Create app.py

app.py

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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 analyze_video(video_file):
+    if video_file is None:
+        return "No video uploaded"
+
+    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()
+
+    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}"
+
+interface = gr.Interface(
+    fn=analyze_post,
+    inputs=[
+        gr.Textbox(label="Enter post text"),
+        gr.File(label="Upload short video (mp4)", file_types=[".mp4"])
+    ],
+    outputs="text",
+    title="📱 Emotion & Sentiment Analyzer",
+    description="Analyze sentiment from text and facial emotion from video in one go."
+)
+
+interface.launch()