Update app.py

app.py

@@ -1,59 +1,33 @@
-import os
-import random
-import uuid
 import gradio as gr
-import numpy as np
-from PIL import Image
-import torch
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
-from typing import Tuple
+import requests
 
-css = '''
-.gradio-container{max-width: 575px !important}
-h1{text-align:center}
-footer {
-    visibility: hidden
-}
-'''
+# Função para gerar imagem usando a API do Hugging Face
+def generate_image(prompt: str):
+    API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
+    headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_API_KEY"}
 
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
-pipe = StableDiffusionXLPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16, revision="fp16")
-pipe = pipe.to("cuda")
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
 
-def generate_image(prompt: str, negative_prompt: str = "", height: int = 512, width: int = 512, num_inference_steps: int = 50, guidance_scale: float = 7.5, num_images_per_prompt: int = 1) -> Tuple[Image.Image, str]:
-    generator = torch.Generator(device="cuda").manual_seed(random.randint(0, 2**32 - 1))
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        height=height,
-        width=width,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        num_images_per_prompt=num_images_per_prompt,
-        generator=generator,
-    ).images[0]
-
-    image_id = str(uuid.uuid4())
-    image_path = f"/tmp/{image_id}.png"
-    image.save(image_path)
-
-    return image, image_path
+    if response.status_code == 200:
+        image_url = response.json()
+        return image_url
+    else:
+        return None
 
+# Função para criar a interface do Gradio
 def gradio_interface():
-    with gr.Blocks(css=css) as demo:
-        gr.Markdown("## Gere imagens usando Stable Diffusion XL")
+    with gr.Blocks() as demo:
+        gr.Markdown("## Gere imagens usando Stable Diffusion")
         with gr.Row():
             with gr.Column():
                 prompt = gr.Textbox(label="Prompt", placeholder="Digite o prompt aqui...")
-                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Digite o negative prompt aqui...")
                 run_button = gr.Button("Gerar Imagem")
             with gr.Column():
                 result = gr.Image(label="Imagem Gerada")
 
         run_button.click(
-            fn=lambda p, np: generate_image(p, np)[0],
-            inputs=[prompt, negative_prompt],
+            fn=generate_image,
+            inputs=prompt,
             outputs=result,
         )