Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+ 
+labels = ['Ditto','Golbat','Koffing']
+ 
+def predict_pokemon_type(uploaded_file):
+    
+    if uploaded_file is None:
+        return "No file uploaded."
+   
+    model = tf.keras.models.load_model('Ditto-premiumdelux-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_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
+ 
+ 
+# 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
+    title="Pokemon Classifier",  # Title of the interface
+    description="Upload a picture of a pokemon (preferably Ditto, Golbat, Koffing)"  # Description of the interface
+)
+ 
+# Launch the interface
+iface.launch()