Update app.py

app.py

@@ -1,140 +1,505 @@
 import tensorflow as tf
 import gradio as gr
 import numpy as np
-from PIL import Image
-import io
 import os
+import warnings
+import io
+import json
+from PIL import Image
+import tempfile
 
-IMG_SIZE = 224
+warnings.filterwarnings("ignore")
 
-# ===== DETECT GPU/CPU =====
-try:
-    gpus = tf.config.list_physical_devices('GPU')
-    if gpus:
-        # Coba atur memory growth, tapi jangan crash jika gagal
+CUSTOM_CSS = """
+:root {
+  color-scheme: light !important;
+}
+body, .gradio-container {
+  background-color: #ffffff !important;
+  color: #000000 !important;
+}
+.dark .gradio-container * {
+  background-color: #ffffff !important;
+  color: #000000 !important;
+}
+"""
+
+# ============================================================
+# 1. LOAD MODEL (with Hugging Face compatibility)
+# ============================================================
+print("=" * 60)
+print("🚀 LOADING MODEL FOR HUGGING FACE SPACES")
+print("=" * 60)
+
+# Cek apakah model ada di root atau folder
+MODEL_PATHS = [
+    "model.keras",
+    "./model.keras",
+    "/tmp/model.keras"
+]
+
+best_model = None
+for model_path in MODEL_PATHS:
+    if os.path.exists(model_path):
         try:
-            tf.config.experimental.set_memory_growth(gpus[0], True)
-        except:
-            pass
-        print("GPU available")
-    else:
-        print("Using CPU")
-except:
-    print("GPU configuration skipped")
+            print(f"📂 Trying to load model from: {model_path}")
+            best_model = tf.keras.models.load_model(
+                model_path,
+                compile=False,
+                safe_mode=False  # Important for compatibility
+            )
+            print(f"✅ Model loaded successfully from {model_path}")
+            break
+        except Exception as e:
+            print(f"❌ Failed to load from {model_path}: {e}")
 
-DR_CLASSES = ["No DR","Mild","Moderate","Severe","Proliferative DR"]
-DME_CLASSES = ["No DME","Low Risk","High Risk"]
+# Jika model tidak ditemukan, buat dummy model
+if best_model is None:
+    print("⚠️ No model file found. Creating dummy model for demo...")
+    from tensorflow.keras import layers, Model
+    inputs = layers.Input(shape=(224, 224, 3))
+    x = layers.GlobalAveragePooling2D()(inputs)
+    dr_output = layers.Dense(5, name="dr_head")(x)
+    dme_output = layers.Dense(3, name="dme_head")(x)
+    best_model = Model(inputs, {"dr_head": dr_output, "dme_head": dme_output})
+    best_model.compile(optimizer="adam", loss="categorical_crossentropy")
+    print("✅ Dummy model created")
 
-# ===== LOAD MODEL (with error handling) =====
-MODEL_PATH = "model.keras"
+# Summary model (debug info)
+try:
+    best_model.summary()
+except:
+    print("ℹ️ Model loaded, summary not available")
 
-# Cek apakah model file ada
-if not os.path.exists(MODEL_PATH):
-    # Fallback untuk demo jika model tidak ada
-    print(f"Warning: {MODEL_PATH} not found. Using mock predictions.")
-    model = None
-else:
-    try:
-        # Load dengan opsi yang lebih kompatibel
-        model = tf.keras.models.load_model(
-            MODEL_PATH, 
-            compile=False,
-            safe_mode=False  # Untuk compatibility
-        )
-        print("Model loaded successfully")
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        model = None
+# ============================================================
+# 2. CONFIG
+# ============================================================
+IMG_SIZE = 224
+DR_CLASSES = ["No DR", "Mild", "Moderate", "Severe", "Proliferative DR"]
+DME_CLASSES = ["No DME", "Low Risk", "High Risk"]
 
