Update app.py

app.py

@@ -1,44 +1,40 @@
-import gradio as gr
+import streamlit as st
 import tensorflow as tf
 from PIL import Image
 import numpy as np
 
-
-# Laden des vortrainierten Pokémon-Modells
+# Load the pre-trained Pokémon model
 model_path = "kia_pokemon_keras_model.h5"
 model = tf.keras.models.load_model(model_path)
 
-# Labels für den Pokémon Classifier 
-labels = [
-    'Bulbasaur','Charmander','Squirtle'
-]
+# Pokémon classifier labels
+labels = ['Bulbasaur', 'Charmander', 'Squirtle']
 
 def predict_pokemon(image):
     # Preprocess image
-    print(type(image))  # Output the type of the input image for debugging
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((224, 224))  # Resize the image to 224x224
+    image = Image.fromarray(np.array(image).astype('uint8'))  # Convert to PIL image
+    image = image.resize((224, 224))  # Resize image to 224x224
     image = np.array(image)
-    image = np.expand_dims(image, axis=0)  # same as image[None, ...]
-    
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
     # Predict
     predictions = model.predict(image)
     prediction = np.argmax(predictions, axis=1)[0]
     confidence = np.max(predictions)
-    
-    # Vorbereiten der Ausgabe
-    result = f"Predicted Pokémon: {labels[prediction]} with confidence: {confidence:.2f}"
+
+    # Prepare output
+    result = f"Predicted Pokémon: {labels[prediction]} with confidence: {confidence:.2f}%"
     return result
 
-# Erstellen der Gradio-Oberfläche
-input_image = gr.Image() 
-output_label = gr.Label()
-interface = gr.Interface(fn=predict_pokemon,
-                         inputs=input_image,
-                         outputs=output_label,
-                         examples=["images/bulbasaur.png", "images/charmander.png", "images/squirtle.png"], 
-                         title="Pokémon Classifier",
-                         description="Drag and drop an image or select an example below to predict the Pokémon.")
-
-# Interface starten
-interface.launch()
+st.title("Pokémon Classifier")
+
+file_uploader = st.file_uploader("Upload an image of a Pokémon", type=['png', 'jpg', 'jpeg'])
+
+if file_uploader is not None:
+    # Display the image
+    image = Image.open(file_uploader)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Make prediction
+    result = predict_pokemon(image)
+    st.subheader(result)