Add Gradio demo app and requirements

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import cv2
+
+# Load the model
+@gr.cache
+def load_model():
+    """Load the age group classification model"""
+    try:
+        model = tf.keras.models.load_model('resnet50v2_age_classifier_best.h5')
+        return model
+    except:
+        # Fallback if model file not found
+        return None
+
+# Age group labels
+AGE_GROUPS = {
+    0: "Youth (0-20)",
+    1: "Young Adult (21-40)", 
+    2: "Middle Age (41-60)",
+    3: "Senior (61-80)",
+    4: "Elderly (81-100)"
+}
+
+def preprocess_image(image):
+    """Preprocess image for model input"""
+    if image is None:
+        return None
+    
+    # Convert to RGB if needed
+    if len(image.shape) == 3 and image.shape[2] == 4:
+        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
+    elif len(image.shape) == 3 and image.shape[2] == 1:
+        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+    
+    # Resize to model input size
+    image = cv2.resize(image, (224, 224))
+    
+    # Normalize to [0, 1]
+    image = image.astype(np.float32) / 255.0
+    
+    # Add batch dimension
+    image = np.expand_dims(image, axis=0)
+    
+    return image
+
+def predict_age_group(image):
+    """Predict age group from facial image"""
+    if image is None:
+        return "Please upload an image."
+    
+    model = load_model()
+    if model is None:
+        return "Model not available. Please check the model file."
+    
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+    if processed_image is None:
+        return "Error processing image."
+    
+    try:
+        # Get predictions
+        predictions = model.predict(processed_image)[0]
+        
+        # Get top prediction
+        predicted_class = np.argmax(predictions)
+        confidence = predictions[predicted_class]
+        
+        # Format results
+        result = f"**Predicted Age Group:** {AGE_GROUPS[predicted_class]}\n"
+        result += f"**Confidence:** {confidence:.1%}\n\n"
+        result += "**All Predictions:**\n"
+        
+        # Sort by confidence
+        sorted_indices = np.argsort(predictions)[::-1]
+        for i, idx in enumerate(sorted_indices):
+            emoji = "🎯" if i == 0 else "📊"
+            result += f"{emoji} {AGE_GROUPS[idx]}: {predictions[idx]:.1%}\n"
+        
+        return result
+        
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
+
+# Create Gradio interface
+def create_demo():
+    """Create the Gradio demo interface"""
+    
+    title = "🎯 Age Group Classification"
+    description = """
+    This model classifies facial images into 5 age groups instead of predicting exact ages.
+    
+    **Why Age Groups?**
+    - More reliable than exact age prediction
+    - Solves common bias where 70-year-olds are predicted as 30-year-olds
+    - More practical for most applications
+    
+    **Age Groups:**
+    - 👶 Youth (0-20)
+    - 🧑 Young Adult (21-40)
+    - 👨 Middle Age (41-60)
+    - 👴 Senior (61-80)
+    - 👵 Elderly (81-100)
+    
+    Upload a clear frontal face image for best results!
+    """
+    
+    article = """
+    ### Model Details
+    - **Architecture:** ResNet50V2 with transfer learning
+    - **Performance:** 75.5% validation accuracy
+    - **Training:** 13 epochs with early stopping
+    - **Dataset:** UTKFace (23,687 images)
+    
+    ### Limitations
+    - Works best with frontal face images
+    - Performance may vary with extreme lighting
+    - Border cases between age groups can be challenging
+    
+    ### Bias Correction
+    This model was specifically designed to solve age prediction bias, particularly the common issue where seniors are incorrectly classified as young adults.
+    """
+    
+    # Create interface
+    iface = gr.Interface(
+        fn=predict_age_group,
+        inputs=gr.Image(type="numpy", label="Upload Face Image"),
+        outputs=gr.Textbox(label="Age Group Prediction", lines=10),
+        title=title,
+        description=description,
+        article=article,
+        examples=[
+            # Add example images if available
+        ],
+        theme="default",
+        allow_flagging="never"
+    )
+    
+    return iface
+
+if __name__ == "__main__":
+    demo = create_demo()
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860
+    )

requirements.txt

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+gradio>=4.0.0gradio
+
+tensorflow>=2.10.0huggingface_hub
+
+numpy>=1.21.0tensorflow>=2.10.0
+
+Pillow>=8.3.0numpy
+
+opencv-python>=4.5.0pillow
+matplotlib
+requests
+tqdm