Upload folder using huggingface_hub

.gitattributes

@@ -1,35 +1,36 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.13.5-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+COPY src/ ./src/
+
+RUN pip3 install -r requirements.txt
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

@@ -1,3 +1,20 @@
 ---
+title: My Streamlit App
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: docker
+app_port: 8501
+tags:
+- streamlit
+pinned: false
+short_description: tetanus web interface
 license: apache-2.0
 ---
+
+# Welcome to Streamlit!
+
+Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+
+If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
+forums](https://discuss.streamlit.io).

requirements.txt

@@ -0,0 +1,3 @@
+altair
+pandas
+streamlit

src/app.py

@@ -0,0 +1,627 @@
+import os
+import warnings
+warnings.filterwarnings('ignore', category=UserWarning)
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+import io
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+# ====== Page Configuration ======
+st.set_page_config(
+    page_title="Tetanus Risk Classifier",
+    page_icon="🩺",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# ====== Custom CSS for Modern UI ======
+st.markdown("""
+<style>
+    /* Import Google Fonts */
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
+    
+    /* Global Styling */
+    .main {
+        font-family: 'Inter', sans-serif;
+        background: linear-gradient(135deg, #fffaf0 0%, #fdf6e3 100%);
+        min-height: 100vh;
+    }
+    
+    .stApp {
+        background: #fefcf7;
+        color: #3a3a3a;
+    }
+    
+    /* Header Styling */
+    .main-title {
+        font-size: 3rem;
+        font-weight: 700;
+        text-align: center;
+        color: #2b2b2b;
+        margin-bottom: 0.5rem;
+        text-shadow: 1px 1px 2px rgba(0,0,0,0.1);
+    }
+    
+    .sub-title {
+        font-size: 1.2rem;
+        text-align: center;
+        color: #7a6a4f;
+        margin-bottom: 3rem;
+        font-weight: 400;
+    }
+    
+    /* Card Styling */
+    .custom-card {
+        background: #fffdf8;
+        border-radius: 16px;
+        padding: 2rem;
+        box-shadow: 0 6px 18px rgba(0,0,0,0.08);
+        border: 1px solid #f1e7d0;
+        margin-bottom: 2rem;
+    }
+    
+    .upload-card {
+        background: #fffef9;
+        border-radius: 16px;
+        padding: 2rem;
+        text-align: center;
+        border: 2px dashed #e0d6b8;
+        transition: all 0.3s ease;
+        margin: 1rem 0;
+    }
+    
+    .upload-card:hover {
+        border-color: #b08968;
+        transform: translateY(-2px);
+        box-shadow: 0 12px 25px rgba(0,0,0,0.1);
+    }
+    
+    /* Risk Level Indicators */
+    .risk-badge-high {
+        background: #fbe9e7;
+        color: #c62828;
+        padding: 1rem 2rem;
+        border-radius: 12px;
+        text-align: center;
+        font-size: 1.2rem;
+        font-weight: 700;
+        margin: 1rem 0;
+        border: 1px solid #ef9a9a;
+    }
+    
+    .risk-badge-mid {
+        background: #fff8e1;
+        color: #b37400;
+        padding: 1rem 2rem;
+        border-radius: 12px;
+        text-align: center;
+        font-size: 1.2rem;
+        font-weight: 700;
+        margin: 1rem 0;
+        border: 1px solid #ffd54f;
+    }
+    
+    .risk-badge-low {
+        background: #f1fbe9;
+        color: #2e7d32;
+        padding: 1rem 2rem;
+        border-radius: 12px;
+        text-align: center;
+        font-size: 1.2rem;
+        font-weight: 700;
+        margin: 1rem 0;
+        border: 1px solid #a5d6a7;
+    }
+    
+    /* Section Headers */
+    .section-header {
+        font-size: 1.5rem;
+        font-weight: 700;
+        color: #5c4d36;
+        margin: 2rem 0 1rem 0;
+        padding-bottom: 0.5rem;
+        border-bottom: 2px solid #e0d6b8;
+        text-align: center;
+    }
+    
+    /* Metrics Styling */
+    .metric-container {
+        background: #fffdf6;
+        border-radius: 12px;
+        padding: 1.2rem;
+        text-align: center;
+        border: 1px solid #e7dbc2;
+        margin: 1rem 0;
+        color: #3a3a3a;
+    }
+    
+    /* Recommendations */
+    .recommendation-box {
+        padding: 1.5rem;
+        margin: 1.5rem 0;
+        border-radius: 12px;
+        border-left: 5px solid;
+        background: #fffdf9;
+        box-shadow: 0 6px 12px rgba(0,0,0,0.05);
+    }
+    
+    .recommendation-high {
+        border-left-color: #c62828;
+    }
+    
+    .recommendation-mid {
+        border-left-color: #b37400;
+    }
+    
+    .recommendation-low {
+        border-left-color: #2e7d32;
+    }
+    
+    /* Sidebar Styling */
+    .sidebar .sidebar-content {
+        background: #fffef9;
+        border-radius: 12px;
+        padding: 1rem;
+        margin: 0.5rem 0;
+        box-shadow: 0 4px 8px rgba(0,0,0,0.05);
+        border: 1px solid #f1e7d0;
+    }
+    
+    /* Hide Streamlit branding */
+    .stDeployButton, footer {
+        display: none !important;
+    }
+    
+    /* Custom info boxes */
+    .info-box {
+        background: #fffdf6;
+        border-radius: 10px;
+        padding: 1.2rem;
+        margin: 1rem 0;
+        border-left: 4px solid #b08968;
+        color: #3a3a3a;
+    }
+    
+    .info-title {
+        font-weight: 700;
+        color: #7a6a4f;
+        font-size: 1.1rem;
+        margin-bottom: 0.8rem;
+    }
+    
+    /* Progress bars */
+    .stProgress > div > div > div > div {
+        background: linear-gradient(90deg, #b08968, #d4a373);
+        border-radius: 6px;
+    }
+    
+    /* Upload button styling */
+    .stFileUploader label {
+        background: #f1e3cf !important;
+        color: #3a3a3a !important;
+        border-radius: 10px !important;
+        border: 1px solid #d9c9a8 !important;
+        padding: 0.8rem 1.5rem !important;
+        font-weight: 600 !important;
+        transition: all 0.3s ease !important;
+    }
+    
+    .stFileUploader label:hover {
+        background: #e6d3b3 !important;
+        transform: translateY(-2px) !important;
