New Try

app.py

@@ -278,28 +278,45 @@ def process_video(video_path: str) -> Dict[str, Any]:
         return {
             "success": True,
             "message": "No faces detected in video",
-            "results": [],
+            "results": {
+                "average_emotions": {},
+                "dominant_emotion": None,
+                "detections": [],
+                "summary": {
+                    "total_frames": total_frames,
+                    "total_detections": 0
+                }
+            },
             "error": None
         }
     
-    # Calculate summary statistics
-    emotions_count = {}
+    emotion_scores = {e: [] for e in emotions}  # Initialize with all emotion types
+    
     for detection in largest_face_detections:
         emotion = detection['emotion']
-        emotions_count[emotion] = emotions_count.get(emotion, 0) + 1
+        confidence = detection['confidence']
+        emotion_scores[emotion].append(confidence)
     
-    # Get dominant emotion
-    dominant_emotion = max(emotions_count.items(), key=lambda x: x[1])[0]
+    # Calculate summary statistics
+    average_emotions = {
+        e: sum(scores)/len(scores) if scores else 0 
+        for e, scores in emotion_scores.items()
+    }
+    
+    # Get dominant emotion based on average confidence
+    dominant_emotion = max(average_emotions.items(), key=lambda x: x[1])[0]
     
     return {
         "success": True,
         "message": "Video processed successfully",
         "results": {
-            "detections": largest_face_detections,
+            "average_emotions": average_emotions,
+            "dominant_emotion": dominant_emotion,
+            "detections": largest_face_detections,  # Optional: include all detections
             "summary": {
                 "total_frames": total_frames,
                 "total_detections": len(largest_face_detections),
-                "emotions_count": emotions_count,
+                "emotions_count": {e: len(s) for e, s in emotion_scores.items()},
                 "dominant_emotion": dominant_emotion
             }
         },
@@ -317,14 +334,22 @@ def gradio_analyze_video(video_path: str):
     summary = result["results"]["summary"]
     detections = result["results"]["detections"]
     
+    # output = {
+    #     "summary": {
+    #         "total_frames": summary["total_frames"],
+    #         "faces_detected": summary["total_detections"],
+    #         "dominant_emotion": summary["dominant_emotion"],
+    #         "emotion_distribution": summary["emotions_count"]
+    #     },
+    #     "sample_detections": detections[:5]  # Show first 5 detections
+    # }
+    # return output
+
     output = {
-        "summary": {
-            "total_frames": summary["total_frames"],
-            "faces_detected": summary["total_detections"],
-            "dominant_emotion": summary["dominant_emotion"],
-            "emotion_distribution": summary["emotions_count"]
-        },
-        "sample_detections": detections[:5]  # Show first 5 detections
+        "average_emotions": result["results"]["average_emotions"],
+        "dominant_emotion": result["results"]["dominant_emotion"],
+        "frames_analyzed": result["results"]["summary"]["total_frames"],
+        "faces_detected": result["results"]["summary"]["total_detections"]
     }
     return output
 
@@ -404,7 +429,7 @@ async def root():
     """
 
 # Mount Gradio app to FastAPI
-app = gr.mount_gradio_app(app, demo, path="/gradio")
+app = gr.mount_gradio_app(app, demo, path="/")
 
 if __name__ == "__main__":
     import uvicorn