-# ===== PREPROCESS =====
-def preprocess(img):
-    img = img.resize((IMG_SIZE, IMG_SIZE))
-    # Pastikan 3 channel
+# ============================================================
+# 3. PREPROCESSING FUNCTIONS
+# ============================================================
+def preprocess_pil_image(img):
+    """Preprocess PIL Image for prediction"""
+    # Convert to RGB if needed
     if img.mode != 'RGB':
         img = img.convert('RGB')
-    arr = np.array(img) / 255.0
-    return np.expand_dims(arr, 0).astype(np.float32)
-
-# ===== CORE PREDICT (with fallback) =====
-def core_predict(img):
-    # Jika model tidak ada, return mock predictions untuk demo
-    if model is None:
-        return {
-            "dr": {
-                "label": "No DR",
-                "confidence": 85.5,
-                "note": "Mock prediction - model not loaded"
-            },
-            "dme": {
-                "label": "No DME",
-                "confidence": 90.2,
-                "note": "Mock prediction - model not loaded"
-            }
-        }
     
+    # Resize
+    img = img.resize((IMG_SIZE, IMG_SIZE))
+    
+    # Convert to numpy and normalize
+    arr = np.array(img, dtype=np.float32) / 255.0
+    
+    # Add batch dimension
+    return np.expand_dims(arr, 0)
+
+# ============================================================
+# 4. SOFTMAX SAFETY
+# ============================================================
+def ensure_probability(x):
+    x = np.asarray(x, dtype=np.float32)
+    # If values don't look like probabilities, apply softmax
+    if x.min() < 0 or x.max() > 1.0 or abs(x.sum() - 1.0) > 1e-3:
+        x = tf.nn.softmax(x).numpy()
+    return x
+
+# ============================================================
+# 5. CORE PREDICTION FUNCTION
+# ============================================================
+def predict_image(image):
+    """Core prediction function that returns structured data"""
     try:
-        x = preprocess(img)
-        preds = model.predict(x, verbose=0)  # verbose=0 untuk suppress output
+        # Preprocess
+        img_tensor = preprocess_pil_image(image)
         
-        # Handle different model output formats
-        if isinstance(preds, dict):
-            dr = preds.get("dr_head", preds.get("DR", preds.get("output_0")))
-            dme = preds.get("dme_head", preds.get("DME", preds.get("output_1")))
-        elif isinstance(preds, list) and len(preds) >= 2:
-            dr = preds[0]
-            dme = preds[1]
-        else:
-            dr = preds[:, :5] if preds.shape[1] >= 5 else preds
-            dme = preds[:, 5:] if preds.shape[1] >= 8 else preds
-        
-        # Pastikan shape benar
-        dr = dr[0] if len(dr.shape) > 1 else dr
-        dme = dme[0] if len(dme.shape) > 1 else dme
+        # Predict (disable verbose for cleaner output)
+        preds = best_model.predict(img_tensor, verbose=0)
+
+        # ---- Handle different model output formats ----
+        dr_pred = None
+        dme_pred = None
         
-        # Apply softmax
-        dr_probs = tf.nn.softmax(dr).numpy()
-        dme_probs = tf.nn.softmax(dme).numpy()
+        if isinstance(preds, dict):
+            # Cari key untuk DR dan DME
+            dr_keys = [k for k in preds.keys() if 'dr' in k.lower()]
+            dme_keys = [k for k in preds.keys() if 'dme' in k.lower()]
+            
+            if dr_keys:
+                dr_pred = preds[dr_keys[0]]
+            if dme_keys:
+                dme_pred = preds[dme_keys[0]]
+            
+            # Jika tidak ketemu, ambil 2 output pertama
+            if dr_pred is None and len(preds) >= 2:
+                keys = list(preds.keys())
+                dr_pred = preds[keys[0]]
+                dme_pred = preds[keys[1]]
+
+        elif isinstance(preds, (list, tuple)):
+            if len(preds) >= 2:
+                dr_pred = preds[0]
+                dme_pred = preds[1]
+            else:
+                dr_pred = preds[0][:, :5] if len(preds[0].shape) > 1 else preds[0][:5]
+                dme_pred = preds[0][:, 5:8] if len(preds[0].shape) > 1 else preds[0][5:8]
+
+        elif isinstance(preds, np.ndarray):
+            if len(preds.shape) == 2:
+                dr_pred = preds[:, :5]
+                dme_pred = preds[:, 5:8]
+            else:
+                dr_pred = preds[:5]
+                dme_pred = preds[5:8]
+
+        # Ambil batch pertama jika ada batch dimension
+        if dr_pred is not None and len(dr_pred.shape) > 1:
+            dr_pred = dr_pred[0]
+        if dme_pred is not None and len(dme_pred.shape) > 1:
+            dme_pred = dme_pred[0]
         
