Create app.py

app.py

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+import os
+import streamlit as st
+from PIL import Image
+import google.generativeai as genai
+import json
+from datetime import datetime
+
+# Configure Gemini API key (ideally load from environment variable in production)
+# Using st.secrets for better security in Streamlit
+api_key = "AIzaSyDWm-Z7X4H-b41r2ZRN61UfABdv81D2Gxo"  # In production, use st.secrets["GEMINI_API_KEY"]
+genai.configure(api_key=api_key)
+
+# --- Generation Configuration and Chat Session Setup ---
+generation_config = {
+    "temperature": 0.8,  # Slightly reduced for more consistent medical responses
+    "top_p": 0.95,
+    "top_k": 40,
+    "max_output_tokens": 8192,
+    "response_mime_type": "application/json",  # Changed to JSON for structured parsing
+}
+
+model = genai.GenerativeModel(
+    model_name="gemini-1.5-flash",
+    generation_config=generation_config,
+)
+
+# Define the system prompt for structured JSON responses
+SYSTEM_PROMPT = """
+As a highly skilled medical practitioner specializing in image analysis, 
+you are tasked with examining medical images for a renowned hospital. 
+Your expertise is crucial in identifying any anomalies, diseases, or health issues 
+that may be present in the images.
+
+Your responsibilities:
+1. **Detailed Analysis**: Thoroughly examine the image for abnormalities.
+2. **Analysis Report**: Document findings clearly.
+3. **Recommendations**: Suggest necessary tests or treatments.
+4. **Treatments**: Provide possible treatments for better recovery.
+5. **Risk Level**: Indicate the severity level as "Low", "Medium", "High", or "Critical".
+
+**Important Notes**:
+- Only respond if the image is related to human health.
+- If the image is unclear, state: "Unable to determine based on the uploaded image."
+- Include a disclaimer about consulting with a doctor.
+
+Format the response as a JSON object with these fields:
+{
+  "image_type": "String describing the type of medical image",
+  "detailed_analysis": "Thorough analysis of visible features",
+  "analysis_report": "Summary of findings",
+  "recommendations": "Suggested tests or follow-ups",
+  "treatments": "Possible treatment options",
+  "risk_level": "Low/Medium/High/Critical",
+  "confidence_score": 75,
+  "areas_of_concern": ["List of specific areas or features of concern"]
+}
+"""
+
+
+# --- Functions for UI Components ---
+
+def display_sidebar():
+    with st.sidebar:
+        st.title("🏥 Cure Connect")
+        st.subheader("Your AI Medical Assistant")
+
+        st.markdown("---")
+        st.markdown("### About This Tool")
+        st.markdown("""
+        Cure Connect uses advanced AI to analyze medical images and provide potential insights.
+        **IMPORTANT:** This tool is for educational purposes only and should not replace professional medical advice.
+        """)
+
+        st.markdown("---")
+        st.markdown("### Supported Image Types")
+        st.markdown("""
+        - X-rays
+        - MRI scans
+        - CT scans
+        - Ultrasound images
+        - Dermatological photos
+        """)
+
+        st.markdown("---")
+        st.markdown("### Usage Tips")
+        st.markdown("""
+        1. Upload a clear medical image
+        2. Click "Analyze Image"
+        3. Review the AI analysis
+        4. Share results with your healthcare provider
+        """)
+
+
+def create_analysis_card(title, content, color):
+    st.markdown(
+        f"""
+        <div style="
+            background-color: {color};
+            padding: 20px;
+            border-radius: 10px;
+            margin-bottom: 10px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        ">
+            <h3 style="color: white;">{title}</h3>
+            <p style="color: white;">{content}</p>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+
+def display_risk_gauge(risk_level, confidence):
+    # Map risk levels to numerical values
+    risk_values = {"Low": 1, "Medium": 2, "High": 3, "Critical": 4}
+    risk_value = risk_values.get(risk_level, 0)
+
+    # Colors for different risk levels
+    colors = {
+        "Low": "#4CAF50",  # Green
+        "Medium": "#FFC107",  # Amber
+        "High": "#FF5722",  # Deep Orange
+        "Critical": "#F44336"  # Red
+    }
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.markdown("### Risk Assessment")
+        st.markdown(
+            f"""
+            <div style="
+                background-color: {colors.get(risk_level, "#607D8B")};
+                color: white;
+                text-align: center;
+                padding: 20px;
+                border-radius: 10px;
+                font-size: 24px;
+                font-weight: bold;
+            ">
+                {risk_level}
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
+
+    with col2:
+        st.markdown("### AI Confidence")
+        # Ensure confidence is an integer before using it
+        if isinstance(confidence, str):
+            try:
+                confidence = int(float(confidence))
+            except (ValueError, TypeError):
+                confidence = 75  # Default value if conversion fails
+
+        st.progress(confidence / 100)
+        st.markdown(f"<h1 style='text-align: center;'>{confidence}%</h1>", unsafe_allow_html=True)
+
+
+def generate_pdf_report(analysis_data, image):
+    # In a real app, you would implement PDF generation here
+    st.download_button(
+        label="📄 Download PDF Report",
+        data=f"Sample report for {analysis_data['image_type']} - {datetime.now().strftime('%Y-%m-%d')}",
+        file_name="medical_report.txt",
+        mime="text/plain"
+    )
+
+
+# --- Main Application UI ---
+st.set_page_config(page_title='Cure Connect - Medical Image Analytics',
