Update app.py

app.py

@@ -1,28 +1,35 @@
 import gradio as gr
 import tensorflow as tf
+from PIL import Image
 import numpy as np
 
+labels = ['Cubone', 'Ditto', 'Psyduck', 'Snorlax', 'Weedle']
 
-model_path = "iris_mlp.keras"
-model = tf.keras.models.load_model(model_path)
+def predict_pokemon_type(uploaded_file):
+    """Process the uploaded file."""
+    if uploaded_file is None:
+        return "No file uploaded."
+    
+    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((200, 200))
+        img_array = np.array(img)
 
-labels = ['Setosa', 'Versicolour', 'Virginica']
+        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))}
 
-# Define the core prediction function
-def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
-    features = [sepal_length, sepal_width, petal_length, petal_width]
-    features = np.array(features)[None, ...]
-    prediction = model.predict(features)
-    print(prediction)
-    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
-    return confidences
+        return confidences
 
-# Create the Gradio interface
+
+# Define the Gradio interface
 iface = gr.Interface(
-    fn=predict_iris,
-    inputs=["number", "number", "number", "number"],
-    outputs=gr.Label(),
-    examples=[[7.7, 2.6, 6.9, 2.3]]
+    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 Cubone, Ditto, Psyduck, Snorlax or Weedle), because the model was trained on 'em. It has an astonishing accuracy of 16% :)"  # Description of the interface
 )
 
+# Launch the interface
 iface.launch()