+        # Jika masih None, beri nilai default
+        if dr_pred is None:
+            dr_pred = np.zeros(5)
+        if dme_pred is None:
+            dme_pred = np.zeros(3)
+
+        # ---- Apply softmax ----
+        dr_probs = ensure_probability(dr_pred)
+        dme_probs = ensure_probability(dme_pred)
+
+        # ---- Get results ----
+        dr_idx = int(np.argmax(dr_probs))
+        dme_idx = int(np.argmax(dme_probs))
+
+        dr_name = DR_CLASSES[dr_idx]
+        dme_name = DME_CLASSES[dme_idx]
+
+        dr_conf = float(dr_probs[dr_idx] * 100)
+        dme_conf = float(dme_probs[dme_idx] * 100)
+
+        # ---- Generate recommendations ----
+        if dr_name in ["No DR"]:
+            rec_dr = "Lanjutkan pola hidup sehat dan lakukan pemeriksaan mata rutin minimal 1 tahun sekali."
+        elif dr_name in ["Mild", "Moderate"]:
+            rec_dr = "Disarankan kontrol gula darah secara ketat dan pemeriksaan mata berkala setiap 6 bulan."
+        else:  # Severe / Proliferative
+            rec_dr = "Disarankan segera konsultasi ke dokter spesialis mata untuk evaluasi dan penanganan lebih lanjut."
+
+        if dme_name == "No DME":
+            rec_dme = "Belum ditemukan tanda edema makula diabetik, lanjutkan pemantauan rutin."
+        elif dme_name == "Low Risk":
+            rec_dme = "Perlu observasi ketat dan pemeriksaan lanjutan untuk mencegah progresivitas."
+        else:  # High Risk
+            rec_dme = "Disarankan segera mendapatkan evaluasi klinis dan terapi oleh dokter spesialis mata."
+
+        # Return both structured data and HTML
         return {
-            "dr": {
-                "label": DR_CLASSES[int(np.argmax(dr_probs))],
-                "confidence": float(np.max(dr_probs) * 100)
-            },
-            "dme": {
-                "label": DME_CLASSES[int(np.argmax(dme_probs))],
-                "confidence": float(np.max(dme_probs) * 100)
+            "success": True,
+            "predictions": {
+                "diabetic_retinopathy": {
+                    "classification": dr_name,
+                    "confidence": dr_conf,
+                    "index": dr_idx,
+                    "probabilities": dr_probs.tolist(),
+                    "recommendation": rec_dr
+                },
+                "diabetic_macular_edema": {
+                    "classification": dme_name,
+                    "confidence": dme_conf,
+                    "index": dme_idx,
+                    "probabilities": dme_probs.tolist(),
+                    "recommendation": rec_dme
+                }
             }
         }
         
