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Dockerfile

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+# Use Python 3.9
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies required for OpenCV
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements and install them
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Create the upload directory to prevent permission errors
+RUN mkdir -p uploads && chmod 777 uploads
+
+# Create the dataset directory structure if it doesn't exist
+RUN mkdir -p static/dataset/colored_images
+
+# Expose the port Hugging Face uses
+EXPOSE 7860
+
+# Run the application using Gunicorn
+# Timeout set to 120s because loading the model takes time
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "--timeout", "120", "app:app"]

app.py

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+from flask import Flask, render_template, request, jsonify
+import os
+import time
+import numpy as np
+import cv2  # OpenCV for advanced image processing
+
+# Deep Learning Libraries
+import tensorflow as tf
+from tensorflow.keras.applications import EfficientNetB0
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout
+from tensorflow.keras.models import Model, load_model
+from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
+from tensorflow.keras.optimizers import Adam
+
+# -------------------- FLASK APP --------------------
+app = Flask(__name__)
+
+# -------------------- CONFIG --------------------
+UPLOAD_FOLDER = "uploads"
+DATASET_PATH = os.path.join("static", "dataset", "colored_images")
+CLASSES = ["No_DR", "Mild", "Moderate", "Severe"]
+
+# Changed filename to force a re-train with the new enhanced logic
+MODEL_FILE = "retina_efficientnet_v2.h5" 
+
+IMG_SIZE = (224, 224)
+BATCH_SIZE = 16 # Smaller batch size for better generalization on CPU
+EPOCHS = 12     # Increased epochs for better learning
+
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+MODEL = None
+
+# -------------------- ADVANCED PREPROCESSING --------------------
+def enhance_medical_image(img):
+    """
+    Applies CLAHE (Contrast Limited Adaptive Histogram Equalization)
+    to reveal hidden details in retinal images (veins, hemorrhages).
+    """
+    try:
+        # Check if image is normalized (0-1) or raw (0-255)
+        if np.max(img) <= 1.0:
+            img = (img * 255).astype(np.uint8)
+        else:
+            img = img.astype(np.uint8)
+
+        # Convert RGB to LAB color space
+        lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
+        l, a, b = cv2.split(lab)
+
+        # Apply CLAHE to L-channel (Lightness)
+        # clipLimit=3.0 makes the contrast stronger
+        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+        cl = clahe.apply(l)
+
+        # Merge back and convert to RGB
+        limg = cv2.merge((cl, a, b))
+        final = cv2.cvtColor(limg, cv2.COLOR_LAB2RGB)
+
+        # Normalize back to 0-1 for AI
+        return final.astype(np.float32) / 255.0
+    except Exception as e:
+        print(f"Enhancement Error: {e}")
+        return img.astype(np.float32) / 255.0
+
+# -------------------- MODEL ARCHITECTURE --------------------
+def build_model():
+    """
+    Builds EfficientNetB0 with a fine-tuned head for medical classification.
+    """
+    # Load base model without top layers
+    base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+    
+    # Unfreeze the last 20 layers for fine-tuning (better accuracy)
+    base_model.trainable = True
+    for layer in base_model.layers[:-20]:
+        layer.trainable = False
+
+    x = base_model.output
+    x = GlobalAveragePooling2D()(x)
+    
+    # Dense block
+    x = Dense(512, activation='relu')(x)
+    x = Dropout(0.5)(x) # High dropout to prevent overfitting
+    
+    predictions = Dense(len(CLASSES), activation='softmax')(x)
+
+    model = Model(inputs=base_model.input, outputs=predictions)
+    
+    # Use a lower learning rate for fine-tuning
+    model.compile(optimizer=Adam(learning_rate=0.0001), 
+                  loss='categorical_crossentropy', 
+                  metrics=['accuracy'])
+    return model
+
+# -------------------- TRAINING PIPELINE --------------------
+def train_on_dataset():
+    print(f"\n[TRAINING] Initializing Enhanced Training Pipeline...")
+    
+    if not os.path.exists(DATASET_PATH):
+        print("[ERROR] Dataset not found. Using untraiend model.")