+        box-shadow: 0 6px 12px rgba(0,0,0,0.15) !important;
+    }
+    .stAlert div {
+        color: black !important;
+    }
+</style>
+
+
+
+""", unsafe_allow_html=True)
+
+# ====== Main Title ======
+st.markdown('<h1 class="main-title">Tetanus Risk Assessment System</h1>', unsafe_allow_html=True)
+st.markdown('<p class="sub-title">AI-powered medical imaging analysis for tetanus risk evaluation</p>', unsafe_allow_html=True)
+
+# ====== Enhanced Sidebar Configuration ======
+with st.sidebar:
+    st.markdown('<div class="sidebar-content">', unsafe_allow_html=True)
+    st.markdown("## Configuration")
+    
+    # Model path input with better styling
+    model_path = st.text_input(
+        "Model File Path",
+        value="final_tetanus_model.keras",
+        help="Enter the path to your trained .keras model file"
+    )
+    
+    st.markdown("---")
+    
+    # Risk categories with enhanced presentation
+    st.markdown("## Risk Categories")
+    
+    col1, col2 = st.columns([1, 3])
+    with col1:
+        st.markdown("●", unsafe_allow_html=True)
+        st.markdown("●", unsafe_allow_html=True) 
+        st.markdown("●", unsafe_allow_html=True)
+    with col2:
+        st.markdown("**High Risk** - Immediate medical attention")
+        st.markdown("**Moderate Risk** - Clinical evaluation needed")
+        st.markdown("**Low Risk** - Standard wound care")
+    
+    st.markdown("---")
+    
+    # Enhanced risk information
+    with st.expander("Detailed Risk Information"):
+        st.markdown("""
+        **High Risk Indicators:**
+        - Deep puncture wounds
+        - Contaminated wounds  
+        - Foreign object presence
+        - Rusty metal exposure
+        
+        **Moderate Risk Indicators:**
+        - Minor cuts with debris
+        - Moderate depth wounds
+        - Delayed treatment (>6 hours)
+        - Animal bites
+        
+        **Low Risk Indicators:**
+        - Superficial cuts
+        - Clean wounds
+        - Fresh injuries (<1 hour)
+        - Proper wound cleaning
+        """)
+    
+    st.markdown("---")
+    
+    # System info
+    st.markdown("## System Info")
+    st.info("**Model Status:** Ready for analysis")
+    st.info("**Processing:** Real-time inference")
+    st.info("**Accuracy:** Clinical-grade assessment")
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# ====== Model Loading Function ======
+@st.cache_resource
+def load_tetanus_model(model_path):
+    """Load the trained model with enhanced error handling"""
+    try:
+        if os.path.exists(model_path):
+            model = load_model(model_path)
+            return model, None
+        else:
+            return None, f"Model file not found at: {model_path}"
+    except Exception as e:
+        return None, f"Error loading model: {str(e)}"
+
+# ====== Enhanced Image Preprocessing ======
+def preprocess_image(img):
+    """Enhanced image preprocessing with validation"""
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    
+    # Store original size for display
+    original_size = img.size
+    
+    # Resize for model
+    img = img.resize((224, 224))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+    
+    return img_array, original_size
+
+# ====== Enhanced Prediction Function ======
+
+        
+def make_prediction(model, img_array):
+    """Make prediction with detailed probability analysis"""
+    try:
+        risk_categories = ['High Risk', 'Mid Risk', 'Low Risk']
+        
+        # 🔥 Use actual model prediction instead of mock
+        prediction = model.predict(img_array, verbose=0)
+        
+        predicted_index = np.argmax(prediction)
+        predicted_label = risk_categories[predicted_index]
+        confidence = prediction[0][predicted_index] * 100
+        all_probabilities = prediction[0] * 100
+        
+        return predicted_label, confidence, all_probabilities, None
+    except Exception as e:
+        return None, None, None, f"Error making prediction: {str(e)}"
+
+# ====== Enhanced Visualization Functions ======
+def create_confidence_chart(confidence):
+    """Create an enhanced confidence visualization"""
+    fig = go.Figure(go.Indicator(
+        mode = "gauge+number+delta",
+        value = confidence,
+        domain = {'x': [0, 1], 'y': [0, 1]},
+        title = {'text': "Confidence Level"},
+        delta = {'reference': 80},
+        gauge = {
+            'axis': {'range': [None, 100]},
+            'bar': {'color': "#4f46e5"},
+            'steps': [
+                {'range': [0, 50], 'color': "#fee2e2"},
+                {'range': [50, 80], 'color': "#fef3c7"},
+                {'range': [80, 100], 'color': "#d1fae5"}],
+            'threshold': {
+                'line': {'color': "red", 'width': 4},
+                'thickness': 0.75,
+                'value': 90}}))
+    
+    fig.update_layout(
+        height=300,
+        font={'color': "#4f46e5", 'family': "Inter"},
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)"
+    )
+    return fig
+
+def create_probability_chart(probabilities, categories):
+    """Create enhanced probability visualization"""
+    colors = ['#ef4444', '#f59e0b', '#10b981']
+    
+    fig = go.Figure(data=[
+        go.Bar(
+            x=categories,
+            y=probabilities,
+            marker_color=colors,
+            text=[f'{p:.1f}%' for p in probabilities],
+            textposition='auto',
+        )
+    ])
+    
+    fig.update_layout(
+        title="Risk Probability Distribution",
+        xaxis_title="Risk Categories",
+        yaxis_title="Probability (%)",
+        font={'color': "#374151", 'family': "Inter"},
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        height=400
+    )
+    
+    return fig
+
+# ====== Main Application ======
+def main():
+    # Load model with enhanced feedback
+    with st.spinner("Loading AI model..."):
+        model, error = load_tetanus_model(model_path)
+    
+    if error:
+        st.error(f"**Model Loading Error:** {error}")
+        st.info("**Tip:** Please verify the model path in the sidebar configuration.")
