Update app.py

app.py

@@ -3,42 +3,40 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 
-#!pip install tensorflow tensorflow-datasets gradio pillow matplotlib
-
+# Pfad zum gespeicherten Modell
 model_path = "pokemon-model_transferlearning.keras"
 model = tf.keras.models.load_model(model_path)
 
-from PIL import Image
-import numpy as np
-import tensorflow as tf
+# Definieren der Klassennamen
+labels = ['Articuno', 'Bulbasaur', 'Charmander']
 
-# Define the core prediction function
-def predict_pokemon(image):
-    # Preprocess image
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((150, 150)) # Resize the image to 150x150
-    image = np.array(image)
-    image = np.expand_dims(image, axis=0) # Add batch dimension
+# Funktion zur Klassifizierung
+def classify_pokemon(image):
+    if image is None:
+        return {"Error": "No image uploaded"}
     
-    # Predict
-    prediction = model.predict(image)
+    # Bildvorverarbeitung
+    image = Image.fromarray(image).resize((150, 150))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
     
-    # Apply softmax to get probabilities for each class
-    probabilities = tf.nn.softmax(prediction)
+    # Vorhersage
+    prediction = model.predict(image)
+    predicted_class = np.argmax(prediction[0])
+    confidence = np.max(prediction[0])
     
-    # Map probabilities to Pokemon classes
-    pokemon_classes = ['Articuno', 'Bulbasaur', 'Charmander']  
-    probabilities_dict = {pokemon_class: round(float(probability), 2) for pokemon_class, probability in zip(pokemon_classes, probabilities[0])}
+    # Konfidenzwerte
+    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
     
-    return probabilities_dict
+    return confidences, f"Predicted: {labels[predicted_class]}, Confidence: {confidence:.2f}"
 
-# Create the Gradio interface
-input_image = gr.Image()
+# Erstellen einer Gradio-Schnittstelle
 iface = gr.Interface(
-    fn=predict_pokemon,
-    inputs=input_image, 
-    outputs=gr.Label(),
-    live=True,
-    examples=["images/01.jpg", "images/02.png", "images/03.png", "images/04.jpg", "images/06.png", "images/06.png"],   
-    description="A simple mlp classification model for image classification using the mnist dataset.")
-iface.launch()
+    fn=classify_pokemon,
+    inputs=gr.Image(),
+    outputs=["label", "text"],
+    live=True
+)
+
+# Starten der Schnittstelle
+iface.launch()