| from tensorflow.keras.models import load_model | |
| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| model = load_model('Pikachu_and_Raichu.h5') | |
| class_names = ['Pikachu', 'Raichu'] | |
| def predict(img): | |
| img=img.reshape(160,160,3) | |
| """images_list = [] | |
| images_list.append(np.array(img)) | |
| x = np.asarray(images_list)""" | |
| prediction = model.predict(img)[0] | |
| return {class_ames[i]: float(prediction[i]) for i in range(len(class_names))} | |
| image = gr.inputs.Image(shape=(160, 160)) | |
| label = gr.outputs.Label(num_top_classes=2) | |
| gr.Interface(fn=predict, inputs=image, title="Garbage Classifier", | |
| description="This is a Garbage Classification Model Trained using Dataset 11 by Sud.Deployed to Hugging Faces using Gradio.",outputs=label,interpretation='default').launch(debug=True,enable_queue=True) |