Update app.py

app.py

@@ -40,11 +40,10 @@ class EmotionDetector:
         self.face_detector = MTCNN(keep_all=False, device=self.device)
         self.transform = transforms.Compose([
             transforms.Resize((48, 48))
-            # No ToTensor() here – face already comes as tensor
         ])
         self.softmax = torch.nn.Softmax(dim=1)
 
-        # OPTIONAL: load pre-trained weights if available
+        # Load pre-trained weights here if available
         # self.model.load_state_dict(torch.load("emotion_model.pt", map_location=self.device))
 
     def detect_emotions_video(self, video_path, sample_rate=30, max_size_mb=50):
@@ -79,8 +78,8 @@ class EmotionDetector:
                     continue
 
                 face_tensor = self.transform(face_tensor)  # Resize
-                face_tensor = face_tensor.mean(dim=0, keepdim=True)  # convert to grayscale
-                face_tensor = face_tensor.unsqueeze(0).to(self.device)  # Add batch + channel
+                face_tensor = face_tensor.mean(dim=0, keepdim=True)  # grayscale
+                face_tensor = face_tensor.unsqueeze(0).to(self.device)  # batch + channel
 
                 with torch.no_grad():
                     output = self.model(face_tensor)
@@ -132,23 +131,32 @@ class EmotionDetector:
 def create_interface():
     detector = EmotionDetector()
 
-    def process(video, sample_rate):
-        if video is None:
+    def process(video_file, sample_rate):
+        if video_file is None:
             return None, "Please upload a video."
-        return detector.detect_emotions_video(video, sample_rate)
+
+        try:
+            # Read and save to a temporary file
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp:
+                tmp.write(video_file.read())
+                tmp_path = tmp.name
+
+            return detector.detect_emotions_video(tmp_path, sample_rate)
+        except Exception as e:
+            return None, f"Error processing video: {str(e)}"
 
     return gr.Interface(
         fn=process,
         inputs=[
-            gr.Video(label="Upload Video"),
+            gr.Video(label="Upload Video", type="file"),  # file-like object
             gr.Slider(minimum=1, maximum=60, step=1, value=30, label="Sample Rate (Frames)")
         ],
         outputs=[
             gr.Image(type="pil", label="Emotion Chart"),
             gr.Textbox(label="Analysis Summary")
         ],
-        title="PyTorch-Only Emotion Detection",
-        description="Analyze emotions from a video using PyTorch and facenet-pytorch."
+        title="AI Emotion Detection",
+        description="Upload a video to analyze emotions over time."
     )
 
 if __name__ == "__main__":