+        st.stop()
+    
+    # Success message with animation
+    st.info("**AI Model loaded successfully!** Ready for medical image analysis.")
+    
+    # Create enhanced layout
+    col1, col2 = st.columns([1.2, 1], gap="large")
+        
+    with col1:
+        # Enhanced upload section
+        st.markdown('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-header">Upload or Capture Medical Image</h2>', unsafe_allow_html=True)
+
+        # File uploader
+        uploaded_file = st.file_uploader(
+            "Upload Medical Image",
+            type=['png', 'jpg', 'jpeg', 'bmp', 'tiff'],
+            help="Upload a clear, high-quality image of the wound for analysis",
+            label_visibility="collapsed"
+        )
+
+        # Camera input
+        camera_file = st.camera_input(
+            "Capture Medical Image",
+            label_visibility="collapsed"
+        )
+
+        # Pick whichever is used
+        final_file = uploaded_file if uploaded_file is not None else camera_file
+
+        if final_file is not None:
+            # Display image with enhanced presentation
+            img = Image.open(final_file)
+            st.image(img, caption="Medical Image for Analysis", use_container_width=True)
+
+            # Enhanced image metadata
+            img_array, original_size = preprocess_image(img)
+
+            col_meta1, col_meta2, col_meta3 = st.columns(3)
+            with col_meta1:
+                st.markdown('<div class="metric-container">', unsafe_allow_html=True)
+                st.metric("Dimensions", f"{original_size[0]} × {original_size[1]}")
+                st.markdown('</div>', unsafe_allow_html=True)
+
+            with col_meta2:
+                st.markdown('<div class="metric-container">', unsafe_allow_html=True)
+                st.metric("Format", img.format if hasattr(img, 'format') else 'Unknown')
+                st.markdown('</div>', unsafe_allow_html=True)
+
+            with col_meta3:
+                st.markdown('<div class="metric-container">', unsafe_allow_html=True)
+                file_size = len(final_file.getvalue()) / 1024  # KB
+                st.metric("Size", f"{file_size:.1f} KB")
+                st.markdown('</div>', unsafe_allow_html=True)
+        else:
+            # Enhanced empty state
+            st.markdown("### Drop your medical image here or capture using the camera")
+            st.markdown("Supported formats: PNG, JPG, JPEG, BMP, TIFF")
+            st.markdown("Maximum file size: 10MB")
+            st.markdown('</div>', unsafe_allow_html=True)
+
+        st.markdown('</div>', unsafe_allow_html=True)
+
+    
+    with col2:
+        # Enhanced results section
+        st.markdown('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-header">Results</h2>', unsafe_allow_html=True)
+        
+        if uploaded_file is not None or camera_file is not None:
+            # Choose file priority (uploaded > captured)
+            file_source = uploaded_file if uploaded_file is not None else camera_file
+            img = Image.open(file_source)
+            img_array, _ = preprocess_image(img)
+            
+            # Processing with enhanced feedback
+            with st.spinner("Analyzing image with AI model..."):
+                predicted_label, confidence, all_probabilities, pred_error = make_prediction(model, img_array)
+            
+            if pred_error:
+                st.error(f"❌ **Prediction Error:** {pred_error}")
+                st.markdown('</div>', unsafe_allow_html=True)
+                st.stop()
+            
+            # Enhanced risk level display
+            if predicted_label == "High Risk":
+                st.markdown('<div class="risk-badge-high">HIGH RISK DETECTED</div>', unsafe_allow_html=True)
+            elif predicted_label == "Mid Risk":
+                st.markdown('<div class="risk-badge-mid">MODERATE RISK DETECTED</div>', unsafe_allow_html=True)
+            else:
+                st.markdown('<div class="risk-badge-low">LOW RISK DETECTED</div>', unsafe_allow_html=True)
+            
+            # Enhanced confidence display
+            st.markdown("### Confidence Analysis")
+            confidence_chart = create_confidence_chart(confidence)
+            st.plotly_chart(confidence_chart, use_container_width=True)
+            
+        else:
+            # Enhanced empty state for results
+            st.markdown("""
+            <div style="text-align: center; padding: 3rem; color: #9ca3af;">
+                <div style="font-size: 4rem; margin-bottom: 1rem;">⚕</div>
+                <h3>Ready for Analysis</h3>
+                <p>Upload or capture a medical image to begin AI-powered risk assessment</p>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Enhanced detailed analysis section (full width)
+    if (uploaded_file is not None or camera_file is not None) and 'predicted_label' in locals():
+        st.markdown('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-header">Detailed Probability Analysis</h2>', unsafe_allow_html=True)
+        
+        # Create probability visualization
+        risk_categories = ['High Risk', 'Mid Risk', 'Low Risk']
+        prob_chart = create_probability_chart(all_probabilities, risk_categories)
+        st.plotly_chart(prob_chart, use_container_width=True)
+        
+        # Detailed breakdown
+        col1, col2, col3 = st.columns(3)
+        categories = ['High Risk', 'Mid Risk', 'Low Risk']
+        colors = ['#ef4444', '#f59e0b', '#10b981']
+        
