    }
-    .api-section {
-        background-color: #e8f0fe;
-        padding: 15px;
-        border-radius: 8px;
-        margin: 10px 0;
-        border-left: 4px solid #1a73e8;
-    }
-    .status-success {
-        color: #137333;
-        font-weight: 500;
-    }
-    .status-error {
-        color: #d93025;
-        font-weight: 500;
-    }
-    .quota-info {
-        background-color: #fff3cd;
-        padding: 10px;
-        border-radius: 5px;
-        margin: 10px 0;
-        border-left: 3px solid #ffc107;
-    }
+    .gradio-container { max-width: 1400px !important; }
+    .medical-header { text-align: center; color: #1a73e8; margin-bottom: 20px; }
+    .status-success { color: #137333; font-weight: 500; }
+    .status-error { color: #d93025; font-weight: 500; }
     """
-    
     with gr.Blocks(css=css, theme=gr.themes.Soft(), title="Gemini Medical AI") as interface:
-        
-        # Header
-        gr.HTML("""
-        <div class="medical-header">
-            <h1>🏥 Medical Image AI Analyzer</h1>
-            <h2>🤖 Powered by Google Gemini Vision Pro</h2>
-            <p><em>Fast, efficient medical image analysis using Google's latest AI</em></p>
-        </div>
-        """)
-        
-        # Check for existing API key on startup
-        initial_api_key = get_api_key_from_secrets()
-        
-        # API Configuration Section
-        with gr.Accordion("🔑 API Configuration", open=not bool(initial_api_key)):
-            if initial_api_key:
-                gr.Markdown("""
-                ### ✅ API Key Found in Secrets
-                An API key was found in the environment/secrets.
-                """)
-            else:
-                gr.Markdown("""
-                ### 🔐 Google Gemini API Setup Required
-                You need a Google API key to use Gemini Vision Pro. 
-                
-                **For HuggingFace Spaces:** Add your API key as a secret named `GOOGLE_API_KEY`
-                **For local use:** Set the `GOOGLE_API_KEY` environment variable or enter it below
-                
-                🔗 Get your API key from: [Google AI Studio](https://makersuite.google.com/app/apikey)
-                """)
-            
-            gr.HTML("""
-            <div class="quota-info">
-                <strong>💰 Important:</strong> Make sure your Google API key has sufficient quota and billing is set up.
-                The 429 error indicates quota limits have been exceeded.
-            </div>
-            """)
-            
-            api_key_input = gr.Textbox(
-                label="Google API Key (Optional Override)",
-                type="password",
-                placeholder="Enter your Google API key here to override secrets...",
-                info="Leave empty to use API key from secrets/environment",
-                value=""
-            )
-            
-            status_display = gr.Textbox(
-                label="Connection Status",
-                value="⏳ Testing connection..." if initial_api_key else "⏳ Enter API key to connect",
-                interactive=False
-            )
-        
+        gr.HTML("<h1 class='medical-header'>Gemini Vision Pro Medical Image Analysis</h1><p>*Fast, efficient medical image analysis using Google's latest AI*</p>")
         with gr.Row():
-            # Left column - Inputs
-            with gr.Column(scale=1):
-                gr.Markdown("## 📤 Upload Medical Image")
-                
-                image_input = gr.Image(
-                    type="pil",
-                    label="Medical Image",
-                    height=300,
-                    sources=["upload", "clipboard", "webcam"]
-                )
-                
-                gr.Markdown("*Supported: X-rays, CT, MRI, photographs, microscopy, dermatology images, etc.*")
-                
-                gr.Markdown("## 📋 Clinical Information")
-                
-                with gr.Group():
-                    with gr.Row():
-                        age_input = gr.Textbox(
-                            label="Patient Age",
-                            placeholder="e.g., 45 years",
-                            max_lines=1
-                        )
-                        gender_input = gr.Dropdown(
-                            choices=["", "Male", "Female", "Other"],
-                            label="Gender",
-                            value=""
-                        )
-                    
-                    symptoms_input = gr.Textbox(
-                        label="Chief Complaint / Symptoms",
-                        placeholder="e.g., Chest pain, shortness of breath for 3 days",
-                        lines=2
-                    )
-                    
-                    history_input = gr.Textbox(
-                        label="Medical History",
-                        placeholder="e.g., Hypertension, diabetes, previous surgeries",
-                        lines=2
-                    )
-                    
-                    medications_input = gr.Textbox(
-                        label="Current Medications",
-                        placeholder="e.g., Metformin, Lisinopril, Aspirin",
