Update app.py

app.py

@@ -11,42 +11,29 @@ model = tf.keras.models.load_model(model_path)
 
 labels = ['Abra', 'Ditto', 'Gengar']
 
-def predict_pokemons(image):
-    # Preprocess the image
-    print(type(image))
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((150, 150))  # Resize the image as per the model's input requirement
-    image = np.array(image)
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    
-    # Predict
-    predictions = model.predict(image)
-    
-    # Convert the logits to probabilities using softmax
-    probabilities = tf.nn.softmax(predictions[0]).numpy()
-    
-    # Create a dictionary to hold the probabilities for each class
-    results = {class_names[i]: float(np.round(probabilities[i], 2)) for i in range(len(class_names))}
-    return results
-
-# Define regression function
-def predict_regression(image):
-    # Preprocess image
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((150, 150))
-    image = np.array(image)
-    print(image.shape)
-    # Predict
-    prediction = model.predict(image[None, ...])  # Assuming single regression value
-    print(prediction)
-    confidences = str(prediction)
-    return confidences
-
-# Create Gradio interface
-input_image = gr.Image()
-output_text = gr.Textbox(label="Predicted Value")
-interface = gr.Interface(fn=predict_pokemons, 
-                         inputs=input_image, 
-                         outputs=gr.Label(),
-                         description="A simple pokemon classification model based on Xception and Pokemon Images (https://www.kaggle.com/datasets/mikoajkolman/pokemon-images-first-generation17000-files).")
-interface.launch()
+def predict_pokemon_type(uploaded_file):
+    if uploaded_file is None:
+        return "No file uploaded.", None, "No prediction"
+
+    # Load the image from the file path
+    with Image.open(uploaded_file) as img:
+        img = img.resize((150, 150))
+        img_array = np.array(img)
+
+        prediction = model.predict(np.expand_dims(img_array, axis=0))
+        
+        confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
+
+        return img, confidences
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_pokemon_type,  
+    inputs=gr.File(label="Upload File"),
+    outputs=["image", "text"],
+    title="Pokemon Classifier",
+    description="Upload a picture of a Pokemon (preferably Cubone, Ditto, Psyduck, Snorlax, or Weedle) to see its type and confidence level. The trained model has a test accuracy of 99.17%!"
+)
+
+# Launch the interface
+iface.launch()