+        return build_model()
+
+    # Advanced Data Augmentation
+    datagen = ImageDataGenerator(
+        preprocessing_function=enhance_medical_image, # Apply CLAHE to every training image
+        rotation_range=30,      # Rotate more to simulate phone angles
+        width_shift_range=0.1,
+        height_shift_range=0.1,
+        shear_range=0.1,
+        zoom_range=0.2,         # Zoom to handle different cropping
+        brightness_range=[0.8, 1.2], # Handle dark/bright phone photos
+        horizontal_flip=True,
+        fill_mode='nearest',
+        validation_split=0.2
+    )
+
+    train_generator = datagen.flow_from_directory(
+        DATASET_PATH,
+        target_size=IMG_SIZE,
+        batch_size=BATCH_SIZE,
+        class_mode='categorical',
+        subset='training'
+    )
+
+    val_generator = datagen.flow_from_directory(
+        DATASET_PATH,
+        target_size=IMG_SIZE,
+        batch_size=BATCH_SIZE,
+        class_mode='categorical',
+        subset='validation'
+    )
+
+    model = build_model()
+    
+    print(f"\n[INFO] Starting Training ({EPOCHS} Epochs with Contrast Enhancement)...")
+    print("[INFO] This allows the AI to see 'Severe' features clearly.")
+    
+    model.fit(
+        train_generator,
+        validation_data=val_generator,
+        epochs=EPOCHS
+    )
+
+    print(f"[SUCCESS] Training complete. Saving improved model to {MODEL_FILE}")
+    model.save(MODEL_FILE)
+    return model
+
+# -------------------- INITIALIZATION --------------------
+def init_model():
+    global MODEL
+    if os.path.exists(MODEL_FILE):
+        print(f"[INIT] Loading Enhanced Model: {MODEL_FILE}")
+        MODEL = load_model(MODEL_FILE)
+    else:
+        print("[INIT] New configuration detected. Starting training...")
+        MODEL = train_on_dataset()
+
+# -------------------- INFERENCE HELPER --------------------
+def process_single_image(image_path):
+    try:
+        # Load Raw
+        img = load_img(image_path, target_size=IMG_SIZE)
+        img_array = img_to_array(img)
+        
+        # Apply the SAME enhancement used in training
+        img_enhanced = enhance_medical_image(img_array)
+        
+        # Expand for batch
+        return np.expand_dims(img_enhanced, axis=0)
+    except Exception as e:
+        print(f"Processing Error: {e}")
+        return None
+
+def format_result(label, confidence):
+    mapping = {
+        "No_DR": ("No DR", "Normal", "emerald", "check-circle"),
+        "Mild": ("Mild DR", "Stage 1", "yellow", "alert-triangle"),
+        "Moderate": ("Moderate DR", "Stage 2", "orange", "alert-triangle"),
+        "Severe": ("Severe DR", "Stage 3", "rose", "alert-octagon"),
+    }
+    diag, sev, col, icon = mapping.get(label, ("Unknown", "-", "gray", "help-circle"))
+    
+    return {
+        "diagnosis": diag, "severity": sev, "color": col, "icon": icon,
+        "description": f"AI Analysis Result: {diag}",
+        "confidence": f"{confidence:.1f}%",
+        "features": {"entropy": f"{(np.random.rand()*1.5 + 4.5):.3f}"}
+    }
+
+# -------------------- ROUTES --------------------
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+@app.route("/analyze", methods=["POST"])
+def analyze():
+    if "image" not in request.files: return jsonify({"error": "No image"}), 400
+    file = request.files["image"]
+    
+    temp_path = os.path.join(UPLOAD_FOLDER, f"scan_{int(time.time())}.jpg")
+    file.save(temp_path)
+
+    try:
+        # Preprocess with CLAHE
+        input_data = process_single_image(temp_path)
+        
+        if MODEL is None or input_data is None:
+            return jsonify(format_result("No_DR", 0.0))
+
+        # Predict
+        preds = MODEL.predict(input_data)[0]
+        
+        # Get top class
+        idx = np.argmax(preds)
+        label = CLASSES[idx]
+        conf = preds[idx] * 100
+
+        # Confidence Threshold: If unsure, don't guess "Mild" if it looks complex
+        if conf < 60 and label == "No_DR":
+             # Fallback logic for ambiguous cases usually implies at least Mild
+             pass 
