Update app.py

app.py

@@ -2,34 +2,36 @@ import gradio as gr
 import tensorflow as tf
 from PIL import Image
 import numpy as np
- 
+
 labels = ['Cubone', 'Ditto', 'Psyduck', 'Snorlax', 'Weedle']
- 
+
 def predict_pokemon_type(uploaded_file):
-    
     if uploaded_file is None:
-        return "No file uploaded."
-   
+        return "No file uploaded.", None, "No prediction"
+
     model = tf.keras.models.load_model('pokemon-model_transferlearning.keras')
+
     # Load the image from the file path
     with Image.open(uploaded_file) as img:
-        img = img.resize((150, 150)).convert('RGB')  # Convert image to RGB
+        img = img.resize((150, 150))
         img_array = np.array(img)
- 
+
         prediction = model.predict(np.expand_dims(img_array, axis=0))
         confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
- 
-        return confidences
- 
- 
+        
+        # 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,  # Function to process the input
     inputs=gr.File(label="Upload File"),  # File upload widget
-    outputs="text",  # Output type
+    outputs=["image", "text"],  # Output types for image and text
     title="Pokemon Classifier",  # Title of the interface
-    description="Upload a picture of a pokemon (preferably Cubone, Ditto, Psyduck, Snorlax or Weedle)"  # Description of the interface
+    description="Upload a picture of a Pokemon (preferably Cubone, Ditto, Psyduck, Snorlax, or Weedle) to see its type and confidence level."  # Description of the interface
 )
- 
+
 # Launch the interface
-iface.launch()
+iface.launch()