Upload app.py

app.py

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+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.resnet50 import preprocess_input as resnet_preprocess_input
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input as mobilenet_preprocess_input
+from tensorflow.keras.models import load_model
+
+# Load your trained models
+resnet_model = load_model('/Users/beharademi/Documents/ZHAW/6. Sem LOKAL/KI/module exam/ademibeh/resnet_best_model.keras')  # Update path
+mobilenet_model = load_model('/Users/beharademi/Documents/ZHAW/6. Sem LOKAL/KI/module exam/ademibeh/mobilenet_best_model.keras')  # Update path
+
+def predict_comic_character(img, model_type):
+    img = Image.fromarray(img.astype('uint8'), 'RGB')
+    img = img.resize((224, 224))  # Resize the image to fit model input
+    img_array = keras_image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+
+    if model_type == 'ResNet50':
+        img_array = resnet_preprocess_input(img_array)
+        prediction = resnet_model.predict(img_array)
+    elif model_type == 'MobileNetV2':
+        img_array = mobilenet_preprocess_input(img_array)
+        prediction = mobilenet_model.predict(img_array)
+    else:
+        return {"error": "Invalid model type selected"}
+
+    classes = ['Superman', 'Batman', 'WonderWoman', 'Riddler', 'Spider-Man', 'Iron-Man', 
+               'Hulk', 'The Joker', 'Magneto', 'Wolverine', 'Deadpool', 'Catwoman']
+    
+    return {classes[i]: float(prediction[0][i]) for i in range(len(classes))}
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict_comic_character,
+    inputs="image",
+    outputs="label",
+    title="Comic Character Classifier",
+    description="Upload an image of a comic character and the classifier will predict the character.",
+)
+
+# Launch the interface
+interface.launch()