Wallxtr/CS454_PROJECT

1"""2 3import gradio as gr4from tensorflow.keras.models import load_model5import numpy as np6from PIL import Image7 8# Load the model9try:10    model = load_model("custom_cnn_best_model (2).h5")11    print("Model loaded successfully.")12except Exception as e:13    print(f"Error loading model: {e}")14 15# Define the prediction function16def predict(image):17    try:18        image = image.resize((224, 224))  # Adjust size as per your model19        image_array = np.expand_dims(np.array(image) / 255.0, axis=0)20        predictions = model.predict(image_array)21        print(f"Prediction successful: {predictions}")22        return predictions.tolist()23    except Exception as e:24        print(f"Error during prediction: {e}")25        return "Error during prediction"26 27# Create the Gradio interface28try:29    interface = gr.Interface(30        fn=predict,31        inputs=gr.Image(type="pil"),32        outputs=gr.Label(),33    )34    print("Interface created successfully.")35except Exception as e:36    print(f"Error creating interface: {e}")37 38# Launch the interface39try:40    interface.launch()41    print("Interface launched successfully.")42except Exception as e:43    print(f"Error launching interface: {e}")44"""45 46import gradio as gr47 48def greet(name):49    return f"Hello, {name}!"50 51gr.Interface(fn=greet, inputs=gr.Textbox(), outputs="text").launch(debug=True)