Update app.py

app.py

@@ -1,9 +1,9 @@
-import gradio as gr
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 import numpy as np
 import random
 import torch
 from diffusers import DiffusionPipeline
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 import boto3
 from io import BytesIO
 import time
@@ -27,6 +27,17 @@ pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_d
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
+app = FastAPI()
+
+class InferenceRequest(BaseModel):
+    prompt: str
+    seed: int = 42
+    randomize_seed: bool = False
+    width: int = 1024
+    height: int = 1024
+    guidance_scale: float = 5.0
+    num_inference_steps: int = 28
+
 def save_image_to_s3(image):
     img_byte_arr = BytesIO()
     image.save(img_byte_arr, format='PNG')
@@ -42,107 +53,31 @@ def save_image_to_s3(image):
     url = f"https://{S3_BUCKET}.s3.{S3_REGION}.amazonaws.com/{filename}"
     return url
 
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-    if randomize_seed:
+@app.post("/infer")
+async def infer(request: InferenceRequest):
+    if request.randomize_seed:
         seed = random.randint(0, MAX_SEED)
+    else:
+        seed = request.seed
+
     generator = torch.Generator().manual_seed(seed)
-    image = pipe(
-        prompt=prompt, 
-        width=width,
-        height=height,
-        num_inference_steps=num_inference_steps, 
-        generator=generator,
-        guidance_scale=guidance_scale
-    ).images[0]
     
-    image_url = save_image_to_s3(image)
-    
-    return image_url, seed
-
-examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
-        """)
-        
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Text(label="Image URL", show_label=True)
+    try:
+        image = pipe(
+            prompt=request.prompt, 
+            width=request.width,
+            height=request.height,
+            num_inference_steps=request.num_inference_steps, 
+            generator=generator,
+            guidance_scale=request.guidance_scale
+        ).images[0]
         
-        with gr.Accordion("Advanced Settings", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=15,
-                    step=0.1,
-                    value=3.5,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=28,
-                )
+        image_url = save_image_to_s3(image)
         
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed],
-            cache_examples="lazy"
-        )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result, seed]
-    )
+        return {"image_url": image_url, "seed": seed}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 
-demo.launch(share=True)
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the IG API"}