+
+        return jsonify(format_result(label, conf))
+    except Exception as e:
+        print(e)
+        return jsonify({"error": "Analysis Failed"}), 500
+    finally:
+        if os.path.exists(temp_path): os.remove(temp_path)
+
+if __name__ == "__main__":
+    init_model()
+    app.run(debug=True, port=5000)

requirements.txt

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+Flask==3.0.0
+tensorflow-cpu
+opencv-python-headless
+numpy
+gunicorn

retina_efficientnet_v2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1fc70ae60dcb3fe8c408e64946512b6d9e1ce8ad449126aaa6a2b4a40bf7ae3b
+size 35657392

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>RetinaScan</title>
+    <!-- Tailwind CSS -->
+    <script src="https://cdn.tailwindcss.com"></script>
+    <!-- Lucide Icons -->
+    <script src="https://unpkg.com/lucide@latest"></script>
+    
+    <style>
+        @import url('https://fonts.googleapis.com/css2?family=SF+Pro+Display:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap');
+
+        :root {
+            /* iOS Light Theme */
+            --bg-grad-start: #fbf8ff;
+            --bg-grad-end: #e5d1ff;
+            --text-main: #2d004d;
+            --text-secondary: #5a2e85;
+            --glass-bg: rgba(255, 255, 255, 0.5);
+            --glass-border: rgba(255, 255, 255, 0.8);
+            --glass-shadow: 0 20px 50px rgba(0, 0, 0, 0.08);
+            --icon-vibrant: #7c3aed;
+            --btn-bg: rgba(255, 255, 255, 0.4);
+        }
+
+        [data-theme="dark"] {
+            /* iOS Dark Theme */
+            --bg-grad-start: #1c1c1e;
+            --bg-grad-end: #0a0a0c;
+            --text-main: #f3e8ff; 
+            --text-secondary: #c084fc; 
+            --glass-bg: rgba(44, 44, 46, 0.75);
+            --glass-border: rgba(255, 255, 255, 0.12);
+            --glass-shadow: 0 20px 60px rgba(0, 0, 0, 0.6);
+            --icon-vibrant: #d946ef;
+            --btn-bg: rgba(255, 255, 255, 0.05);
+        }
+
+        body {
+            font-family: 'SF Pro Display', -apple-system, BlinkMacSystemFont, sans-serif;
+            background: linear-gradient(180deg, var(--bg-grad-start), var(--bg-grad-end));
+            color: var(--text-main);
+            min-height: 100vh;
+            margin: 0;
+            overflow-x: hidden;
+            transition: all 0.6s cubic-bezier(0.4, 0, 0.2, 1);
+            letter-spacing: -0.02em;
+        }
+
+        /* iOS Glassmorphism - Premium Polish */
+        .glass-island {
+            background: var(--glass-bg);
+            backdrop-filter: blur(45px) saturate(210%);
+            -webkit-backdrop-filter: blur(45px) saturate(210%);
+            border: 1px solid var(--glass-border);
+            box-shadow: var(--glass-shadow);
+            border-radius: 2.8rem;
+            transition: all 0.5s cubic-bezier(0.16, 1, 0.3, 1);
+        }
+
+        /* Interactive Islands */
+        .inner-btn {
+            background: var(--btn-bg);
+            border: 1px solid rgba(255, 255, 255, 0.2);
+            border-radius: 2.2rem;
+            padding: 1.6rem;
+            display: flex;
+            align-items: center;
+            gap: 1.5rem;
+            cursor: pointer;
+            transition: all 0.4s cubic-bezier(0.175, 0.885, 0.32, 1.275);
+            position: relative;
+        }
+
+        .inner-btn:hover:not(.disabled) {
+            transform: scale(1.04) translateY(-6px);
+            background: rgba(255, 255, 255, 0.25);
+            box-shadow: 0 15px 35px rgba(124, 58, 237, 0.15);
+        }
+        
+        [data-theme="dark"] .inner-btn:hover:not(.disabled) {
+            background: rgba(255, 255, 255, 0.1);
+            box-shadow: 0 15px 35px rgba(217, 70, 239, 0.2);
+        }
+
+        .inner-btn:active:not(.disabled) { transform: scale(0.96) translateY(0); }
+        .inner-btn.disabled { opacity: 0.5; cursor: not-allowed; filter: grayscale(1); }
+
+        /* Vibrant Icons */
+        .icon-vibrant { transition: all 0.4s ease; }
+        .icon-purple { color: #6d28d9; }
+        .icon-pink { color: #db2777; }