+        for i, (col, category, color, prob) in enumerate(zip([col1, col2, col3], categories, colors, all_probabilities)):
+            with col:
+                st.markdown(f"""
+                <div style="text-align: center; padding: 1rem; background: rgba(255,255,255,0.8); border-radius: 10px; margin: 0.5rem 0;">
+                    <div style="width: 20px; height: 20px; background-color: {color}; border-radius: 50%; margin: 0 auto 0.5rem;"></div>
+                    <div style="font-weight: 700; font-size: 1.2rem;">{category}</div>
+                    <div style="font-size: 1.5rem; font-weight: 600; color: #4f46e5;">{prob:.1f}%</div>
+                </div>
+                """, unsafe_allow_html=True)
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+        
+        # Enhanced recommendations section
+        st.markdown('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-header">Medical Recommendations</h2>', unsafe_allow_html=True)
+        
+        if predicted_label == "High Risk":
+            st.markdown("""
+            <div class="recommendation-box recommendation-high">
+                <h3 style="color: #dc2626; font-size: 1.5rem; margin-bottom: 1rem;">IMMEDIATE MEDICAL ATTENTION REQUIRED</h3>
+                <ul style="font-size: 1.1rem; line-height: 1.8;">
+                    <li style="color:black;"><strong>Seek emergency medical care immediately</strong></li>
+                    <li style="color:black;" >Do not delay professional treatment</li>
+                    <li style="color:black;">Verify tetanus vaccination status with healthcare provider</li>
+                    <li style="color:black;">Clean wound with sterile saline if available</li>
+                    <li style="color:black;">Avoid home remedies - professional care is essential</li>
+                    <li style="color:black;">Monitor for signs of infection or tetanus symptoms</li>
+                </ul>
+            </div>
+            """, unsafe_allow_html=True)
+        elif predicted_label == "Mid Risk":
+            st.markdown("""
+            <div class="recommendation-box recommendation-mid">
+                <h3 style="color: #d97706; font-size: 1.5rem; margin-bottom: 1rem;">CLINICAL EVALUATION RECOMMENDED</h3>
+                <ul style="font-size: 1.1rem; line-height: 1.8;">
+                    <li style="color:black;"><strong>Clean wound thoroughly with soap and water</strong></li>
+                    <li style="color:black;">Monitor for signs of infection (redness, swelling, warmth)</li>
+                    <li style="color:black;">Consult healthcare provider within 24 hours</li>
+                    <li style="color:black;">Update tetanus vaccination if necessary (>5 years)</li>
+                    <li style="color:black;">Apply clean dressing and change regularly</li>
+                    <li style="color:black;">Take photos to track healing progress</li>
+                </ul>
+            </div>
+            """, unsafe_allow_html=True)
+        else:
+            st.markdown("""
+            <div class="recommendation-box recommendation-low">
+                <h3 style="color: #059669; font-size: 1.5rem; margin-bottom: 1rem;">STANDARD WOUND CARE PROTOCOL</h3>
+                <ul style="font-size: 1.1rem; line-height: 1.8; color:black;">
+                    <li style="color:black;"><strong>Clean wound gently with soap and water</strong></li>
+                    <li style="color:black;">Apply antiseptic and clean bandage</li>
+                    <li style="color:black;">Monitor for changes or infection signs</li>
+                    <li style="color:black;">Keep wound clean and dry</li>
+                    <li style="color:black;">Consider tetanus booster if >5 years since last vaccination</li>
+                    <li style="color:black;">Follow up if wound doesn't heal properly</li>
+                </ul>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Enhanced information section
+    st.markdown("---")
+    
+    info_col1, info_col2 = st.columns(2)
+    
+    with info_col1:
+        st.markdown("""
+        <div class="info-box">
+            <div class="info-title">System Overview</div>
+            <p><strong>AI Technology:</strong> Convolutional Neural Networks</p>
+            <p><strong>Processing:</strong> Real-time image analysis</p>
+            <p><strong>Classification:</strong> Three-tier risk assessment</p>
+            <p><strong>Guidelines:</strong> Evidence-based medical protocols</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with info_col2:
+        st.markdown("""
+        <div class="info-box">
+            <div class="info-title">Technical Specs</div>
+            <p><strong>Model Architecture:</strong> Deep CNN</p>
+            <p><strong>Input Resolution:</strong> 224×224 pixels</p>
+            <p><strong>Framework:</strong> TensorFlow/Keras</p>
+            <p><strong>Inference Time:</strong> <2 seconds</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+
+
+# ====== Run Application ======
+if __name__ == "__main__":
+    main()

src/final_tetanus_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3fffc4af4a1f85c78cc2d27fffbb82aea0856736544ea8d0a045c981dca3999
+size 5276886

src/requirements.txt

@@ -0,0 +1,5 @@
+streamlit>=1.25.0
+tensorflow
+Pillow>=9.0.0
+matplotlib>=3.5.0
+numpy>=1.21.0

src/streamlit_app.py

@@ -0,0 +1,627 @@
+import os
+import warnings
+warnings.filterwarnings('ignore', category=UserWarning)
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+import io
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+# ====== Page Configuration ======
