vuelvo a la prueba original

app.py

@@ -1,146 +1,25 @@
-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
 
-custom_model="lichorosario/dott_remastered_style_lora_sdxl",
-weight_name="dott_style.safetensors",
 
-def infer (prompt, inf_steps, guidance_scale, width, height, seed, lora_weight):    
+def predict():
     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="dott style. "+prompt,
-		# inf_steps=inf_steps,
-		# guidance_scale=guidance_scale,
-		# width=width,
-		# height=height,
-		# seed=-seed,
-		# lora_weight=lora_weight,
-		# api_name="/infer"
-  #   )
+    		custom_model="lichorosario/dott_remastered_style_lora_sdxl",
+    result = client.predict(
+		custom_model="lichorosario/dott_remastered_style_lora_sdxl",
+		weight_name="dott_style.safetensors",
+		prompt="house interior. two vanishing points, one at the left and one at the right.  Cartoon background. Curved perspective. Retro cartoon scene. twisted objects. simple irregular shapes. Cartoon Expressionism.foreshortening. wide angle.  lens distortion. converging lines",
+		inf_steps=25,
+		guidance_scale=12,
+		width=1024,
 
     return result
 
 
+demo = gr.Interface(
+    fn=predict,
+    inputs=[],
+    outputs=["image", "text"],
+)
 
-
-css="""
-#col-container{
-    margin: 0 auto;
-    max-width: 720px;
-    text-align: left;
-}
-div#warning-duplicate {
-    background-color: #ebf5ff;
-    padding: 0 16px 16px;
-    margin: 20px 0;
-}
-div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
-    color: #0f4592!important;
-}
-div#warning-duplicate strong {
-    color: #0f4592;
-}
-p.actions {
-    display: flex;
-    align-items: center;
-    margin: 20px 0;
-}
-div#warning-duplicate .actions a {
-    display: inline-block;
-    margin-right: 10px;
-}
-button#load_model_btn{
-    height: 46px;
-}
-#status_info{
-    font-size: 0.9em;
-}
-.custom-color {
-    color: #030303 !important;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        prompt_in = gr.Textbox(
-          label="Your Prompt",
-          info = "Dont' forget to include your trigger word if necessary"
-        )   
-        with gr.Accordion("Advanced Settings", open=False):
-            with gr.Row():
-                inf_steps = gr.Slider(
-                    label="Inference steps",
-                    minimum=12,
-                    maximum=50,
-                    step=1,
-                    value=25
-                )
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=50.0,
-                    step=0.1,
-                    value=7.5
-                )
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=2048,
-                    step=32,
-                    value=1024,
-                )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=2048,
-                    step=32,
-                    value=1024,
-                )
-    
-            with gr.Row():
-                seed = gr.Slider(
-                    label="Seed",
-                    info = "-1 denotes a random seed",
-                    minimum=-1,
-                    maximum=423538377342,
-                    step=1,
-                    value=-1
-                )
-                last_used_seed = gr.Number(
-                    label = "Last used seed",
-                    info = "the seed used in the last generation",
-                )
-            lora_weight = gr.Slider(
-                label="LoRa weigth",
-                minimum=0.0,
-                maximum=1.0,
-                step=0.01,
-                value=1.0
-            )
-    submit_btn = gr.Button("Submit")
-    image_out = gr.Image(label="Image output")
-
-    submit_btn.click(
-        fn = infer,
-        inputs = [prompt_in, inf_steps, guidance_scale, width, height, seed, lora_weight],
-        outputs = [last_used_seed]
-    )
-
-
-demo.launch()
-
+demo.launch()