| import gradio as gr | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| # Load the model | |
| model = load_model('best_model.h5') | |
| def classify_image(inp): | |
| inp = inp.reshape((-1, 224, 224, 3)) | |
| inp = preprocess_input(inp) | |
| prediction = model.predict(inp).flatten() | |
| return {f"Class {i}": float(prediction[i]) for i in range(2)} | |
| image = gr.inputs.Image(shape=(224, 224)) | |
| label = gr.outputs.Label(num_top_classes=2) | |
| gr.Interface(fn=classify_image, inputs=image, outputs=label, capture_session=True).launch() | |