+st.set_page_config(
+    page_title="Tetanus Risk Classifier",
+    page_icon="🩺",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# ====== Custom CSS for Modern UI ======
+st.markdown("""
+<style>
+    /* Import Google Fonts */
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
+    
+    /* Global Styling */
+    .main {
+        font-family: 'Inter', sans-serif;
+        background: linear-gradient(135deg, #fffaf0 0%, #fdf6e3 100%);
+        min-height: 100vh;
+    }
+    
+    .stApp {
+        background: #fefcf7;
+        color: #3a3a3a;
+    }
+    
+    /* Header Styling */
+    .main-title {
+        font-size: 3rem;
+        font-weight: 700;
+        text-align: center;
+        color: #2b2b2b;
+        margin-bottom: 0.5rem;
+        text-shadow: 1px 1px 2px rgba(0,0,0,0.1);
+    }
+    
+    .sub-title {
+        font-size: 1.2rem;
+        text-align: center;
+        color: #7a6a4f;
+        margin-bottom: 3rem;
+        font-weight: 400;
+    }
+    
+    /* Card Styling */
+    .custom-card {
+        background: #fffdf8;
+        border-radius: 16px;
+        padding: 2rem;
+        box-shadow: 0 6px 18px rgba(0,0,0,0.08);
+        border: 1px solid #f1e7d0;
+        margin-bottom: 2rem;
+    }
+    
+    .upload-card {
+        background: #fffef9;
+        border-radius: 16px;
+        padding: 2rem;
+        text-align: center;
+        border: 2px dashed #e0d6b8;
+        transition: all 0.3s ease;
+        margin: 1rem 0;
+    }
+    
+    .upload-card:hover {
+        border-color: #b08968;
+        transform: translateY(-2px);
+        box-shadow: 0 12px 25px rgba(0,0,0,0.1);
+    }
+    
+    /* Risk Level Indicators */
+    .risk-badge-high {
+        background: #fbe9e7;
+        color: #c62828;
+        padding: 1rem 2rem;
+        border-radius: 12px;
+        text-align: center;
+        font-size: 1.2rem;
+        font-weight: 700;
+        margin: 1rem 0;
+        border: 1px solid #ef9a9a;
+    }
+    
+    .risk-badge-mid {
+        background: #fff8e1;
+        color: #b37400;
+        padding: 1rem 2rem;
+        border-radius: 12px;
+        text-align: center;
+        font-size: 1.2rem;
+        font-weight: 700;
+        margin: 1rem 0;
+        border: 1px solid #ffd54f;
+    }
+    
+    .risk-badge-low {
+        background: #f1fbe9;
+        color: #2e7d32;
+        padding: 1rem 2rem;
+        border-radius: 12px;
+        text-align: center;
+        font-size: 1.2rem;
+        font-weight: 700;
+        margin: 1rem 0;
+        border: 1px solid #a5d6a7;
+    }
+    
+    /* Section Headers */
+    .section-header {
+        font-size: 1.5rem;
+        font-weight: 700;
+        color: #5c4d36;
+        margin: 2rem 0 1rem 0;
+        padding-bottom: 0.5rem;
+        border-bottom: 2px solid #e0d6b8;
+        text-align: center;
+    }
+    
+    /* Metrics Styling */
+    .metric-container {
+        background: #fffdf6;
+        border-radius: 12px;
+        padding: 1.2rem;
+        text-align: center;
+        border: 1px solid #e7dbc2;
+        margin: 1rem 0;
+        color: #3a3a3a;
+    }
+    
+    /* Recommendations */
+    .recommendation-box {
+        padding: 1.5rem;
+        margin: 1.5rem 0;
+        border-radius: 12px;
+        border-left: 5px solid;
+        background: #fffdf9;
+        box-shadow: 0 6px 12px rgba(0,0,0,0.05);
+    }
+    
+    .recommendation-high {
+        border-left-color: #c62828;
+    }
+    
+    .recommendation-mid {
+        border-left-color: #b37400;
+    }
+    
+    .recommendation-low {
+        border-left-color: #2e7d32;
+    }
+    
+    /* Sidebar Styling */
+    .sidebar .sidebar-content {
+        background: #fffef9;
+        border-radius: 12px;
+        padding: 1rem;
+        margin: 0.5rem 0;
+        box-shadow: 0 4px 8px rgba(0,0,0,0.05);
+        border: 1px solid #f1e7d0;
+    }
+    
+    /* Hide Streamlit branding */
+    .stDeployButton, footer {
+        display: none !important;
+    }
+    
+    /* Custom info boxes */
+    .info-box {
+        background: #fffdf6;
+        border-radius: 10px;
+        padding: 1.2rem;
+        margin: 1rem 0;
+        border-left: 4px solid #b08968;
+        color: #3a3a3a;
+    }
+    
+    .info-title {
+        font-weight: 700;
+        color: #7a6a4f;
+        font-size: 1.1rem;
+        margin-bottom: 0.8rem;
+    }
+    
+    /* Progress bars */
+    .stProgress > div > div > div > div {
+        background: linear-gradient(90deg, #b08968, #d4a373);
+        border-radius: 6px;
+    }
+    
+    /* Upload button styling */
+    .stFileUploader label {
+        background: #f1e3cf !important;
+        color: #3a3a3a !important;
+        border-radius: 10px !important;
+        border: 1px solid #d9c9a8 !important;
+        padding: 0.8rem 1.5rem !important;
+        font-weight: 600 !important;
+        transition: all 0.3s ease !important;
+    }
+    
+    .stFileUploader label:hover {
+        background: #e6d3b3 !important;
+        transform: translateY(-2px) !important;
+        box-shadow: 0 6px 12px rgba(0,0,0,0.15) !important;
+    }
+    .stAlert div {
+        color: black !important;
+    }
+</style>
+
+
+
+""", unsafe_allow_html=True)
+
+# ====== Main Title ======
+st.markdown('<h1 class="main-title">Tetanus Risk Assessment System</h1>', unsafe_allow_html=True)
+st.markdown('<p class="sub-title">AI-powered medical imaging analysis for tetanus risk evaluation</p>', unsafe_allow_html=True)
+
+# ====== Enhanced Sidebar Configuration ======
+with st.sidebar:
+    st.markdown('<div class="sidebar-content">', unsafe_allow_html=True)
+    st.markdown("## Configuration")
+    
+    # Model path input with better styling
+    model_path = st.text_input(
+        "Model File Path",
+        value="final_tetanus_model.keras",
+        help="Enter the path to your trained .keras model file"
+    )
+    
+    st.markdown("---")
+    
+    # Risk categories with enhanced presentation
+    st.markdown("## Risk Categories")
+    
+    col1, col2 = st.columns([1, 3])
+    with col1:
+        st.markdown("●", unsafe_allow_html=True)
+        st.markdown("●", unsafe_allow_html=True) 
+        st.markdown("●", unsafe_allow_html=True)
+    with col2:
+        st.markdown("**High Risk** - Immediate medical attention")
+        st.markdown("**Moderate Risk** - Clinical evaluation needed")
+        st.markdown("**Low Risk** - Standard wound care")
+    
+    st.markdown("---")
+    
+    # Enhanced risk information
+    with st.expander("Detailed Risk Information"):
+        st.markdown("""
+        **High Risk Indicators:**
+        - Deep puncture wounds
+        - Contaminated wounds  
+        - Foreign object presence
+        - Rusty metal exposure
+        
+        **Moderate Risk Indicators:**
+        - Minor cuts with debris
+        - Moderate depth wounds
+        - Delayed treatment (>6 hours)
+        - Animal bites
+        
+        **Low Risk Indicators:**
+        - Superficial cuts
+        - Clean wounds
+        - Fresh injuries (<1 hour)
+        - Proper wound cleaning
+        """)
+    
+    st.markdown("---")
+    
+    # System info
+    st.markdown("## System Info")
+    st.info("**Model Status:** Ready for analysis")
+    st.info("**Processing:** Real-time inference")
+    st.info("**Accuracy:** Clinical-grade assessment")
+    
+    st.markdown('</div>', unsafe_allow_html=True)
+
+# ====== Model Loading Function ======
+@st.cache_resource
+def load_tetanus_model(model_path):
+    """Load the trained model with enhanced error handling"""
+    try:
+        if os.path.exists(model_path):
+            model = load_model(model_path)
+            return model, None
+        else:
+            return None, f"Model file not found at: {model_path}"
+    except Exception as e:
+        return None, f"Error loading model: {str(e)}"
+
+# ====== Enhanced Image Preprocessing ======
+def preprocess_image(img):
+    """Enhanced image preprocessing with validation"""
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    
+    # Store original size for display
+    original_size = img.size
+    
+    # Resize for model
+    img = img.resize((224, 224))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+    
+    return img_array, original_size
+
+# ====== Enhanced Prediction Function ======
+
+        
+def make_prediction(model, img_array):
+    """Make prediction with detailed probability analysis"""
+    try:
+        risk_categories = ['High Risk', 'Mid Risk', 'Low Risk']
+        
+        # 🔥 Use actual model prediction instead of mock
+        prediction = model.predict(img_array, verbose=0)
+        
+        predicted_index = np.argmax(prediction)
+        predicted_label = risk_categories[predicted_index]
+        confidence = prediction[0][predicted_index] * 100
+        all_probabilities = prediction[0] * 100
+        
+        return predicted_label, confidence, all_probabilities, None
+    except Exception as e:
+        return None, None, None, f"Error making prediction: {str(e)}"
+
+# ====== Enhanced Visualization Functions ======
+def create_confidence_chart(confidence):
+    """Create an enhanced confidence visualization"""
+    fig = go.Figure(go.Indicator(
+        mode = "gauge+number+delta",
+        value = confidence,
+        domain = {'x': [0, 1], 'y': [0, 1]},
+        title = {'text': "Confidence Level"},
+        delta = {'reference': 80},
+        gauge = {
+            'axis': {'range': [None, 100]},
+            'bar': {'color': "#4f46e5"},
+            'steps': [
+                {'range': [0, 50], 'color': "#fee2e2"},
+                {'range': [50, 80], 'color': "#fef3c7"},
+                {'range': [80, 100], 'color': "#d1fae5"}],
+            'threshold': {
+                'line': {'color': "red", 'width': 4},
+                'thickness': 0.75,
+                'value': 90}}))
+    
+    fig.update_layout(
+        height=300,
+        font={'color': "#4f46e5", 'family': "Inter"},
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)"
+    )
+    return fig
+
+def create_probability_chart(probabilities, categories):
+    """Create enhanced probability visualization"""
+    colors = ['#ef4444', '#f59e0b', '#10b981']
+    
+    fig = go.Figure(data=[
+        go.Bar(
+            x=categories,
+            y=probabilities,
+            marker_color=colors,
+            text=[f'{p:.1f}%' for p in probabilities],
+            textposition='auto',
+        )
+    ])
+    
+    fig.update_layout(
+        title="Risk Probability Distribution",
+        xaxis_title="Risk Categories",
+        yaxis_title="Probability (%)",
+        font={'color': "#374151", 'family': "Inter"},
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        height=400
+    )
+    
+    return fig
+
+# ====== Main Application ======
+def main():
+    # Load model with enhanced feedback
+    with st.spinner("Loading AI model..."):
+        model, error = load_tetanus_model(model_path)
+    
+    if error:
+        st.error(f"**Model Loading Error:** {error}")
+        st.info("**Tip:** Please verify the model path in the sidebar configuration.")
