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import gradio as gr
from textblob import TextBlob
from deepface import DeepFace
import moviepy.editor as mp
import cv2
import tempfile
import os
def analyze_text(text):
blob = TextBlob(text)
polarity = blob.sentiment.polarity
sentiment = "Positive" if polarity > 0 else "Negative" if polarity < 0 else "Neutral"
return f"Text Sentiment: {sentiment} (Polarity: {polarity:.2f}
def analyze_image(image):
try:
result = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False)
dominant_emotion = result[0]['dominant_emotion']
return f"Detected Emotion: {dominant_emotion}"
except Exception as e:
return f"Error: {str(e)}"
def analyze_video(video_file):
try:
tmpdir = tempfile.mkdtemp()
clip = mp.VideoFileClip(video_file)
frame = clip.get_frame(clip.duration / 2)
frame_path = os.path.join(tmpdir, "frame.jpg")
cv2.imwrite(frame_path, cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
result = DeepFace.analyze(frame_path, actions=['emotion'], enforce_detection=False)
dominant_emotion = result[0]['dominant_emotion']
return f"Video Emotion: {dominant_emotion}"
except Exception as e:
return f"Error: {str(e)}"
with gr.Blocks() as demo:
gr.Markdown("# 🧠 Emotion and Sentiment Analyzer")
with gr.Tab("Text Analysis"):
text_input = gr.Textbox(label="Enter Text")
text_output = gr.Textbox(label="Sentiment Result")
text_btn = gr.Button("Analyze Text")
text_btn.click(analyze_text, inputs=text_input, outputs=text_output)
with gr.Tab("Image Analysis"):
img_input = gr.Image(type="filepath", label="Upload Face Image")
img_output = gr.Textbox(label="Emotion Result")
img_btn = gr.Button("Analyze Image")
img_btn.click(analyze_image, inputs=img_input, outputs=img_output)
with gr.Tab("Video Analysis"):
video_input = gr.Video(label="Upload Face Video")
video_output = gr.Textbox(label="Emotion Result")
video_btn = gr.Button("Analyze Video")
video_btn.click(analyze_video, inputs=video_input, outputs=video_output)
demo.launch() |