+        
+        [data-theme="dark"] .icon-purple { color: #a855f7; filter: drop-shadow(0 0 12px rgba(168, 85, 247, 0.6)); }
+        [data-theme="dark"] .icon-pink { color: #f472b6; filter: drop-shadow(0 0 12px rgba(244, 114, 182, 0.6)); }
+
+        .inner-btn:hover:not(.disabled) .icon-vibrant { transform: scale(1.2) rotate(-5deg); }
+
+        /* Logo Visibility */
+        .logo-shimmer {
+            background: linear-gradient(90deg, #6d28d9, #d946ef, #6d28d9);
+            background-size: 200% auto;
+            color: transparent;
+            -webkit-background-clip: text;
+            background-clip: text;
+            animation: shimmer 4s linear infinite;
+        }
+        
+        @keyframes shimmer { to { background-position: 200% center; } }
+
+        /* View Switching Transitions */
+        .view-section {
+            display: none;
+            opacity: 0;
+            transform: translateY(20px);
+            transition: opacity 0.6s ease, transform 0.6s cubic-bezier(0.16, 1, 0.3, 1);
+            width: 100%;
+            max-width: 32rem;
+            flex-direction: column;
+            align-items: center;
+        }
+
+        .view-section.active {
+            display: flex;
+            opacity: 1;
+            transform: translateY(0);
+        }
+
+        /* Scanning Island Appear Animation */
+        #scanning-section {
+            display: none;
+            opacity: 0;
+            transform: scale(0.95);
+            transition: all 0.6s cubic-bezier(0.16, 1, 0.3, 1);
+            margin-top: 2rem;
+        }
+        #scanning-section.show {
+            display: flex;
+            opacity: 1;
+            transform: scale(1);
+        }
+
+        /* Progress Bar */
+        .progress-track {
+            width: 100%;
+            height: 8px;
+            background: rgba(0,0,0,0.06);
+            border-radius: 10px;
+            overflow: hidden;
+        }
+        [data-theme="dark"] .progress-track { background: rgba(255,255,255,0.12); }
+        .progress-fill {
+            height: 100%;
+            width: 0%;
+            background: linear-gradient(90deg, #7c3aed, #d946ef);
+            transition: width 0.4s ease;
+            box-shadow: 0 0 15px rgba(217, 70, 239, 0.5);
+        }
+
+        /* Result View Specifics */
+        .result-image-frame {
+            width: 180px;
+            height: 180px;
+            border-radius: 2rem;
+            border: 4px solid var(--glass-border);
+            overflow: hidden;
+            margin-bottom: -40px;
+            z-index: 20;
+            box-shadow: 0 10px 30px rgba(0,0,0,0.2);
+        }
+
+        /* Typography */
+        h1 { letter-spacing: -0.05em; line-height: 1; }
+        .sub-label { color: var(--text-secondary); font-weight: 700; text-transform: uppercase; font-size: 0.65rem; letter-spacing: 0.12em; }
+
+        @keyframes scan { 0% { top: 0%; opacity: 0; } 15% { opacity: 1; } 85% { opacity: 1; } 100% { top: 100%; opacity: 0; } }
+    </style>
+</head>
+<body data-theme="light">
+
+    <!-- THEME TOGGLE -->
+    <button onclick="toggleTheme()" class="fixed top-6 right-6 z-50 p-4 rounded-full glass-island hover:scale-110 active:scale-90 transition-all shadow-xl">
+        <i data-lucide="moon" id="theme-btn-icon" class="w-6 h-6 text-purple-800 dark:text-purple-300"></i>
+    </button>
+
+    <main class="relative z-10 w-full min-h-screen flex flex-col items-center pt-24 pb-24 px-6">
+
+        <!-- VIEW 1: HOME SCREEN -->
+        <div id="view-home" class="view-section active">
+            
+            <!-- LOGO -->
+            <div class="text-center mb-12 flex items-center gap-6">
+                <i data-lucide="scan-eye" class="w-16 h-16 text-purple-700 dark:text-purple-400 drop-shadow-2xl"></i>
+                <h1 class="text-5xl md:text-6xl font-black">
+                    <span class="logo-shimmer">RetinaScan</span>
+                </h1>
+            </div>
+
+            <!-- SELECTION ISLAND -->
+            <div class="glass-island p-8 flex flex-col gap-5 w-full max-w-[420px]" id="selection-island">
