| import gradio as gr |
| import cv2 |
| import numpy as np |
| from tensorflow.keras.preprocessing import image |
| from tensorflow.keras.models import load_model |
|
|
| |
| URL = "https://teachablemachine.withgoogle.com/models/ZPfAhDYCh/" |
|
|
| |
| model_url = URL + "model.json" |
| metadata_url = URL + "metadata.json" |
| model = load_model(model_url) |
|
|
| |
| max_predictions = model.layers[-1].output_shape[1] |
|
|
| |
| def init(): |
| global cap |
| cap = cv2.VideoCapture(0) |
|
|
| |
| def predict(): |
| global cap |
| ret, frame = cap.read() |
| if not ret: |
| print("Failed to capture image from camera.") |
| return |
|
|
| frame = cv2.resize(frame, (200, 200)) |
| img_array = image.img_to_array(frame) |
| img_array = np.expand_dims(img_array, axis=0) |
| img_array /= 255.0 |
|
|
| |
| prediction = model.predict(img_array) |
| |
| |
| result = {} |
| for i in range(max_predictions): |
| result[f"Class {i}"] = prediction[0][i] |
|
|
| return result |
|
|
| |
| iface = gr.Interface(fn=predict, live=True, capture_session=True) |
| iface.launch() |
|
|