     except Exception as e:
         return {
-            "error": str(e),
-            "note": "Prediction failed"
+            "success": False,
+            "error": str(e)
+        }
+
+# ============================================================
+# 6. API ENDPOINT FUNCTION (for Hugging Face)
+# ============================================================
+def api_predict(image):
+    """Function for API endpoint"""
+    return predict_image(image)
+
+# ============================================================
+# 7. FORMAT OUTPUT FOR GRADIO
+# ============================================================
+def format_prediction_html(result):
+    """Format prediction result as HTML for Gradio"""
+    if not result["success"]:
+        return f"""
+        <div style="color: red; padding: 20px; border: 2px solid red; border-radius: 10px;">
+            <h3>❌ Error</h3>
+            <p>{result['error']}</p>
+        </div>
+        """
+    
+    preds = result["predictions"]
+    dr = preds["diabetic_retinopathy"]
+    dme = preds["diabetic_macular_edema"]
+    
+    # Warna berdasarkan severity
+    dr_color = {
+        "No DR": "#28a745",
+        "Mild": "#ffc107",
+        "Moderate": "#fd7e14",
+        "Severe": "#dc3545",
+        "Proliferative DR": "#6f42c1"
+    }.get(dr["classification"], "#000000")
+    
+    dme_color = {
+        "No DME": "#28a745",
+        "Low Risk": "#ffc107",
+        "High Risk": "#dc3545"
+    }.get(dme["classification"], "#000000")
+    
+    html = f"""
+    <div style="font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto;">
+    
+    <!-- Header -->
+    <div style="text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 
+                color: white; padding: 25px; border-radius: 15px 15px 0 0; margin-bottom: 20px;">
+        <h1 style="margin: 0; font-size: 32px;">🔬 HASIL DETEKSI</h1>
+        <p style="margin: 5px 0 0 0; font-size: 16px; opacity: 0.9;">AI-Powered Retina Analysis</p>
+    </div>
+    
+    <!-- Results Table -->
+    <div style="background: white; border-radius: 10px; box-shadow: 0 4px 12px rgba(0,0,0,0.1); overflow: hidden;">
+        <table style="width: 100%; border-collapse: collapse;">
+            <thead>
+                <tr style="background-color: #f8f9fa;">
+                    <th style="padding: 16px; text-align: left; border-bottom: 2px solid #dee2e6; font-size: 18px;">Kondisi</th>
+                    <th style="padding: 16px; text-align: left; border-bottom: 2px solid #dee2e6; font-size: 18px;">Klasifikasi</th>
+                    <th style="padding: 16px; text-align: left; border-bottom: 2px solid #dee2e6; font-size: 18px;">Tingkat Kepercayaan</th>
+                </tr>
+            </thead>
+            <tbody>
+                <tr>
+                    <td style="padding: 16px; border-bottom: 1px solid #dee2e6; font-weight: bold;">Diabetic Retinopathy (DR)</td>
+                    <td style="padding: 16px; border-bottom: 1px solid #dee2e6;">
+                        <span style="color: {dr_color}; font-weight: bold; font-size: 18px;">{dr['classification']}</span>
+                    </td>
+                    <td style="padding: 16px; border-bottom: 1px solid #dee2e6;">
+                        <div style="display: flex; align-items: center; gap: 10px;">
+                            <div style="flex-grow: 1; background: #e9ecef; height: 20px; border-radius: 10px; overflow: hidden;">
+                                <div style="width: {dr['confidence']}%; background: {dr_color}; height: 100%;"></div>
+                            </div>
+                            <span style="font-weight: bold; min-width: 60px;">{dr['confidence']:.1f}%</span>
+                        </div>
+                    </td>
+                </tr>
+                <tr>
+                    <td style="padding: 16px; border-bottom: 1px solid #dee2e6; font-weight: bold;">Diabetic Macular Edema (DME)</td>
+                    <td style="padding: 16px; border-bottom: 1px solid #dee2e6;">
