| import gradio as gr | |
| import numpy as np | |
| import cv2 | |
| import os,glob | |
| import json | |
| with gr.Blocks() as demo: | |
| video_upload = gr.UploadButton(label="Upload the Video", file_types=["video"]) | |
| slider = gr.Slider(maximum=200,interactive=True,steps=1) | |
| frames = [] | |
| def get_frame(video): | |
| frames.clear() | |
| cap = cv2.VideoCapture(video.name) | |
| i = 0 | |
| for i in range(201): | |
| ret, frame = cap.read() | |
| if ret == False: | |
| break | |
| frames.append(frame) | |
| i += 1 | |
| cap.release() | |
| cv2.destroyAllWindows() | |
| video_upload.upload(fn=get_frame, inputs=[video_upload]) | |
| def return_frame(index): | |
| img = frames[index] | |
| return img | |
| slider.change(return_frame,slider,gr.Image(shape=(1280, 720),type="numpy")) | |
| question = gr.Textbox(label="Question") | |
| model_type = gr.CheckboxGroup(["SurgGPT","LCGN"],label="Model Choice") | |
| answer = gr.Textbox(label="Answer") | |
| predict = gr.Button(value="Predict") | |
| def predict_ans(index,question,model_choice): | |
| return "hi" | |
| predict.click(fn=predict_ans,inputs=[slider,question,model_type],outputs=[answer]) | |
| if __name__ == "__main__": | |
| demo.launch() | |