| 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() |
|
|