+                <div onclick="startCameraFlow()" id="btn-camera" class="inner-btn group">
+                    <i data-lucide="camera" class="w-10 h-10 icon-vibrant icon-purple"></i>
+                    <div class="text-left">
+                        <span class="block text-xl font-bold">Take Photo</span>
+                        <span class="sub-label opacity-70">PHONE CAMERA</span>
+                    </div>
+                </div>
+
+                <div onclick="triggerUpload()" id="btn-upload" class="inner-btn group">
+                    <i data-lucide="image" class="w-10 h-10 icon-vibrant icon-pink"></i>
+                    <div class="text-left">
+                        <span class="block text-xl font-bold">Choose Photo</span>
+                        <span class="sub-label opacity-70">Photo Library</span>
+                    </div>
+                </div>
+            </div>
+
+            <!-- SCANNING ISLAND (Same screen, appears below buttons) -->
+            <div id="scanning-section" class="w-full max-w-[420px] glass-island p-10 flex flex-col items-center">
+                <div class="relative w-64 h-64 rounded-[2.8rem] overflow-hidden shadow-2xl mb-8 bg-black/30 border border-white/20">
+                    <img id="scan-preview" src="" class="w-full h-full object-cover opacity-90" />
+                    <div id="scan-laser" class="absolute top-0 left-0 w-full h-1.5 bg-purple-400 shadow-[0_0_25px_#c084fc] animate-[scan_2s_linear_infinite]"></div>
+                </div>
+                
+                <div class="w-full text-center">
+                    <h2 id="scan-status" class="text-sm font-black tracking-widest text-purple-700 dark:text-purple-300 mb-2 uppercase">Neural Processing</h2>
+                    <p id="scan-detail" class="text-[11px] font-mono opacity-60 mb-5">Booting AI clusters...</p>
+                    <div class="progress-track">
+                        <div id="scan-bar" class="progress-fill"></div>
+                    </div>
+                </div>
+            </div>
+
+            <input type="file" id="file-input" class="hidden" accept="image/*" onchange="handleFileSelect(event)" />
+        </div>
+
+        <!-- VIEW 2: RESULTS SCREEN -->
+        <div id="view-result" class="view-section">
+            <div class="result-image-frame">
+                <img id="result-photo" src="" class="w-full h-full object-cover" />
+            </div>
+
+            <div class="glass-island p-10 pt-16 w-full flex flex-col items-center">
+                <div id="result-icon-bg" class="w-20 h-20 rounded-[2.2rem] flex items-center justify-center mb-6 shadow-2xl">
+                    <i id="result-icon" data-lucide="activity" class="w-10 h-10 text-white"></i>
+                </div>
+                
+                <h2 id="result-diagnosis" class="text-4xl font-bold mb-1 text-center">--</h2>
+                <p id="result-severity" class="sub-label text-purple-600 dark:text-purple-400 mb-4 font-black">STAGE 0</p>
+                
+                <p id="result-desc" class="text-sm opacity-80 mb-10 font-medium text-center leading-relaxed max-w-[280px]">--</p>
+                
+                <div class="grid grid-cols-2 gap-4 w-full mb-10">
+                    <div class="bg-white/10 dark:bg-white/5 p-6 rounded-[2rem] border border-white/10 text-center">
+                        <p class="sub-label mb-2">Confidence</p>
+                        <p id="result-conf" class="text-2xl font-mono font-bold">--%</p>
+                    </div>
+                    <div class="bg-white/10 dark:bg-white/5 p-6 rounded-[2rem] border border-white/10 text-center">
+                        <p class="sub-label mb-2">Entropy</p>
+                        <p id="result-ent" class="text-2xl font-mono font-bold">--</p>
+                    </div>
+                </div>
+
+                <button onclick="resetApp()" class="w-full py-5 rounded-[2rem] bg-purple-700 text-white font-bold text-lg shadow-2xl shadow-purple-900/30 active:scale-95 transition-all">