-                        lines=2
-                    )
-                
-                gr.Markdown("## ⚙️ Analysis Settings")
-                
+            with gr.Column():
+                image_input = gr.Image(type="pil", label="Upload a medical image")
+                age = gr.Textbox(label="Patient Age", placeholder="e.g. 47")
+                gender = gr.Dropdown(choices=["Male", "Female", "Other", "Unknown"], label="Gender", value="Unknown")
+                symptoms = gr.Textbox(label="Symptoms", placeholder="Describe presenting symptoms")
+                history = gr.Textbox(label="Medical History", placeholder="Relevant medical history")
+                medications = gr.Textbox(label="Current Medications", placeholder="List current medications")
                 analysis_type = gr.Radio(
                     choices=["Comprehensive", "Quick Assessment", "Educational"],
-                    label="Analysis Type",
-                    value="Comprehensive",
-                    info="Choose the depth and focus of analysis"
-                )
-                
-                focus_areas = gr.Textbox(
-                    label="Focus Areas (Optional)",
-                    placeholder="e.g., cardiac silhouette, lung fields, bone density",
-                    info="Specific areas to emphasize in analysis"
+                    value="Comprehensive", label="Analysis Type"
                 )
-                
-                analyze_btn = gr.Button(
-                    "🔬 Analyze with Gemini",
-                    variant="primary",
-                    size="lg"
-                )
-            
-            # Right column - Results
-            with gr.Column(scale=1):
-                gr.Markdown("## 🤖 AI Analysis Results")
-                
-                analysis_output = gr.Textbox(
-                    label="Medical Analysis",
-                    lines=25,
-                    max_lines=35,
-                    show_copy_button=True,
-                    placeholder="Analysis results will appear here after processing..."
-                )
-                
-                download_file = gr.File(
-                    label="📥 Download Analysis Report",
-                    visible=False
-                )
-                
-                # Hidden component to store download content
-                download_content = gr.Textbox(visible=False)
-        
-        # Information sections
-        with gr.Accordion("🔧 Troubleshooting Common Issues", open=False):
-            gr.Markdown("""
-            ### 🚨 **Error 429 - Quota Exceeded:**
-            - **Check billing:** Ensure your Google Cloud project has billing enabled
-            - **API limits:** You may have hit free tier limits - consider upgrading
-            - **Rate limiting:** Wait a few minutes and try again
-            - **Multiple keys:** Try generating a new API key
-            
-            ### 🔐 **For HuggingFace Spaces Users:**
-            1. Go to your Space settings
-            2. Add a new secret: `GOOGLE_API_KEY` 
-            3. Paste your Google API key as the value
-            4. Restart your Space
-            
-            ### 📊 **Monitor Usage:**
-            - [Google AI Studio Usage](https://makersuite.google.com/)
-            - [Google Cloud Console](https://console.cloud.google.com/)
-            
-            ### 🔄 **If Still Having Issues:**
-            - Try a different API key
-            - Check if Gemini API is enabled in your project
-            - Verify your Google Cloud project settings
-            """)
-        
-        with gr.Accordion("💡 About Gemini Vision Pro", open=False):
-            gr.Markdown("""
-            ### 🚀 **Advantages of Gemini Vision Pro:**
-            - **Fast Processing**: No local model loading - results in seconds
-            - **Low Resource Usage**: Runs via API calls, minimal local computing needed
-            - **High Quality**: Google's latest multimodal AI model
-            - **Always Updated**: Access to the latest model improvements
-            - **Reliable**: Enterprise-grade infrastructure
-            
-            ### 🔍 **Supported Medical Images:**
-            - **Radiology**: X-rays, CT scans, MRI images, Ultrasound
-            - **Pathology**: Histological slides, Cytology specimens  
-            - **Dermatology**: Skin lesions, Rashes, Clinical photos
-            - **Ophthalmology**: Fundus photos, OCT images
-            - **Clinical Photography**: Wound assessment, Physical findings
-            - **Microscopy**: Cellular and tissue analysis
-            
-            ### 💰 **Cost Information:**
-            - Gemini Vision Pro uses pay-per-use pricing
-            - Typically very affordable for individual analyses
-            - Check [Google AI Pricing](https://ai.google.dev/pricing) for current rates
-            """)
-        
-        # Footer
-        gr.HTML("""
-        <div style="text-align: center; margin-top: 20px; padding: 15px; background-color: #fff3cd; border-radius: 8px;">
-            <strong>⚠️ Medical Disclaimer:</strong> This AI tool is for educational and research purposes only. 
