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import gradio as gr
from inference import model_fn, predict_fn
import os
model_dict = model_fn(".")
def predict(video):
temp_path = "temp.mp4"
try:
# Gradio gives a file path for video input
if isinstance(video, str) and os.path.exists(video):
os.rename(video, temp_path)
else:
with open(temp_path, "wb") as f:
f.write(video.read())
input_data = {"video_path": temp_path}
result = predict_fn(input_data, model_dict)
return result
finally:
if os.path.exists(temp_path):
os.remove(temp_path)
demo = gr.Interface(
fn=predict,
inputs=gr.Video(type="filepath"),
outputs="json",
title="Video Sentiment Analysis",
description="Upload an .mp4 video to get sentiment and emotion predictions for each utterance."
)
if __name__ == "__main__":
demo.launch()
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