corrección de chatgpt

app.py

@@ -1,46 +1,45 @@
 import os
 import gradio as gr
-
-api_key = os.getenv('HF_API_KEY')
-
-# with gr.Blocks() as demo:
-#     gr.load("fffiloni/sd-xl-custom-model", api_key=api_key, hf_token=api_key, src="spaces")
-
 from gradio_client import Client
 
+api_key = os.getenv('HF_API_KEY')
 
-
+custom_model = "lichorosario/dott_remastered_style_lora_sdxl"
+weight_name = "dott_style.safetensors"
 
 def predict(prompt):
-	custom_model="lichorosario/dott_remastered_style_lora_sdxl"
-	weight_name="dott_style.safetensors"
-	client = Client("fffiloni/sd-xl-custom-model")
-	result = client.predict(
-			custom_model=custom_model,
-			api_name="/load_model"
-	)
-
-	client = Client("fffiloni/sd-xl-custom-model")
-	result = client.predict(
-	custom_model=custom_model,
-	weight_name=weight_name,
-	prompt=prompt,
-	inf_steps=25,
-	guidance_scale=12,
-	width=1024,
-	height=512,
-	seed=-1,
-	lora_weight=1,
-	api_name="/infer"
-	)
-
-	return result
+    client = Client("fffiloni/sd-xl-custom-model", api_key=api_key)
+    
+    # Load the model
+    client.predict(
+        custom_model=custom_model,
+        api_name="/load_model"
+    )
+
+    # Infer the image
+    result = client.predict(
+        custom_model=custom_model,
+        weight_name=weight_name,
+        prompt=prompt,
+        inf_steps=25,
+        guidance_scale=12,
+        width=1024,
+        height=512,
+        seed=-1,
+        lora_weight=1,
+        api_name="/infer"
+    )
+
+    # Assuming result is a dictionary with keys 'image' and 'seed'
+    image = result["image"]
+    seed = result["seed"]
+
+    return image, seed
 
 with gr.Blocks() as demo:
-	inputs = gr.Textbox(label="Prompt")
-	outputs = [gr.Image(label="Image"), gr.Textbox(label="Seed")]
-	greet_btn = gr.Button("Generate")
-	greet_btn.click(fn=predict, inputs=inputs, outputs=outputs, api_name="predict")
+    inputs = gr.Textbox(label="Prompt")
+    outputs = [gr.Image(label="Image"), gr.Textbox(label="Seed")]
+    greet_btn = gr.Button("Generate")
+    greet_btn.click(fn=predict, inputs=inputs, outputs=outputs)
 
 demo.launch()
-