prueba2

app.py

@@ -1,19 +1,18 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForTextToImageGeneration
-import torch
+import requests
+import io
+from PIL import Image
 
-# Cargar el tokenizador y el modelo
-tokenizer = AutoTokenizer.from_pretrained("ZB-Tech/Text-to-Image")
-model = AutoModelForTextToImageGeneration.from_pretrained("ZB-Tech/Text-to-Image")
+API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
+headers = {"Authorization": "Bearer HF_API_KEY"}
 
-# Definir la función de generación de imagen
 def generate_image(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
-    with torch.no_grad():
-        output = model.generate(**inputs)
-    return output
+    payload = {"inputs": prompt}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    image_bytes = response.content
+    image = Image.open(io.BytesIO(image_bytes))
+    return image
 
-# Crear la interfaz de Gradio
 iface = gr.Interface(
     fn=generate_image,
     inputs=gr.Textbox(lines=5, label="Descripción de la imagen", placeholder="Introduce el texto aquí..."),
@@ -36,5 +35,4 @@ iface = gr.Interface(
     """
 )
 
-# Ejecutar la interfaz
 iface.launch()