Update README.md

README.md

@@ -27,38 +27,11 @@ You should use `tugce` to trigger the image generation.
 ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
 
 ```py
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import FileResponse
-import torch
 from diffusers import AutoPipelineForText2Image
-import io
-
-app = FastAPI()
-
-# Model ve LoRA'yı yükle
+import torch
 pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
 pipeline.load_lora_weights('codermert/tugce2-lora', weight_name='flux_train_replicate.safetensors')
-
-@app.post("/generate_image")
-async def generate_image(prompt: str, width: int, height: int):
-    try:
-        image = pipeline(
-            prompt,
-            width=width,
-            height=height
-        ).images[0]
-        
-        img_byte_arr = io.BytesIO()
-        image.save(img_byte_arr, format='PNG')
-        img_byte_arr.seek(0)
-        
-        return FileResponse(img_byte_arr, media_type="image/png")
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+image = pipeline('your prompt').images[0]
 ```
 
 For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)