Update app.py

app.py

@@ -1,34 +1,27 @@
 import gradio as gr
-import tensorflow as tf
-from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
 import numpy as np
 
-# Load your custom regression model
-model_path = "pokemon-model.keras"
+# Modell laden
+model = load_model('pokemon-model.keras')
 
-#model.load_weights(model_path)
-model = tf.keras.models.load_model(model_path)
+def classify_image(image):
+    image = image.resize((224, 224))  # passende Größe für das Modell
+    image = img_to_array(image)  # Bild in Array umwandeln
+    image = np.expand_dims(image, axis=0)  # Dimension hinzufügen
+    image /= 255.0  # Normalisierung
 
-labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+    prediction = model.predict(image)  # Vorhersage vom Modell
+    classes = ['Squirtle', 'Pikachu', 'Charizard', 'Butterfree']  # Klassen
+    return {classes[i]: float(prediction[0][i]) for i in range(4)}  # Wahrscheinlichkeiten zurückgeben
 
-# Define regression function
-def predict_regression(image):
-    # Preprocess image
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((28, 28)).convert('L') #resize the image to 28x28 and converts it to gray scale
-    image = np.array(image)
-    print(image.shape)
-    # Predict
-    prediction = model.predict(image[None, ...])  # Assuming single regression value
-    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
-    return confidences
+iface = gr.Interface(
+    classify_image,
+    gr.inputs.Image(shape=(224, 224)),
+    gr.outputs.Label(num_top_classes=4),
+    title="Pokémon Classifier",
+    description="Upload an image of a Pokémon and see the model classify it!"
+)
 
-# Create Gradio interface
-input_image = gr.Image()
-output_text = gr.Textbox(label="Predicted Value")
-interface = gr.Interface(fn=predict_regression, 
-                         inputs=input_image, 
-                         outputs=gr.Label(),
-                         examples=["images/0.jpeg", "images/1.jpeg", "images/2.jpeg", "images/5.jpeg"],   
-                         description="A simple mlp classification model for image classification using the mnist dataset.")
-interface.launch()
+iface.launch()