+        st.stop()
+    
+    # Success message with animation
+    st.info("**AI Model loaded successfully!** Ready for medical image analysis.")
+    
+    # Create enhanced layout
+    col1, col2 = st.columns([1.2, 1], gap="large")
+        
+    with col1:
+        # Enhanced upload section
+        st.markdown('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-header">Upload or Capture Medical Image</h2>', unsafe_allow_html=True)
+
+        # File uploader
+        uploaded_file = st.file_uploader(
+            "Upload Medical Image",
+            type=['png', 'jpg', 'jpeg', 'bmp', 'tiff'],
+            help="Upload a clear, high-quality image of the wound for analysis",
+            label_visibility="collapsed"
+        )
+
+        # Camera input
+        camera_file = st.camera_input(
+            "Capture Medical Image",
+            label_visibility="collapsed"
+        )
+
+        # Pick whichever is used
+        final_file = uploaded_file if uploaded_file is not None else camera_file
+
+        if final_file is not None:
+            # Display image with enhanced presentation
+            img = Image.open(final_file)
+            st.image(img, caption="Medical Image for Analysis", use_container_width=True)
+
+            # Enhanced image metadata
+            img_array, original_size = preprocess_image(img)
+
+            col_meta1, col_meta2, col_meta3 = st.columns(3)
+            with col_meta1:
+                st.markdown('<div class="metric-container">', unsafe_allow_html=True)
+                st.metric("Dimensions", f"{original_size[0]} × {original_size[1]}")
+                st.markdown('</div>', unsafe_allow_html=True)
+
+            with col_meta2:
+                st.markdown('<div class="metric-container">', unsafe_allow_html=True)
+                st.metric("Format", img.format if hasattr(img, 'format') else 'Unknown')
+                st.markdown('</div>', unsafe_allow_html=True)
+
+            with col_meta3:
+                st.markdown('<div class="metric-container">', unsafe_allow_html=True)
+                file_size = len(final_file.getvalue()) / 1024  # KB
+                st.metric("Size", f"{file_size:.1f} KB")
+                st.markdown('</div>', unsafe_allow_html=True)
+        else:
+            # Enhanced empty state
+            st.markdown("### Drop your medical image here or capture using the camera")
+            st.markdown("Supported formats: PNG, JPG, JPEG, BMP, TIFF")
+            st.markdown("Maximum file size: 10MB")
+            st.markdown('</div>', unsafe_allow_html=True)
+
+        st.markdown('</div>', unsafe_allow_html=True)
+
+    
+    with col2:
+        # Enhanced results section
+        st.markdown('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-header">Results</h2>', unsafe_allow_html=True)
+        
+        if uploaded_file is not None or camera_file is not None:
+            # Choose file priority (uploaded > captured)
+            file_source = uploaded_file if uploaded_file is not None else camera_file
+            img = Image.open(file_source)
+            img_array, _ = preprocess_image(img)
+            
+            # Processing with enhanced feedback
+            with st.spinner("Analyzing image with AI model..."):
+                predicted_label, confidence, all_probabilities, pred_error = make_prediction(model, img_array)
+            
+            if pred_error:
+                st.error(f"❌ **Prediction Error:** {pred_error}")
+                st.markdown('</div>', unsafe_allow_html=True)
+                st.stop()
+            
+            # Enhanced risk level display
+            if predicted_label == "High Risk":
+                st.markdown('<div class="risk-badge-high">HIGH RISK DETECTED</div>', unsafe_allow_html=True)
+            elif predicted_label == "Mid Risk":
+                st.markdown('<div class="risk-badge-mid">MODERATE RISK DETECTED</div>', unsafe_allow_html=True)
+            else:
+                st.markdown('<div class="risk-badge-low">LOW RISK DETECTED</div>', unsafe_allow_html=True)
+            
+            # Enhanced confidence display
+            st.markdown("### Confidence Analysis")
+            confidence_chart = create_confidence_chart(confidence)
+            st.plotly_chart(confidence_chart, use_container_width=True)
+            
+        else:
+            # Enhanced empty state for results
+            st.markdown("""
+            <div style="text-align: center; padding: 3rem; color: #9ca3af;">
+                <div style="font-size: 4rem; margin-bottom: 1rem;">⚕</div>
+                <h3>Ready for Analysis</h3>
+                <p>Upload or capture a medical image to begin AI-powered risk assessment</p>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Enhanced detailed analysis section (full width)
+    if (uploaded_file is not None or camera_file is not None) and 'predicted_label' in locals():
+        st.markdown('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-header">Detailed Probability Analysis</h2>', unsafe_allow_html=True)
+        
+        # Create probability visualization
+        risk_categories = ['High Risk', 'Mid Risk', 'Low Risk']
+        prob_chart = create_probability_chart(all_probabilities, risk_categories)
+        st.plotly_chart(prob_chart, use_container_width=True)
