Update app.py

app.py

@@ -1,72 +1,20 @@
 import gradio as gr
-import numpy as np
-import random
 import torch
 import os
-from diffusers import DiffusionPipeline
-from huggingface_hub import login
-
-# Retrieve the Hugging Face API token from the environment variable
-api_token = os.getenv('HUGGINGFACE_API_TOKEN')
-if not api_token:
-    raise ValueError("HUGGINGFACE_API_TOKEN environment variable not set.")
-
-# Log in to Hugging Face Hub
-login(token=api_token)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-):
-    # Load the pipeline only when this function is called
-    pipe = DiffusionPipeline.from_pretrained(
-        "black-forest-labs/FLUX.1-dev", use_auth_token=api_token
-    )
-    pipe.load_lora_weights("EvanZhouDev/open-genmoji", use_auth_token=api_token)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    pipe = pipe.to(device)
-
-    # Handle seed randomization
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.manual_seed(seed)
-
-    # Generate the image using the pipeline
-    result = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt if negative_prompt else None,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-    ).images[0]
-
-    return result, seed
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
+import requests
+import io
+from PIL import Image
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
+headers = {"Authorization": "Bearer " + os.getenv("HF_TOKEN")}
+def infer(prompt):
+    def query(payload):
+    	response = requests.post(API_URL, headers=headers, json=payload)
+    	return response.content
+    image_bytes = query({
+    	"inputs": prompt,
+    })
+
+with gr.Blocks() as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(" # Text-to-Image Gradio Template")
 
@@ -81,68 +29,15 @@ with gr.Blocks(css=css) as demo:
             run_button = gr.Button("Run", scale=0, variant="primary")
             result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=True,
-            )
-            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=512,
-                )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=7.5,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=25,
-                )
 
-        gr.Examples(examples=examples, inputs=[prompt])
 
         # Run inference when run_button is clicked
         run_button.click(
             infer,
             inputs=[
-                prompt,
-                negative_prompt,
-                seed,
-                randomize_seed,
-                width,
-                height,
-                guidance_scale,
-                num_inference_steps,
+                prompt
             ],
-            outputs=[result, seed],
+            outputs=[result],
         )
 
 if __name__ == "__main__":