Update app.py

app.py

@@ -1,226 +1,299 @@
-#!/usr/bin/env python3
-"""
-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
 import google.generativeai as genai
 from PIL import Image
 import json
-import time
+from typing import List, Dict, Any
 import os
-from typing import Dict, Tuple
-from dotenv import load_dotenv  # pip install python-dotenv
-import warnings
-
-warnings.filterwarnings("ignore")
-
-# Load environment variables from .env if present
-load_dotenv()
-
-# Global model instance
-GEMINI_MODEL = None
-
-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:
-        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!"
+from datetime import datetime
+
+# Configure Gemini API
+# You'll need to set this as an environment variable in Hugging Face Spaces
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+
+class MedicalAssistant:
+    def __init__(self):
+        if not GOOGLE_API_KEY:
+            raise ValueError("Please set GOOGLE_API_KEY environment variable")
+        
+        genai.configure(api_key=GOOGLE_API_KEY)
+        
+        # Use Gemini Pro Vision for multimodal capabilities
+        self.model = genai.GenerativeModel('gemini-1.5-pro-latest')
+        
+        # Medical analysis prompt template
+        self.medical_prompt_template = """
+You are an expert medical AI assistant with comprehensive training in medical imaging, diagnostics, and clinical decision support. Your analysis should be thorough, evidence-based, and clinically relevant.
+
+**IMPORTANT DISCLAIMERS:**
+- This analysis is for educational and assistive purposes only
+- It does not replace professional medical consultation
+- All findings should be verified by qualified healthcare providers
+- Emergency cases require immediate medical attention
+
+**PATIENT INFORMATION:**
+{patient_info}
+
+**CHIEF COMPLAINTS:**
+{complaints}
+
+**MEDICAL IMAGES PROVIDED:**
+{image_count} image(s) uploaded for analysis
+
+**TASK:**
+Please provide a comprehensive medical analysis including:
+
+1. **IMAGE ANALYSIS** (if images provided):
+   - Systematic description of visible findings
+   - Anatomical structures identified
+   - Any abnormalities, lesions, or pathological changes
+   - Image quality and limitations assessment
+
+2. **CLINICAL CORRELATION:**
+   - How imaging findings relate to patient symptoms
+   - Differential diagnoses based on presentation
+   - Risk factors and red flags identified
+
+3. **DIFFERENTIAL DIAGNOSES:**
+   - List 3-5 most likely diagnoses with reasoning
+   - Include probability assessment if possible
+   - Consider both common and critical conditions
+
+4. **RECOMMENDED INVESTIGATIONS:**
+   - Additional imaging studies if needed
+   - Laboratory tests to consider
+   - Specialist consultations advised
+
+5. **IMMEDIATE CONCERNS:**
+   - Any findings requiring urgent attention
+   - Red flags or emergency indicators
+
+6. **MANAGEMENT SUGGESTIONS:**
+   - Initial treatment considerations
+   - Follow-up recommendations
+   - Patient education points
+
+7. **LIMITATIONS & CONSIDERATIONS:**
+   - Limitations of the current analysis
+   - Information gaps that need addressing
+   - Quality of provided data
+
+Please structure your response clearly with appropriate medical terminology while ensuring it's understandable. Use evidence-based medicine principles and cite relevant clinical guidelines where applicable.
+
+Remember to maintain a professional, empathetic tone and emphasize the importance of professional medical consultation for definitive diagnosis and treatment.