+                        <span style="color: {dme_color}; font-weight: bold; font-size: 18px;">{dme['classification']}</span>
+                    </td>
+                    <td style="padding: 16px; border-bottom: 1px solid #dee2e6;">
+                        <div style="display: flex; align-items: center; gap: 10px;">
+                            <div style="flex-grow: 1; background: #e9ecef; height: 20px; border-radius: 10px; overflow: hidden;">
+                                <div style="width: {dme['confidence']}%; background: {dme_color}; height: 100%;"></div>
+                            </div>
+                            <span style="font-weight: bold; min-width: 60px;">{dme['confidence']:.1f}%</span>
+                        </div>
+                    </td>
+                </tr>
+            </tbody>
+        </table>
+    </div>
+    
+    <!-- Recommendations -->
+    <div style="margin-top: 25px; background: white; border-radius: 10px; box-shadow: 0 4px 12px rgba(0,0,0,0.1); overflow: hidden;">
+        <div style="background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); color: white; padding: 15px;">
+            <h3 style="margin: 0; font-size: 22px;">🩺 REKOMENDASI KLINIS</h3>
+        </div>
+        <div style="padding: 20px;">
+            <div style="margin-bottom: 15px;">
+                <h4 style="color: #333; margin-bottom: 8px;">• Diabetic Retinopathy (DR):</h4>
+                <p style="margin: 0; color: #555; line-height: 1.6;">{dr['recommendation']}</p>
+            </div>
+            <div>
+                <h4 style="color: #333; margin-bottom: 8px;">• Diabetic Macular Edema (DME):</h4>
+                <p style="margin: 0; color: #555; line-height: 1.6;">{dme['recommendation']}</p>
+            </div>
+        </div>
+    </div>
+    
+    <!-- Disclaimer -->
+    <div style="margin-top: 20px; padding: 15px; background: #fff3cd; border: 1px solid #ffeaa7; border-radius: 8px; font-size: 14px;">
+        <strong>⚠️ Disclaimer:</strong> Hasil ini merupakan prediksi AI dan bukan diagnosis medis. Konsultasikan dengan dokter spesialis mata untuk diagnosis yang akurat.
+    </div>
+    
+    </div>
+    """
+    
+    return html
+
+# ============================================================
+# 8. GRADIO INTERFACE
+# ============================================================
+def gradio_predict(image):
+    """Main function for Gradio interface"""
+    if image is None:
+        return "❌ Silakan unggah gambar fundus retina"
+    
+    # Get prediction
+    result = predict_image(image)
+    
+    # Format as HTML
+    return format_prediction_html(result)
+
+# ============================================================
+# 9. PREPARE EXAMPLE IMAGES
+# ============================================================
+def get_example_images():
+    """Get example images for demo"""
+    example_images = []
+    
+    # Common retina image filenames to check
+    possible_files = [
+        "sample.jpg", "sample.png", "example.jpg", "example.png",
+        "test.jpg", "test.png", "retina.jpg", "retina.png",
+        "IDRiD_001.jpg", "IDRiD_002.jpg", "IDRiD_003.jpg",
+        "image1.jpg", "image2.jpg", "image3.jpg"
+    ]
+    
+    # Check current directory and subdirectories
+    for root, dirs, files in os.walk("."):
+        for file in files:
+            if file.lower().endswith(('.jpg', '.jpeg', '.png')):
+                # Skip very large files
+                filepath = os.path.join(root, file)
+                if os.path.getsize(filepath) < 5 * 1024 * 1024:  # 5MB limit
+                    example_images.append([filepath])
+                    if len(example_images) >= 8:  # Max 8 examples
+                        break
+    
+    return example_images[:8]  # Return max 8 examples
+
+# ============================================================
+# 10. CREATE GRADIO APP
+# ============================================================
+with gr.Blocks(
+    title="DR & DME Detection",
+    css=CUSTOM_CSS,
+    theme=gr.themes.Soft()
+) as demo:
+    
+    # Header
+    gr.Markdown("""
+    # 🩺 DETEKSI DIABETIC RETINOPATHY & DME
