Update app.py

app.py

@@ -149,6 +149,65 @@ interface = gr.Interface(
         gr.Textbox(label="Post Text", placeholder="Enter your message here"),
         gr.File(label="Upload video (.mp4)", file_types=[".mp4"])
     ],
+    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()
+
     outputs="text",
     title="📱 Emotion & Sentiment Analyzer",
     description="Analyze text sentiment and facial emotion from video. No re-running needed. Permanent on Hugging Face."