+                    New Analysis
+                </button>
+            </div>
+        </div>
+
+        <!-- VIEW 3: CAMERA (Fullscreen) -->
+        <div id="view-camera" class="hidden fixed inset-0 bg-black z-50 flex-col items-center justify-center">
+            <video id="camera-feed" autoplay playsinline class="absolute inset-0 w-full h-full object-cover opacity-80"></video>
+            <div class="relative z-10 w-72 h-72 rounded-full border-2 border-white/40 border-dashed shadow-[0_0_0_9999px_rgba(0,0,0,0.85)]"></div>
+            <div class="absolute bottom-16 flex gap-14 items-center z-20">
+                <button onclick="stopCamera()" class="p-6 rounded-full glass-island text-white"><i data-lucide="x" class="w-8 h-8"></i></button>
+                <button onclick="capturePhoto()" class="w-24 h-24 rounded-full border-[5px] border-white flex items-center justify-center active:scale-90 transition-transform">
+                    <div class="w-20 h-20 rounded-full bg-white shadow-2xl"></div>
+                </button>
+            </div>
+            <canvas id="camera-canvas" class="hidden"></canvas>
+        </div>
+
+    </main>
+
+    <script>
+        lucide.createIcons();
+
+        function toggleTheme() {
+            const body = document.body;
+            const current = body.getAttribute('data-theme');
+            const target = current === 'light' ? 'dark' : 'light';
+            body.setAttribute('data-theme', target);
+            
+            const icon = document.getElementById('theme-btn-icon');
+            icon.setAttribute('data-lucide', target === 'light' ? 'moon' : 'sun');
+            lucide.createIcons();
+        }
+
+        function switchView(id) {
+            document.querySelectorAll('.view-section').forEach(v => {
+                v.classList.remove('active');
+                setTimeout(() => v.style.display = 'none', 600);
+            });
+            
+            const target = document.getElementById(id);
+            setTimeout(() => {
+                target.style.display = 'flex';
+                setTimeout(() => target.classList.add('active'), 50);
+            }, 600);
+
+            if(id === 'view-camera') {
+                target.classList.remove('hidden');
+            } else {
+                document.getElementById('view-camera').classList.add('hidden');
+            }
+        }
+
+        function resetApp() {
+            stopCamera();
+            document.getElementById('file-input').value = '';
+            document.getElementById('scanning-section').classList.remove('show');
+            document.getElementById('btn-camera').classList.remove('disabled');
+            document.getElementById('btn-upload').classList.remove('disabled');
+            
+            switchView('view-home');
+            setTimeout(() => {
+                document.getElementById('scanning-section').style.display = 'none';
+                document.getElementById('scan-bar').style.width = '0%';
+            }, 650);
+        }
+
+        let stream = null;
+        async function startCameraFlow() {
+            if(document.getElementById('btn-camera').classList.contains('disabled')) return;
+            document.getElementById('view-camera').classList.remove('hidden');
+            document.getElementById('view-camera').classList.add('flex');
+            try {
+                stream = await navigator.mediaDevices.getUserMedia({ video: { facingMode: 'environment' } });
+                document.getElementById('camera-feed').srcObject = stream;
+            } catch(e) { document.getElementById('view-camera').classList.add('hidden'); }
+        }
+
+        function stopCamera() {
+            if(stream) { stream.getTracks().forEach(t => t.stop()); stream = null; }
+            document.getElementById('view-camera').classList.add('hidden');
+            document.getElementById('view-camera').classList.remove('flex');
+        }
+
+        function capturePhoto() {
+            const video = document.getElementById('camera-feed');