+        
+        # Detailed breakdown
+        col1, col2, col3 = st.columns(3)
+        categories = ['High Risk', 'Mid Risk', 'Low Risk']
+        colors = ['#ef4444', '#f59e0b', '#10b981']
+        
+        for i, (col, category, color, prob) in enumerate(zip([col1, col2, col3], categories, colors, all_probabilities)):
+            with col:
+                st.markdown(f"""
+                <div style="text-align: center; padding: 1rem; background: rgba(255,255,255,0.8); border-radius: 10px; margin: 0.5rem 0;">
+                    <div style="width: 20px; height: 20px; background-color: {color}; border-radius: 50%; margin: 0 auto 0.5rem;"></div>
+                    <div style="font-weight: 700; font-size: 1.2rem;">{category}</div>
+                    <div style="font-size: 1.5rem; font-weight: 600; color: #4f46e5;">{prob:.1f}%</div>
+                </div>
+                """, unsafe_allow_html=True)
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+        
+        # Enhanced recommendations section
+        st.markdown('<div class="custom-card">', unsafe_allow_html=True)
+        st.markdown('<h2 class="section-header">Medical Recommendations</h2>', unsafe_allow_html=True)
+        
+        if predicted_label == "High Risk":
+            st.markdown("""
+            <div class="recommendation-box recommendation-high">
+                <h3 style="color: #dc2626; font-size: 1.5rem; margin-bottom: 1rem;">IMMEDIATE MEDICAL ATTENTION REQUIRED</h3>
+                <ul style="font-size: 1.1rem; line-height: 1.8;">
+                    <li style="color:black;"><strong>Seek emergency medical care immediately</strong></li>
+                    <li style="color:black;" >Do not delay professional treatment</li>
+                    <li style="color:black;">Verify tetanus vaccination status with healthcare provider</li>
+                    <li style="color:black;">Clean wound with sterile saline if available</li>
+                    <li style="color:black;">Avoid home remedies - professional care is essential</li>
+                    <li style="color:black;">Monitor for signs of infection or tetanus symptoms</li>
+                </ul>
+            </div>
+            """, unsafe_allow_html=True)
+        elif predicted_label == "Mid Risk":
+            st.markdown("""
+            <div class="recommendation-box recommendation-mid">
+                <h3 style="color: #d97706; font-size: 1.5rem; margin-bottom: 1rem;">CLINICAL EVALUATION RECOMMENDED</h3>
+                <ul style="font-size: 1.1rem; line-height: 1.8;">
+                    <li style="color:black;"><strong>Clean wound thoroughly with soap and water</strong></li>
+                    <li style="color:black;">Monitor for signs of infection (redness, swelling, warmth)</li>
+                    <li style="color:black;">Consult healthcare provider within 24 hours</li>
+                    <li style="color:black;">Update tetanus vaccination if necessary (>5 years)</li>
+                    <li style="color:black;">Apply clean dressing and change regularly</li>
+                    <li style="color:black;">Take photos to track healing progress</li>
+                </ul>
+            </div>
+            """, unsafe_allow_html=True)
+        else:
+            st.markdown("""
+            <div class="recommendation-box recommendation-low">
+                <h3 style="color: #059669; font-size: 1.5rem; margin-bottom: 1rem;">STANDARD WOUND CARE PROTOCOL</h3>
+                <ul style="font-size: 1.1rem; line-height: 1.8; color:black;">
+                    <li style="color:black;"><strong>Clean wound gently with soap and water</strong></li>
+                    <li style="color:black;">Apply antiseptic and clean bandage</li>
+                    <li style="color:black;">Monitor for changes or infection signs</li>
+                    <li style="color:black;">Keep wound clean and dry</li>
+                    <li style="color:black;">Consider tetanus booster if >5 years since last vaccination</li>
+                    <li style="color:black;">Follow up if wound doesn't heal properly</li>
+                </ul>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Enhanced information section
+    st.markdown("---")
+    
+    info_col1, info_col2 = st.columns(2)
+    
+    with info_col1:
+        st.markdown("""
+        <div class="info-box">
+            <div class="info-title">System Overview</div>
+            <p><strong>AI Technology:</strong> Convolutional Neural Networks</p>
+            <p><strong>Processing:</strong> Real-time image analysis</p>
+            <p><strong>Classification:</strong> Three-tier risk assessment</p>
+            <p><strong>Guidelines:</strong> Evidence-based medical protocols</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with info_col2:
+        st.markdown("""
+        <div class="info-box">
+            <div class="info-title">Technical Specs</div>
+            <p><strong>Model Architecture:</strong> Deep CNN</p>
+            <p><strong>Input Resolution:</strong> 224×224 pixels</p>
+            <p><strong>Framework:</strong> TensorFlow/Keras</p>
+            <p><strong>Inference Time:</strong> <2 seconds</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+
+
+# ====== Run Application ======
+if __name__ == "__main__":
+    main()