-            It should never replace professional medical diagnosis or treatment. 
-            Always consult qualified healthcare providers for medical decisions.
-        </div>
-        """)
-        
-        # Event handlers
-        def create_download_file(content):
-            if content:
-                filename = f"gemini_medical_analysis_{int(time.time())}.json"
-                with open(filename, "w") as f:
-                    f.write(content)
-                return gr.File(value=filename, visible=True)
-            return gr.File(visible=False)
-        
-        def test_api_connection(api_key_override):
-            # Use override if provided, otherwise use secrets
-            test_key = api_key_override if api_key_override.strip() else None
-            success, status = setup_gemini(test_key)
-            return status
-        
-        # Test connection on startup if we have an API key
-        if initial_api_key:
-            def initial_connection_test():
-                return test_api_connection("")
-            
-            interface.load(
-                fn=initial_connection_test,
-                outputs=[status_display]
-            )
-        
-        # API key testing
-        api_key_input.change(
-            fn=test_api_connection,
-            inputs=[api_key_input],
-            outputs=[status_display]
-        )
-        
-        # Main analysis
+                focus_areas = gr.Textbox(label="Special Focus Areas", placeholder="e.g. lung nodules, bone lesions")
+                analyze_btn = gr.Button("Analyze Image", variant="primary")
+            with gr.Column():
+                status = gr.Markdown(value="", elem_classes="status-success")
+                output = gr.Markdown(label="AI Medical Analysis")
+                download = gr.DownloadButton(label="Download Report", file_name="analysis_report.json")
         analyze_btn.click(
-            fn=analyze_medical_image_gemini,
-            inputs=[
-                image_input, age_input, gender_input, symptoms_input,
-                history_input, medications_input, analysis_type, focus_areas, api_key_input
-            ],
-            outputs=[analysis_output, download_content, status_display]
-        ).then(
-            fn=create_download_file,
-            inputs=[download_content],
-            outputs=[download_file]
+            analyze_medical_image_gemini,
+            inputs=[image_input, age, gender, symptoms, history, medications, analysis_type, focus_areas],
+            outputs=[output, download, status]
         )
-    
     return interface
 
 if __name__ == "__main__":
-    print("🏥 Initializing Gemini Medical AI Analyzer...")
-    print("🔍 Checking for API keys in secrets/environment...")
-    
-    # Check for API key
-    api_key_found = get_api_key_from_secrets()
-    if api_key_found:
-        print("✅ API key found in environment/secrets")
-    else:
-        print("⚠️  No API key found - user will need to provide one")
-    
-    print("🚀 No local model loading required - using Google Gemini Vision Pro API")
-    
-    # Create and launch interface
-    interface = create_interface()
-    
-    # Launch with optimized settings
-    interface.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_error=True,
-        quiet=False
-    )
+    demo = create_interface()
+    demo.launch()