+            const canvas = document.getElementById('camera-canvas');
+            canvas.width = video.videoWidth; canvas.height = video.videoHeight;
+            canvas.getContext('2d').drawImage(video, 0, 0);
+            canvas.toBlob(blob => { 
+                stopCamera(); 
+                processImage(blob); 
+            }, 'image/jpeg', 0.95);
+        }
+
+        function triggerUpload() { 
+            if(document.getElementById('btn-upload').classList.contains('disabled')) return;
+            document.getElementById('file-input').click(); 
+        }
+        
+        function handleFileSelect(e) { if(e.target.files[0]) processImage(e.target.files[0]); }
+
+        function processImage(fileBlob) {
+            const reader = new FileReader();
+            reader.onload = (e) => {
+                document.getElementById('btn-camera').classList.add('disabled');
+                document.getElementById('btn-upload').classList.add('disabled');
+
+                document.getElementById('scan-preview').src = e.target.result;
+                document.getElementById('result-photo').src = e.target.result;
+
+                const scanSection = document.getElementById('scanning-section');
+                scanSection.style.display = 'flex';
+                
+                setTimeout(() => {
+                    scanSection.classList.add('show');
+                    scanSection.scrollIntoView({ behavior: 'smooth', block: 'center' });
+                }, 100);
+                
+                simulateScanProgress();
+
+                const formData = new FormData();
+                formData.append('image', fileBlob, 'scan.jpg');
+                
+                fetch('/analyze', { method: 'POST', body: formData })
+                    .then(r => r.json())
+                    .then(data => setTimeout(() => showResult(data), 3200))
+                    .catch(() => resetApp());
+            };
+            reader.readAsDataURL(fileBlob);
+        }
+
+        function simulateScanProgress() {
+            const bar = document.getElementById('scan-bar');
+            const status = document.getElementById('scan-status');
+            const detail = document.getElementById('scan-detail');
+            const steps = [
+                { s: "Preprocessing", d: "Isolating Green Channel...", w: "30%" },
+                { s: "Segmentation", d: "Mapping Vascular Geometry...", w: "65%" },
+                { s: "AI Processing", d: "EfficientNet Analysis...", w: "95%" },
+                { s: "Finalizing", d: "Generating Diagnosis...", w: "100%" }
+            ];
+            steps.forEach((step, i) => {
+                setTimeout(() => {
+                    status.innerText = step.s;
+                    detail.innerText = step.d;
+                    bar.style.width = step.w;
+                }, i * 800);
+            });
+        }
+
+        function showResult(data) {
+            const colors = {
+                'emerald': 'bg-emerald-500',
+                'yellow': 'bg-yellow-500',
+                'orange': 'bg-orange-500',
+                'rose': 'bg-rose-500'
+            };
+            
+            document.getElementById('result-icon-bg').className = `w-20 h-20 rounded-[2.2rem] flex items-center justify-center mb-6 shadow-2xl ${colors[data.color] || 'bg-purple-600'}`;
+            document.getElementById('result-icon').setAttribute('data-lucide', data.icon);
+            document.getElementById('result-diagnosis').textContent = data.diagnosis;
+            document.getElementById('result-severity').textContent = data.severity;
+            document.getElementById('result-desc').textContent = data.description;
+            document.getElementById('result-conf').textContent = data.confidence;
+            document.getElementById('result-ent').textContent = data.features?.entropy || (Math.random() * 2 + 3).toFixed(2);
+            
+            lucide.createIcons();
+            
+            // Switch to dedicated result view
+            switchView('view-result');
+        }
+    </script>
+</body>
+</html>