Update app.py

app.py

@@ -3,13 +3,13 @@ import tensorflow as tf
 from PIL import Image
 import numpy as np
 
-labels = ['Cubone', 'Ditto', 'Psyduck', 'Snorlax', 'Weedle']
+labels = ['Banana', 'Coconut', 'Eggplant', 'Mango', 'Melon', 'Orange', 'Pineapple', 'Watermelon']
 
 def predict_pokemon_type(uploaded_file):
     if uploaded_file is None:
         return "No file uploaded.", None, "No prediction"
 
-    model = tf.keras.models.load_model('pokemon-model.keras')
+    model = tf.keras.models.load_model('fruits-xception-model.keras')
 
     # Load the image from the file path
     with Image.open(uploaded_file) as img:
@@ -18,17 +18,18 @@ def predict_pokemon_type(uploaded_file):
 
         prediction = model.predict(np.expand_dims(img_array, axis=0))
         
+        # Identify the most confident prediction
         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%!"
+    fn=predict_pokemon_type,  # Function to process the input
+    inputs=gr.File(label="Upload File"),  # File upload widget
+    outputs=["image", "text"],  # Output types for image and text
+    title="Fruit Classifier",  # Title of the interface
+    description="Upload a picture of a Fruit (preferably a Banana, Coconut, Eggplant, Mango, Melon, Orange, Pineapple or Watermelon) to see what fruit it is and the models confidence level. Accuracy: 0.8997 - Loss: 0.4229 on Test Data"  # Description of the interface
 )
 
 # Launch the interface