Add New Version

app.py

@@ -10,7 +10,6 @@ from typing import Dict, Any
 from fastapi import FastAPI, HTTPException, File, UploadFile
 from pydantic import BaseModel
 import gradio as gr
-import shutil
 import tempfile
 
 app = FastAPI()
@@ -40,7 +39,7 @@ def load_emotion_model(model_path, device='cuda' if torch.cuda.is_available() el
     return model
 
 # Process the uploaded video (either MP4 or WebM)
-def process_video(video_file: UploadFile) -> Dict[str, Any]:
+async def process_video(video_file: UploadFile) -> Dict[str, Any]:
     global largest_face_detections
     largest_face_detections = []  # Reset detections for new video
     
@@ -64,11 +63,13 @@ def process_video(video_file: UploadFile) -> Dict[str, Any]:
         
     emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
     
-    # Save the uploaded video file to a temporary directory
+    # Save the uploaded video file to a temporary directory without using shutil
     temp_dir = tempfile.mkdtemp()
     video_path = os.path.join(temp_dir, "uploaded_video")
-    with open(video_path, "wb") as buffer:
-        shutil.copyfileobj(video_file.file, buffer)
+    
+    # Open the video file stream and save it as a local file
+    with open(video_path, "wb") as f:
+        f.write(await video_file.read())
     
     cap = cv2.VideoCapture(video_path)
     if not cap.isOpened():
@@ -166,7 +167,7 @@ class VideoRequest(BaseModel):
 @app.post("/process_video/")
 async def process_video_request(file: UploadFile = File(...)):
     try:
-        results = process_video(file)
+        results = await process_video(file)
         return results
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))