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import gradio as gr
from deepface import DeepFace
from transformers import pipeline
import tempfile
import cv2
import moviepy.editor as mp
def analyze_text(text):
classifier = pipeline("sentiment-analysis")
return classifier(text)[0]['label']
def analyze_video_emotion(video_file):
try:
# Save the uploaded video to a temp file
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp:
tmp.write(video_file.read())
tmp_path = tmp.name
# Extract frames using MoviePy (more reliable than OpenCV alone)
video = mp.VideoFileClip(tmp_path)
frames = list(video.iter_frames())
emotions = []
for frame in frames[:60]: # Limit to first 60 frames
try:
# Use DeepFace for emotion detection
result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
emotions.append(result[0]['dominant_emotion'])
except Exception as e:
print("Error analyzing frame:", e)
if emotions:
# Return the most common emotion
return max(set(emotions), key=emotions.count)
else:
return "No face detected"
except Exception as e:
print("Error processing video:", e)
return "Error processing video file"
def process_all(text_input, video_input):
text_result = analyze_text(text_input)
video_result = analyze_video_emotion(video_input)
return f"Text Sentiment: {text_result}\nFacial Emotion: {video_result}"
iface = gr.Interface(
fn=process_all,
inputs=[
gr.Textbox(label="Enter Social Media Text"),
gr.Video(label="Upload a Video Clip")
],
outputs="text",
title="Emotion & Sentiment Decoder",
description="Analyzes social media text & facial expressions from video."
)
iface.launch() |