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# Importing the requirements
import warnings
warnings.filterwarnings("ignore")
import gradio as gr
from src.video_model import describe_video
# Video and text inputs for the interface
video = gr.Video(label="Video")
query = gr.Textbox(label="Question", placeholder="Enter your question here")
# Output for the interface
response = gr.Textbox(label="Predicted answer", show_label=True, show_copy_button=True)
# Examples for the interface
examples = [
[
"videos/2016-01-01_0100_US_KNBC_Channel_4_News_1867.16-1871.38_now.mp4",
"Here are some frames of a video. Describe this video in detail.",
]
# [
# ".videos/2016-01-01_0200_US_KNBC_Channel_4_News_1329.12-1333.29_tonight.mp4",
# "Here are some frames of a video. Describe this video in detail.",
# ],
# [" .videos/2016-01-01_0830_US_KNBC_Tonight_Show_with_Jimmy_Fallon_725.45-729.76_tonight.mp4",
# "Here are some frames of a video. Describe this video in detail.",],
]
# Title, description, and article for the interface
title = "GSoC Super Raid Annotator"
# description = "Gradio Demo for the MiniCPM-V 2.6 Vision Language Understanding and Generation model. This model can answer questions about videos in natural language. To use it, simply upload your video, type a question, and click 'submit', or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://github.com/OpenBMB/MiniCPM-V' target='_blank'>Model GitHub Repo</a> | <a href='https://huggingface.co/openbmb/MiniCPM-V-2_6' target='_blank'>Model Page</a></p>"
# Launch the interface
interface = gr.Interface(
fn=describe_video,
inputs=[video, query],
outputs=response,
examples=examples,
title=title,
description=description,
article=article,
theme="Soft",
allow_flagging="never",
)
interface.launch(debug=False)
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