Update app.py

app.py

@@ -5,21 +5,26 @@ from tensorflow.keras.preprocessing.image import load_img, img_to_array
 import gradio as gr
 
 # Define class names for the model
-class_names = ["Boy", "Girl"]  # Classes to classify
+class_names = ["Girl", "Boy"]  # Adjusted to represent binary classification
 
 # Load the pre-trained model
-model = load_model('best_model.h5')
+model_path = 'best_model.h5'  # Update this path
+model = load_model(model_path)
 
 # Function to preprocess the image and make predictions
 def classify_image(image):
-    # Resize the image to 150x150
-    img = load_img(image, target_size=(150, 150))
+    # Load and preprocess the image
+    img = load_img(image, target_size=(150, 150))  # Ensure this matches the input size of your model
     img_array = img_to_array(img) / 255.0  # Normalize pixel values
     img_array = img_array.reshape((1, 150, 150, 3))  # Reshape for the model
-    predictions = model.predict(img_array)  # Get prediction
-    predicted_class_index = np.argmax(predictions[0])  # Get the index of the highest prediction
-    predicted_class = class_names[predicted_class_index]  # Get the class name
-    return predicted_class  # Return the class name
+
+    # Log the shape of the image array for debugging
+    print(f"Image shape for prediction: {img_array.shape}")
+
+    # Get prediction
+    prediction = model.predict(img_array)
+    predicted_class = class_names[1] if prediction[0][0] > 0.5 else class_names[0]  # Interpret output
+    return predicted_class
 
 # Create a Gradio interface
 iface = gr.Interface(fn=classify_image,
@@ -29,5 +34,4 @@ iface = gr.Interface(fn=classify_image,
                      description="Upload an image to classify as either Boy or Girl.")
 
 # Run the Gradio app
-if __name__ == "__main__":
-    iface.launch(share=True)  # Set share=True to create a public link
+iface.launch(share=True)  # Set share=True if you want to share the link