Update app.py

app.py

@@ -1,32 +1,34 @@
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI
 from pydantic import BaseModel
 import torch
 from diffusers import StableVideoDiffusionPipeline
-import uuid
 
+# API Init
 app = FastAPI()
 
-# ✅ Model Load करो (GPU अगर Available है तो)
+# Load Model
 device = "cuda" if torch.cuda.is_available() else "cpu"
 pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid")
 pipe.to(device)
 
-# ✅ Request Body Format
+# Request Model
 class VideoRequest(BaseModel):
     prompt: str
 
-# ✅ API Endpoint: Video Generate करने के लिए
-@app.post("/generate-video")
-async def generate_video(request: VideoRequest):
-    try:
-        video_frames = pipe(request.prompt, num_inference_steps=50).frames
-        video_path = f"output_{uuid.uuid4().hex}.mp4"
-        video_frames[0].save(video_path)
-        return {"message": "Video generated successfully!", "video_url": video_path}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-# ✅ Root Endpoint Check करने के लिए
+# Root Endpoint
 @app.get("/")
 def home():
-    return {"message": "AI Video Generator API is running!"}
+    return {"message": "AI Video Generator API is running!"}
+
+# Generate Video Endpoint
+@app.post("/generate-video")
+def generate_video(request: VideoRequest):
+    video_frames = pipe(request.prompt, num_inference_steps=50).frames
+    video_path = "output.mp4"
+    video_frames[0].save(video_path)
+    return {"message": "Video generated successfully!", "video_url": video_path}
+
+# Run API
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)