+"""
+
+    def format_patient_info(self, age, gender, medical_history, current_medications, allergies, vital_signs):
+        """Format patient information for the prompt"""
+        info = []
+        if age:
+            info.append(f"Age: {age}")
+        if gender:
+            info.append(f"Gender: {gender}")
+        if medical_history:
+            info.append(f"Medical History: {medical_history}")
+        if current_medications:
+            info.append(f"Current Medications: {current_medications}")
+        if allergies:
+            info.append(f"Allergies: {allergies}")
+        if vital_signs:
+            info.append(f"Vital Signs: {vital_signs}")
+        
+        return "\n".join(info) if info else "No patient information provided"
+
+    def analyze_medical_case(self, images, age, gender, medical_history, 
+                           current_medications, allergies, vital_signs, complaints):
+        """Analyze medical case with Gemini"""
+        try:
+            # Format patient information
+            patient_info = self.format_patient_info(
+                age, gender, medical_history, current_medications, 
+                allergies, vital_signs
+            )
+            
+            # Prepare the prompt
+            prompt = self.medical_prompt_template.format(
+                patient_info=patient_info,
+                complaints=complaints if complaints else "No specific complaints provided",
+                image_count=len(images) if images else 0
+            )
+            
+            # Prepare content for Gemini
+            content = [prompt]
+            
+            # Add images if provided
+            if images:
+                for img_path in images:
+                    try:
+                        img = Image.open(img_path)
+                        content.append(img)
+                    except Exception as e:
+                        return f"Error loading image {img_path}: {str(e)}"
+            
+            # Generate response
+            response = self.model.generate_content(content)
+            
+            # Add timestamp and disclaimer
+            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            output = f"**Analysis Generated on: {timestamp}**\n\n"
+            output += "⚠️ **MEDICAL DISCLAIMER**: This is an AI-generated analysis for educational purposes only. "
+            output += "It is not a substitute for professional medical advice, diagnosis, or treatment. "
+            output += "Always seek the advice of qualified healthcare providers.\n\n"
+            output += "---\n\n"
+            output += response.text
+            
+            return output
+            
+        except Exception as e:
+            return f"Error during analysis: {str(e)}\n\nPlease check your API key and inputs."
+
+# Initialize the assistant
+assistant = MedicalAssistant()
+
+# Create Gradio interface
+def process_medical_case(images, age, gender, medical_history, 
+                        current_medications, allergies, vital_signs, complaints):
+    """Process medical case through Gradio interface"""
+    
+    # Validate inputs
+    if not complaints and not images:
+        return "Please provide either medical images or patient complaints for analysis."
+    
+    # Get image paths if images uploaded
+    image_paths = []
+    if images:
+        if isinstance(images, list):
+            image_paths = [img.name if hasattr(img, 'name') else img for img in images]
         else:
-            return False, "❌ Failed to connect to Gemini API"
-    except Exception as e:
-        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.\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"
+            image_paths = [images.name if hasattr(images, 'name') else images]
+    
+    # Run analysis
+    result = assistant.analyze_medical_case(
+        image_paths, age, gender, medical_history,
+        current_medications, allergies, vital_signs, complaints
     )
-    clinical_context = ""
-    if clinical_data and any(v.strip() for v in clinical_data.values() if v):
-        clinical_context = "\n**CLINICAL CONTEXT:**\n"
-        context_items = []
-        if clinical_data.get("age"): context_items.append(f"Patient Age: {clinical_data['age']}")
-        if clinical_data.get("gender"): context_items.append(f"Gender: {clinical_data['gender']}")
-        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_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 result
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🏥 Medical AI Assistant powered by Gemini
+        
+        This AI assistant analyzes medical images and patient data to provide clinical insights.
+        Upload medical images and enter patient information for comprehensive analysis.
+        
+        **Note**: Ensure you have set the GOOGLE_API_KEY environment variable in your Hugging Face Space settings.