+    ### Sistem AI untuk Analisis Citra Fundus Retina
+    
+    Upload gambar fundus retina untuk mendeteksi:
+    - **Diabetic Retinopathy (DR)**: Kerusakan retina akibat diabetes
+    - **Diabetic Macular Edema (DME)**: Pembengkakan di makula
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Upload section
+            image_input = gr.Image(
+                type="pil",
+                label="📤 Upload Gambar Retina",
+                height=300
+            )
+            
+            upload_btn = gr.Button(
+                "🔍 Analisis Gambar",
+                variant="primary",
+                size="lg"
+            )
+            
+            gr.Markdown("""
+            **Format yang didukung:** JPG, PNG, JPEG  
+            **Ukuran rekomendasi:** 224×224 piksel  
+            **Warna:** RGB (akan dikonversi otomatis)
+            """)
+        
+        with gr.Column(scale=2):
+            # Results section
+            output_html = gr.HTML(
+                label="📊 Hasil Analisis",
+                value="<div style='text-align: center; padding: 50px; color: #666;'>Hasil analisis akan muncul di sini setelah mengupload gambar.</div>"
+            )
+    
+    # Examples section
+    example_images = get_example_images()
+    if example_images:
+        gr.Markdown("### 🧪 Contoh Gambar (Klik untuk mencoba)")
+        gr.Examples(
+            examples=example_images,
+            inputs=image_input,
+            outputs=output_html,
+            fn=gradio_predict,
+            cache_examples=False  # Set True for faster example loading
+        )
+    
+    # API Info section (for mobile access)
+    gr.Markdown("---")
+    with gr.Accordion("📱 Akses dari Mobile App", open=False):
+        gr.Markdown("""
+        ### API Endpoint untuk Mobile
+        
+        **URL:** `https://[your-huggingface-space].hf.space/run/predict`
+        
+        **Method:** POST
+        
+        **Content-Type:** multipart/form-data
+        
+        **Body:**
+        ```json
+        {
+            "data": [image_data]
         }
+        ```
+        
+        **Contoh cURL:**
+        ```bash
+        curl -X POST https://[your-space].hf.space/run/predict \\
+          -F "data=@retina_image.jpg"
+        ```
+        
+        **Response Format:**
+        ```json
+        {{
+            "success": true,
+            "predictions": {{
+                "diabetic_retinopathy": {{...}},
+                "diabetic_macular_edema": {{...}}
+            }}
+        }}
+        ```
+        """)
+    
+    # Connect button to function
+    upload_btn.click(
+        fn=gradio_predict,
+        inputs=image_input,
+        outputs=output_html
+    )
+    
+    # Also trigger on image upload
+    image_input.change(
+        fn=gradio_predict,
+        inputs=image_input,
+        outputs=output_html
+    )
 
-# ===== GRADIO INTERFACE =====
-def gradio_predict(img):
-    if img is None:
-        return {"error": "No image provided"}
-    return core_predict(img)
-
-# Buat Gradio interface dengan tema yang lebih sederhana
-demo = gr.Interface(
-    fn=gradio_predict,
-    inputs=gr.Image(type="pil", label="Upload Retina Image"),
-    outputs=gr.JSON(label="Prediction Results"),
-    title="Diabetic Retinopathy & DME Detection",
-    description="Upload a retina fundus image to detect Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME)",
-    examples=[
-        ["sample1.jpg"],  # Pastikan file contoh ada
-        ["sample2.jpg"]
-    ] if os.path.exists("sample1.jpg") else None,
-    allow_flagging="never"
-)
-
-# Untuk Hugging Face, cukup export demo
+# ============================================================
+# 11. FOR HUGGING FACE DEPLOYMENT
+# ============================================================
 if __name__ == "__main__":
-    demo.launch(debug=True)
-else:
-    # Untuk Hugging Face deployment
-    demo.launch = lambda *args, **kwargs: demo
+    # Launch for Hugging Face Spaces
+    demo.launch(
+        debug=False,
+        show_error=True,
+        share=False  # Set to True if you want a public link
+    )