Update app.py

app.py

@@ -1,22 +1,32 @@
-import gradio as gr
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 import torch
 from diffusers import StableVideoDiffusionPipeline
+import uuid
 
-# Load Model
+app = FastAPI()
+
+# ✅ Model Load करो (GPU अगर Available है तो)
+device = "cuda" if torch.cuda.is_available() else "cpu"
 pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid")
-pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+pipe.to(device)
 
-def generate_video(prompt):
-    video_frames = pipe(prompt, num_inference_steps=50).frames
-    video_path = "output.mp4"
-    video_frames[0].save(video_path)
-    return video_path
+# ✅ Request Body Format
+class VideoRequest(BaseModel):
+    prompt: str
 
-iface = gr.Interface(
-    fn=generate_video,
-    inputs=gr.Textbox(label="Enter Prompt"),
-    outputs=gr.Video(label="Generated Video"),
-    title="AI Video Generator"
-)
+# ✅ 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))
 
-iface.launch()
+# ✅ Root Endpoint Check करने के लिए
+@app.get("/")
+def home():
+    return {"message": "AI Video Generator API is running!"}