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import gradio as gr
import cv2
from deepface import DeepFace
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import tempfile
analyzer = SentimentIntensityAnalyzer()
def analyze_text(text):
score = analyzer.polarity_scores(text)
if score['compound'] >= 0.05:
return "Positive 😊"
elif score['compound'] <= -0.05:
return "Negative 😠"
else:
return "Neutral 😐"
def process_all(text, video):
text_sentiment = analyze_sentiment(text)
video_emotion = analyze_video_emotion(video)
return f"Text Sentiment: {text_sentiment}\nFacial Emotion: {video_emotion}"
iface = gr.Interface(
fn=process_all,
inputs=[gr.Textbox(label="Social Media Post"), gr.Video(label="Upload Video")],
outputs="text",
title="Emotion & Sentiment Analyzer"
)
iface.launch()
def analyze_video(video_file):
if video_file is None:
return "No video uploaded"
# Save uploaded file temporarily
temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
with open(temp_path, "wb") as f:
f.write(video_file.read())
cap = cv2.VideoCapture(temp_path)
success, frame = cap.read()
cap.release()
def analyze_video_emotion(video_file):
# 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
cap = cv2.VideoCapture(tmp_path)
emotions = []
frame_count = 0
import cv2
import tempfile
from deepface import DeepFace
def analyze_video_emotion(video_file):
# 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
cap = cv2.VideoCapture(tmp_path)
emotions = []
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret or frame_count > 60: # Limit to first 60 frames
break
try:
result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
emotions.append(result[0]['dominant_emotion'])
except Exception as e:
print("Error analyzing frame:", e)
frame_count += 1
cap.release()
if emotions:
# Return most frequent emotion
return max(set(emotions), key=emotions.count)
else:
return "No emotion detected or face not found"
while cap.isOpened():
ret, frame = cap.read()
if not ret or frame_count > 60: # Limit to 60 frames max
break
try:
result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
emotions.append(result[0]['dominant_emotion'])
except:
pass
frame_count += 1
cap.release()
if emotions:
# Return most common emotion
return max(set(emotions), key=emotions.count)
else:
return "No face detected"
if not success:
return "Could not read video"
try:
result = DeepFace.analyze(frame, actions=["emotion"], enforce_detection=False)
return result[0]['dominant_emotion'].capitalize()
except Exception as e:
return f"Error: {str(e)}"
def analyze_post(text, video):
sentiment = analyze_text(text)
emotion = analyze_video(video)
return f"📝 Sentiment: {sentiment}\n🎥 Emotion: {emotion}"
import gradio as gr
def analyze_text(text):
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
return classifier(text)[0]['label']
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}"
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."
).launch()
interface = gr.Interface(
fn=analyze_post,
inputs=[
gr.Textbox(label="Post Text", placeholder="Enter your message here"),
gr.File(label="Upload video (.mp4)", file_types=[".mp4"])
],
outputs="text",
title="📱 Emotion & Sentiment Analyzer",
description="Analyze text sentiment and facial emotion from video. No re-running needed. Permanent on Hugging Face."
)
interface.launch()import gradio as gr
import cv2
from deepface import DeepFace
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import tempfile
analyzer = SentimentIntensityAnalyzer()
def analyze_text(text):
score = analyzer.polarity_scores(text)
if score['compound'] >= 0.05:
return "Positive 😊"
elif score['compound'] <= -0.05:
return "Negative 😠"
else:
return "Neutral 😐"
def process_all(text, video):
text_sentiment = analyze_sentiment(text)
video_emotion = analyze_video_emotion(video)
return f"Text Sentiment: {text_sentiment}\nFacial Emotion: {video_emotion}"
iface = gr.Interface(
fn=process_all,
inputs=[gr.Textbox(label="Social Media Post"), gr.Video(label="Upload Video")],
outputs="text",
title="Emotion & Sentiment Analyzer"
)
iface.launch()
def analyze_video(video_file):
if video_file is None:
return "No video uploaded"
# Save uploaded file temporarily
temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
with open(temp_path, "wb") as f:
f.write(video_file.read())
cap = cv2.VideoCapture(temp_path)
success, frame = cap.read()
cap.release()
def analyze_video_emotion(video_file):
# 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
cap = cv2.VideoCapture(tmp_path)
emotions = []
frame_count = 0
import cv2
import tempfile
from deepface import DeepFace
def analyze_video_emotion(video_file):
# 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
cap = cv2.VideoCapture(tmp_path)
emotions = []
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret or frame_count > 60: # Limit to first 60 frames
break
try:
result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
emotions.append(result[0]['dominant_emotion'])
except Exception as e:
print("Error analyzing frame:", e)
frame_count += 1
cap.release()
if emotions:
# Return most frequent emotion
return max(set(emotions), key=emotions.count)
else:
return "No emotion detected or face not found"
while cap.isOpened():
ret, frame = cap.read()
if not ret or frame_count > 60: # Limit to 60 frames max
break
try:
result = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
emotions.append(result[0]['dominant_emotion'])
except:
pass
frame_count += 1
cap.release()
if emotions:
# Return most common emotion
return max(set(emotions), key=emotions.count)
else:
return "No face detected"
if not success:
return "Could not read video"
try:
result = DeepFace.analyze(frame, actions=["emotion"], enforce_detection=False)
return result[0]['dominant_emotion'].capitalize()
except Exception as e:
return f"Error: {str(e)}"
def analyze_post(text, video):
sentiment = analyze_text(text)
emotion = analyze_video(video)
return f"📝 Sentiment: {sentiment}\n🎥 Emotion: {emotion}"
import gradio as gr
def analyze_text(text):
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
return classifier(text)[0]['label']
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}"
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."
).launch()
interface = gr.Interface(
fn=analyze_post,
inputs=[
gr.Textbox(label="Post Text", placeholder="Enter your message here"),
gr.File(label="Upload video (.mp4)", file_types=[".mp4"])
],
outputs="text",
title="📱 Emotion & Sentiment Analyzer",
description="Analyze text sentiment and facial emotion from video. No re-running needed. Permanent on Hugging Face."
if text_input:
# Process text only
elif video_input:
# Process video only
else:
return "No input provided"
)
interface.launch() |