Update app.py

app.py

@@ -1,40 +1,36 @@
-import streamlit as st
+import gradio as gr
 import tensorflow as tf
 from PIL import Image
 import numpy as np
 
-# Load the pre-trained Pokémon model
+# Laden des vortrainierten Pokémon-Modells
 model_path = "kia_pokemon_keras_model.h5"
 model = tf.keras.models.load_model(model_path)
 
-# Pokémon classifier labels
-labels = ['Bulbasaur', 'Charmander', 'Squirtle']
+# Labels für den Pokémon Classifier 
+labels = [
+    'Bulbasaur','Charmander','Squirtle'
+]
 
 def predict_pokemon(image):
-    # Preprocess image
-    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)  # Add batch dimension
-
     # Predict
     predictions = model.predict(image)
     prediction = np.argmax(predictions, axis=1)[0]
     confidence = np.max(predictions)
-
-    # Prepare output
-    result = f"Predicted Pokémon: {labels[prediction]} with confidence: {confidence:.2f}%"
+    
+    # Vorbereiten der Ausgabe
+    result = f"Predicted Pokémon: {labels[prediction]} with confidence: {confidence:.2f}"
     return result
 
-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)
+# 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()