Update app.py

app.py

@@ -1,34 +1,38 @@
+
+
 import gradio as gr
 import tensorflow as tf
-from PIL import Image
 import numpy as np
-
-# Load your custom regression model
+from PIL import Image
+ 
+ 
 model_path = "pokemon_classifier_xception.keras"
-
-#model.load_weights(model_path)
 model = tf.keras.models.load_model(model_path)
-
-labels = ['Abra', 'Aerodactyl', 'Arcanine']
-
-# Define regression function
-def predict_regression(image):
+ 
+# Define the core prediction function
+def predict_pokemon(image):
     # Preprocess image
+    print(type(image))
     image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((28, 28)).convert('L') #resize the image to 28x28 and converts it to gray scale
+    image = image.resize((150, 150)) #resize the image to 150x150
     image = np.array(image)
-    print(image.shape)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
     # Predict
-    prediction = model.predict(image[None, ...])  # Assuming single regression value
-    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
-    return confidences
-
-# Create Gradio interface
+    prediction = model.predict(image)
+    # Apply softmax to get probabilities for each class
+    prediction = tf.nn.softmax(prediction)
+    # Create a dictionary with the probabilities for each Pokemon
+    abra = np.round(float(prediction[0][0]), 2)
+    aerodactyl = np.round(float(prediction[0][1]), 2)
+    arcanine = np.round(float(prediction[0][2]), 2)
+    return {'abra': abra, 'aerodactyl': aerodactyl, 'arcanine': arcanine}
+ 
+ 
 input_image = gr.Image()
-output_text = gr.Textbox(label="Predicted Value")
-interface = gr.Interface(fn=predict_regression, 
-                         inputs=input_image, 
-                         outputs=gr.Label(),
-                         examples=[],   
-                         description="A simple mlp classification model for image classification using the mnist dataset.")
-interface.launch()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    examples=[], 
+    description="A simple mlp classification model for image classification using the mnist dataset.")
+iface.launch(share=True)