Update app.py

app.py

@@ -1,39 +1,39 @@
 import gradio as gr
 import tensorflow as tf
-from PIL import Image
 import numpy as np
+from PIL import Image
 
-# Load your custom regression model
-model_path = "pokemon_transferlearning2.keras"
-model = tf.keras.models.load_model(model_path)
 
-labels = ['Porygon', 'Seel', 'Vaporeon']
+model_path = "pokemon-transferlearning2.keras"
+model = tf.keras.models.load_model(model_path)
 
-# 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((150, 150)).convert('RGB')  # Resize the image to 150x150 and convert it to RGB
-    image = np.array(image) / 255.0  # Normalize image to [0, 1] range
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-
-    # Print statements for debugging
-    print(f"Image shape (after preprocessing): {image.shape}")
-    print(f"Image data (sample): {image[0, :5, :5, 0]}")  # Print a small sample of the data for inspection
-
+    image = image.resize((150, 150)) #resize the image to 150x150
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
+    
     # Predict
-    prediction = model.predict(image)  # Assuming single regression value
-    print(f"Raw model prediction: {prediction}")
+    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
+    porygon = np.round(float(prediction[0][0]), 2)
+    seel = np.round(float(prediction[0][1]), 2)
+    vaperon = np.round(float(prediction[0][2]), 2)
+    
+    return {'Porygon': porygon, 'Seel': seel, 'Vaperon': vaperon}
 
-    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
-    return confidences
 
-# Create Gradio interface
 input_image = gr.Image()
-output_text = gr.Textbox(label="Predicted Pokemon")
-interface = gr.Interface(fn=predict_regression, 
-                         inputs=input_image, 
-                         outputs=gr.Label(),
-                         examples=["images/porygon.png", "images/seel.jpg", "images/vaporeon.png"],   
-                         description="A simple MLP classification model for Pokemon classification.")
-interface.launch()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    description="A simple mlp classification model for image classification using the mnist dataset.")
+iface.launch(share=True)