+                   page_icon='🏥',
+                   layout='wide',
+                   initial_sidebar_state='expanded')
+
+# Apply custom CSS for better styling
+st.markdown("""
+<style>
+    .main {
+        padding: 2rem;
+        background-color: #f8f9fa;
+    }
+    .stButton button {
+        background-color: #4285F4;
+        color: white;
+        border-radius: 10px;
+        padding: 0.5rem 1rem;
+        font-weight: bold;
+    }
+    h1, h2, h3 {
+        color: #01579B;
+    }
+    .stProgress > div > div {
+        background-color: #4285F4;
+    }
+    .disclaimer {
+        background-color: #FFECB3;
+        padding: 10px;
+        border-radius: 5px;
+        border-left: 5px solid #FFC107;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Display sidebar
+display_sidebar()
+
+# Main content area
+st.title("🩺 Cure Connect - Medical Image Analysis")
+st.subheader("AI-assisted diagnostic support platform")
+
+st.markdown("""
+This platform uses advanced AI to analyze medical images and provide potential insights.
+Upload your medical scan below to get started.
+""")
+
+# Create tabs for different sections
+tab1, tab2, tab3 = st.tabs(["📊 Analysis", "ℹ️ How It Works", "❓ FAQ"])
+
+with tab1:
+    # File upload with enhanced UI
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.markdown("### Upload Medical Image")
+        uploaded_file = st.file_uploader('Select an image file',
+                                         type=['png', 'jpg', 'jpeg'],
+                                         help="Upload a clear, high-resolution medical image")
+
+        analyze_col, clear_col = st.columns([1, 1])
+        with analyze_col:
+            analyze_button = st.button('🔍 Analyze Image', use_container_width=True)
+        with clear_col:
+            clear_button = st.button('🗑️ Clear Results', use_container_width=True)
+
+    # Analysis section
+    if analyze_button and uploaded_file:
+        try:
+            # Open and display the uploaded image
+            img = Image.open(uploaded_file)
+
+            with col2:
+                st.markdown("### Uploaded Medical Image")
+                st.image(img, use_column_width=True)
+                st.caption("The AI model will analyze this image to identify potential areas of concern.")
+
+            # Show processing indicator
+            with st.spinner('Analyzing image... Please wait'):
+                # In a production app, you would handle the JSON response properly
+                # For this prototype, we'll simulate a structured response
+                try:
+                    # Generate analysis using the system prompt and the uploaded image
+                    response = model.generate_content([SYSTEM_PROMPT, img])
+
+                    # Try to parse as JSON first
+                    try:
+                        # Parse JSON response
+                        analysis_data = json.loads(response.text)
+                    except json.JSONDecodeError:
+                        # If not valid JSON, create a structured dictionary from text
+                        analysis_data = {
+                            "image_type": "Medical scan",
+                            "detailed_analysis": response.text[:500],
+                            "analysis_report": "See detailed analysis",
+                            "recommendations": "Consult with a healthcare professional",
+                            "treatments": "To be determined by your doctor",
+                            "risk_level": "Medium",
+                            "confidence_score": 75,
+                            "areas_of_concern": ["Unable to parse specific areas from text response"]
+                        }
+
+                    # Ensure confidence_score is an integer
+                    if isinstance(analysis_data["confidence_score"], str):
+                        try:
+                            analysis_data["confidence_score"] = int(float(analysis_data["confidence_score"]))
+                        except (ValueError, TypeError):
+                            analysis_data["confidence_score"] = 75  # Default if conversion fails
+
+                    # Display results in a structured format
+                    st.markdown("## Analysis Results")
+                    st.markdown(f"### Image Type: {analysis_data['image_type']}")
+
+                    # Display risk gauge
+                    display_risk_gauge(analysis_data['risk_level'], analysis_data['confidence_score'])
+
+                    # Display color-coded analysis sections
+                    st.markdown("### Key Findings")
+                    create_analysis_card("Detailed Analysis", analysis_data['detailed_analysis'], "#0277BD")
+                    create_analysis_card("Analysis Report", analysis_data['analysis_report'], "#00897B")
+                    create_analysis_card("Recommendations", analysis_data['recommendations'], "#7B1FA2")
+                    create_analysis_card("Treatments", analysis_data['treatments'], "#C2185B")
+
+                    # Areas of concern
+                    st.markdown("### Areas of Concern")
+                    for i, area in enumerate(analysis_data['areas_of_concern']):
+                        st.markdown(f"- **Area {i + 1}:** {area}")
+
+                    # Disclaimer
+                    st.markdown("""
+                    <div class="disclaimer">
+                        <strong>⚠️ IMPORTANT DISCLAIMER:</strong> This analysis is for informational purposes only 
+                        and should not be considered medical advice. Always consult with a qualified healthcare 
+                        professional for proper diagnosis and treatment options.
