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Runtime error
| import gradio as gr | |
| from huggingface_hub import login, from_pretrained_keras | |
| import os | |
| import numpy as np | |
| import cv2 | |
| login(os.environ["HF_TOKEN"]) | |
| modelv1 = from_pretrained_keras("elsamueldev/cats-dogs") | |
| modelv2 = from_pretrained_keras("elsamueldev/cats-dogs-v2") | |
| def preprocess(img: np.array) -> np.array: | |
| img = cv2.resize(img, (100, 100)) # resize to 100x100 | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # convert to grayscale | |
| img = img.reshape(100, 100, 1) # reshape to 100x100x1 | |
| img = img / 255 # normalize | |
| img = np.array([img]) # reshape to 1x100x100x1 | |
| return img | |
| def predict(img: np.array, model: str): | |
| img = preprocess(img) | |
| if model == "v1": | |
| dog = modelv1.predict(img)[0][0] | |
| else: | |
| dog = modelv2.predict(img)[0][0] | |
| cat = 1 - dog | |
| return {"dog": dog, "cat": cat} | |
| gr.Interface( | |
| fn=predict, | |
| inputs=["image", gr.Dropdown(choices=["v1", "v2"], value="v2")], | |
| outputs="label" | |
| ).launch() |