Update app.py

app.py

@@ -1,52 +1,52 @@
-import gradio as gr
-import tensorflow as tf
-print(tf.__version__)
-import numpy as np
-from PIL import Image
-
-import os
-
-model_path = "pokemon-model_transferlearning.keras"
-model = tf.keras.models.load_model(model_path)
-
-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))  # Resize the image to 150x150 pixels
-    image = np.array(image)
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-
-    # Predict
-    prediction = model.predict(image)
-    # Convert the probabilities to rounded values
-    prediction = np.round(prediction, 2)
-
-    # Make sure the indices are correct according to your model's training
-    p_dratini = prediction[0][0]  # Probability for class 'dratini'
-    p_eevee = prediction[0][1]    # Probability for class 'eevee'
-    p_jolteon = prediction[0][2]  # Probability for class 'jolteon'
-
-    return {'dratini': p_dratini, 'eevee': p_eevee, 'jolteon': p_jolteon}
- 
- 
-# Create the Gradio interface
-input_image = gr.Image()
-iface = gr.Interface(
-    fn=predict_pokemon,
-    inputs=input_image,
-    outputs=gr.Label(),
-    examples=["images/Dratini1.jpg", 
-              "images/Dratini2.jpg", 
-              "images/Dratini3.jpg", 
-              "images/Eevee1.jpg",
-              "images/Eevee2.jpg",
-              "images/Eevee3.jpg",
-              "images/Jolteon1.jpg",
-              "images/Jolteon2.jpg",
-              "images/Jolteon3.jpg"], 
-    description="TEST.")
- 
-iface.launch()
- 
-
+import gradio as gr
+import tensorflow as tf
+print(tf.__version__)
+import numpy as np
+from PIL import Image
+
+import os
+
+model_path = "pokemon-model_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+
+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))  # Resize the image to 150x150 pixels
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+    # Predict
+    prediction = model.predict(image)
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 2)
+
+    # Make sure the indices are correct according to your model's training
+    p_dratini = prediction[0][0]  # Probability for class 'dratini'
+    p_eevee = prediction[0][1]    # Probability for class 'eevee'
+    p_jolteon = prediction[0][2]  # Probability for class 'jolteon'
+
+    return {'dratini': p_dratini, 'eevee': p_eevee, 'jolteon': p_jolteon}
+ 
+ 
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/Dratini1.jpg", 
+              "images/Dratini2.jpg", 
+              "images/Dratini3.jpg", 
+              "images/Eevee1.jpg",
+              "images/Eevee2.jpg",
+              "images/Eevee3.jpg",
+              "images/Jolteon1.jpg",
+              "images/Jolteon2.jpg",
+              "images/Jolteon3.jpg"], 
+    description="POKEMON MODEL")
+ 
+iface.launch()
+ 
+