+                    </div>
+                    """, unsafe_allow_html=True)
+
+                    # Generate PDF report option
+                    st.markdown("### Export Options")
+                    generate_pdf_report(analysis_data, img)
+
+                except Exception as e:
+                    st.error(f"Error processing response: {str(e)}")
+
+        except Exception as e:
+            st.error(f"Analysis failed: {str(e)}")
+            st.error("Please ensure you're using a valid medical image format (JPEG/PNG)")
+
+with tab2:
+    st.markdown("## How Cure Connect Works")
+
+    st.markdown("""
+    ### 1. Image Analysis
+    When you upload a medical image, our AI system processes it using advanced computer vision techniques
+    to identify patterns, abnormalities, and potential areas of concern.
+
+    ### 2. Medical Knowledge Base
+    The analysis is informed by a vast medical knowledge base that includes information about various
+    conditions, diseases, and their visual presentations in medical imaging.
+
+    ### 3. Risk Assessment
+    Based on the analysis, the system provides a risk assessment categorized as:
+    - **Low Risk** (Green): Minor or no abnormalities detected
+    - **Medium Risk** (Amber): Notable findings that should be discussed with a healthcare provider
+    - **High Risk** (Orange): Significant findings that require prompt medical attention
+    - **Critical Risk** (Red): Urgent findings that may require immediate medical intervention
+
+    ### 4. Confidence Score
+    The confidence score indicates how certain the AI is about its analysis based on image quality,
+    clarity of findings, and comparison with known patterns.
+    """)
+
+    # Add a simple diagram
+    st.markdown("""
+    ```
+    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
+    │  Image      │    │  AI         │    │  Analysis   │    │  Results    │
+    │  Upload     │ -> │  Processing │ -> │  Generation │ -> │  Display    │
+    └─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
+    ```
+    """)
+
+    st.warning("""
+    **Remember:** While our system uses advanced technology to analyze medical images,
+    it is designed to be a supportive tool for healthcare professionals, not a replacement
+    for proper medical consultation and diagnosis.
+    """)
+
+with tab3:
+    st.markdown("## Frequently Asked Questions")
+
+    faq_data = [
+        {
+            "question": "How accurate is the AI analysis?",
+            "answer": "The AI analysis provides an estimated confidence score with each result. However, accuracy varies based on image quality, the type of medical condition, and other factors. Always consult with a healthcare professional for accurate diagnosis."
+        },
+        {
+            "question": "Is my medical data secure?",
+            "answer": "We take data privacy seriously. Your uploaded images are processed securely and not stored permanently unless explicitly requested. All processing is done in compliance with healthcare data protection standards."
+        },
+        {
+            "question": "What types of medical images can I upload?",
+            "answer": "The system can analyze various medical imaging types including X-rays, MRIs, CT scans, ultrasound images, and dermatological photos. The clearer and higher resolution the image, the better the analysis will be."
+        },
+        {
+            "question": "How should I use the results?",
+            "answer": "Consider the results as informational insights that you can discuss with your healthcare provider. The analysis is meant to supplement, not replace, professional medical advice."
+        },
+        {
+            "question": "Can I share the analysis with my doctor?",
+            "answer": "Yes! You can download a PDF report of the analysis to share with your healthcare provider. This can be a helpful starting point for discussions about your health concerns."
+        }
+    ]
+
+    for i, faq in enumerate(faq_data):
+        with st.expander(f"{i + 1}. {faq['question']}"):
+            st.markdown(faq['answer'])
+
+    st.markdown("""
+    ### Have more questions?
+    If you have any other questions about using Cure Connect, please contact our support team
+    at [email protected].
+    """)