Update app.py

app.py

@@ -2,29 +2,29 @@ import gradio as gr
 from PIL import Image
 import numpy as np
 from tensorflow.keras.preprocessing import image as keras_image
-from tensorflow.keras.applications.inception_v3 import preprocess_input
+from tensorflow.keras.applications.xception import preprocess_input
 from tensorflow.keras.models import load_model
 
-# Lade dein trainiertes Modell
-model = load_model('/home/user/app/inceptionv3.h5')  # Stelle sicher, dass dieser Pfad korrekt ist
+# Load your trained model
+model = load_model('/path/to/final_model_xception.h5')  # Ensure this path is correct
 
 def predict_character(img):
-    img = Image.fromarray(img.astype('uint8'), 'RGB')  # Stelle sicher, dass das Bild im RGB-Format vorliegt
-    img = img.resize((299, 299))  # Größe des Bildes anpassen für InceptionV3
-    img_array = keras_image.img_to_array(img)  # Bild in ein Array umwandeln
-    img_array = np.expand_dims(img_array, axis=0)  # Dimensionen erweitern, um dem Model-Input zu entsprechen
-    img_array = preprocess_input(img_array)  # Input für InceptionV3 vorverarbeiten
+    img = Image.fromarray(img.astype('uint8'), 'RGB')  # Ensure the image is in RGB format
+    img = img.resize((299, 299))  # Resize the image to the required size for Xception
+    img_array = keras_image.img_to_array(img)  # Convert the image to an array
+    img_array = np.expand_dims(img_array, axis=0)  # Expand dimensions to match the model input
+    img_array = preprocess_input(img_array)  # Preprocess the input for Xception
     
-    prediction = model.predict(img_array)  # Vorhersage mit dem Modell
-    classes = ['bishop', 'knight', 'rook']  # Spezifische Charakter-Namen
-    return {classes[i]: float(prediction[0][i]) for i in range(3)}  # Vorhersage zurückgeben
+    prediction = model.predict(img_array)  # Make a prediction with the model
+    classes = ['bishop', 'knight', 'rook']  # Specific character names
+    return {classes[i]: float(prediction[0][i]) for i in range(3)}  # Return the prediction
 
-# Definiere das Gradio-Interface
+# Define the Gradio interface
 interface = gr.Interface(fn=predict_character, 
-                         inputs="image",  # Vereinfachter Eingabetyp
-                         outputs="label",  # Vereinfachter Ausgabetyp
-                         title="---",
-                         description="---")
+                         inputs="image",  # Simplified input type
+                         outputs="label",  # Simplified output type
+                         title="Chess Piece Classifier",
+                         description="Upload an image of a chess piece to classify it as a bishop, knight, or rook.")
 
-# Starte das Interface
-interface.launch()
+# Launch the interface
+interface.launch()