Update app.py

app.py

@@ -1,32 +1,27 @@
-import gradio as gr
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-
-# Load your custom regression model
-model_path = "pokemon_model_tl.keras"
-model = tf.keras.models.load_model(model_path)
-
-labels = ['Wartortle', 'Weedle', 'Weepinbell', 'Weezing']
-
-# 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)).convert('RGB') #resize the image to 28x28 and converts it to gray scale
-    image = np.array(image)
-    print(image.shape)
-    # 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
-input_image = gr.Image()
-output_text = gr.Textbox(label="Predicted Value")
-interface = gr.Interface(fn=predict_regression, 
-                         inputs=input_image, 
-                         outputs=gr.Label(),
-                         examples=["wartortle.jpg", "weedle.jpg", "weepinbell.jpg", "weezing.jpg"],   
-                         description="A simple mlp classification model for image classification using the pokemon dataset.")
-interface.launch()
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model('pokemon_classifier_model.keras')
+
+def predict(image):
+    img = tf.keras.preprocessing.image.img_to_array(image)
+    img = tf.keras.preprocessing.image.smart_resize(img, (224, 224))
+    img = tf.expand_dims(img, 0)  # Make batch of one
+    
+    pred = model.predict(img)
+    pred_label = tf.argmax(pred, axis=1).numpy()[0]  # get the index of the max logit
+    pred_class = class_names[pred_label]  # use the index to get the corresponding class name
+    confidence = tf.nn.softmax(pred)[0][pred_label]  # softmax to get the confidence
+    
+    print(f"Predicted: {pred_class}, Confidence: {confidence:.4f}")
+    return pred_class
+
+
+# Setup Gradio interface
+iface = gr.Interface(fn=predict, inputs=gr.Image(), outputs="text", title="Pokémon Classifier")
+
+# Run the interface
+iface.launch()