+        """
     )
-    return (
-        base_prompt
-        + clinical_context
-        + analysis_instructions.get(analysis_type, analysis_instructions["Comprehensive"])
-        + focus_instruction
-        + disclaimer
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📋 Patient Information")
+            age = gr.Number(label="Age", precision=0)
+            gender = gr.Dropdown(
+                label="Gender", 
+                choices=["Male", "Female", "Other", "Prefer not to say"],
+                value="Prefer not to say"
+            )
+            medical_history = gr.Textbox(
+                label="Medical History",
+                placeholder="Previous diagnoses, surgeries, chronic conditions...",
+                lines=3
+            )
+            current_medications = gr.Textbox(
+                label="Current Medications",
+                placeholder="List all current medications and dosages...",
+                lines=2
+            )
+            allergies = gr.Textbox(
+                label="Allergies",
+                placeholder="Drug allergies, food allergies, etc...",
+                lines=2
+            )
+            vital_signs = gr.Textbox(
+                label="Vital Signs",
+                placeholder="BP: 120/80, HR: 72, Temp: 98.6°F, etc...",
+                lines=2
+            )
+            
+        with gr.Column(scale=1):
+            gr.Markdown("### 🏥 Clinical Data")
+            complaints = gr.Textbox(
+                label="Chief Complaints & Symptoms",
+                placeholder="Describe main symptoms, duration, severity, associated factors...",
+                lines=6
+            )
+            images = gr.File(
+                label="Medical Images",
+                file_count="multiple",
+                file_types=["image"],
+                type="filepath"
+            )
+            gr.Markdown(
+                """
+                **Supported formats**: X-rays, CT scans, MRI, ultrasound, clinical photos
+                
+                **Privacy Note**: Ensure all images are properly anonymized
+                """
+            )
+    
+    submit_btn = gr.Button("🔬 Analyze Case", variant="primary", size="lg")
+    
+    output = gr.Markdown(label="Analysis Results")
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            [
+                None,  # images
+                45,    # age
+                "Male", # gender
+                "Hypertension for 5 years, Type 2 diabetes", # medical history
+                "Metformin 500mg BD, Amlodipine 5mg OD", # medications
+                "None known", # allergies
+                "BP: 140/90, HR: 78, RR: 16", # vital signs
+                "Chest pain for 2 days, radiating to left arm, associated with shortness of breath" # complaints
+            ],
+            [
+                None,
+                28,
+                "Female",
+                "Migraine headaches",
+                "None",
+                "Penicillin - rash",
+                "BP: 110/70, HR: 68",
+                "Severe headache, photophobia, nausea for 6 hours"
+            ]
+        ],
+        inputs=[images, age, gender, medical_history, current_medications, 
+                allergies, vital_signs, complaints]
+    )
+    
+    submit_btn.click(
+        fn=process_medical_case,
+        inputs=[images, age, gender, medical_history, current_medications, 
+                allergies, vital_signs, complaints],
+        outputs=output
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        ### ⚕️ Important Information:
+        - This tool is for educational and assistive purposes only
+        - Always consult qualified healthcare professionals for medical decisions
+        - Ensure patient privacy and HIPAA compliance when using this tool
+        - Report any issues or feedback to improve the system
+        
+        ### 🔧 Setup Instructions for Hugging Face:
+        1. Create a Space on Hugging Face
+        2. Add your Google API key as a secret: GOOGLE_API_KEY
+        3. Install requirements: `google-generativeai gradio pillow`
+        4. Deploy this code
+        """
     )
 
-def analyze_medical_image_gemini(
-    image: Image.Image,
-    age: str,
-    gender: str,
-    symptoms: str,
-    history: str,
-    medications: str,
-    analysis_type: str,
-    focus_areas: 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"
-    progress(0.1, desc="Connecting to Gemini...")
-    success, status = setup_gemini()
-    if not success:
-        return status, "", status
-    try:
-        progress(0.3, desc="Preparing analysis...")
-        clinical_data = {
-            "age": age.strip(),
-            "gender": gender,
-            "symptoms": symptoms.strip(),
-            "history": history.strip(),
-            "medications": medications.strip()
-        }
-        prompt = create_medical_prompt(clinical_data, analysis_type, focus_areas)
-        progress(0.5, desc="Analyzing image with Gemini...")
-        response = GEMINI_MODEL.generate_content([prompt, image])
-        if not response or not response.text:
-            return "❌ No response received from Gemini API", "", "❌ Analysis failed"
-        progress(0.9, desc="Preparing results...")
-        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,
-            "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)}"
-        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"""
-    css = """
-    .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:
-        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():
-            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"],
-                    value="Comprehensive", label="Analysis Type"
-                )
-                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(
-            analyze_medical_image_gemini,
-            inputs=[image_input, age, gender, symptoms, history, medications, analysis_type, focus_areas],
-            outputs=[output, download, status]
-        )
-    return interface
-
+# Launch the app
 if __name__ == "__main__":
-    demo = create_interface()
-    demo.launch()
+    demo.launch()