adffrim?

Dockerfile

@@ -35,4 +35,4 @@ USER appuser
 EXPOSE 7860
 
 # Run the server
-CMD ["python", "/app/intruct.py"]
+CMD ["python", "/app/runway.py"]

runway.py

@@ -0,0 +1,74 @@
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from PIL import Image
+import io
+import torch
+from diffusers import StableDiffusionInpaintPipeline
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load the pre-trained inpainting model (Stable Diffusion)
+model_id = "runwayml/stable-diffusion-inpainting"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+try:
+    pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id)
+    pipe.to(device)
+except Exception as e:
+    raise RuntimeError(f"Failed to load model: {e}")
+
+@app.get("/")
+def read_root():
+    return {"message": "Welcome to the Image Inpainting API!"}
+
+@app.post("/inpaint/")
+async def inpaint_image(
+    image: UploadFile = File(...),
+    mask: UploadFile = File(...),
+    prompt: str = "Fill the masked area with appropriate content."
+):
+    """
+    Endpoint for image inpainting.
+    - `image`: Original image file (PNG/JPG).
+    - `mask`: Mask file indicating areas to inpaint (black for masked areas, white for unmasked).
+    - `prompt`: Text prompt describing the desired output.
+    """
+    try:
+        # Load the uploaded image and mask
+        image_bytes = await image.read()
+        mask_bytes = await mask.read()
+
+        original_image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        mask_image = Image.open(io.BytesIO(mask_bytes)).convert("L")
+
+        # Ensure dimensions match between image and mask
+        if original_image.size != mask_image.size:
+            raise HTTPException(status_code=400, detail="Image and mask dimensions must match.")
+
+        # Perform inpainting using the pipeline
+        result = pipe(prompt=prompt, image=original_image, mask_image=mask_image).images[0]
+
+        # Convert result to bytes for response
+        result_bytes = io.BytesIO()
+        result.save(result_bytes, format="PNG")
+        result_bytes.seek(0)
+
+        return JSONResponse(content={"message": "Inpainting successful!"}, media_type="image/png")
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error during inpainting: {e}")
+
+
+
+
+@app.get("/")
+async def root():
+    """
+    Root endpoint for basic health check.
+    """
+    return {"message": "InstructPix2Pix API is running. Use POST /edit-image/ or /inpaint/ to edit images."}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)