Update app.py

app.py

@@ -8,35 +8,139 @@ api_key = os.getenv('HF_API_KEY')
 
 from gradio_client import Client
 
+custom_model="lichorosario/dott_remastered_style_lora_sdxl",
+weight_name="dott_style.safetensors",
 
-def predict(prompt):
+def infer (custom_model, weight_name, prompt, inf_steps, guidance_scale, width, height, seed, lora_weight, progress=gr.Progress(track_tqdm=True)):    
     client = Client("fffiloni/sd-xl-custom-model")
     result = client.predict(
-    		custom_model="lichorosario/dott_remastered_style_lora_sdxl",
+    		custom_model=custom_model,
     		api_name="/load_model"
     )
 
     client = Client("fffiloni/sd-xl-custom-model")
     result = client.predict(
-		custom_model="lichorosario/dott_remastered_style_lora_sdxl",
-		weight_name="dott_style.safetensors",
-		prompt=prompt,
-		inf_steps=25,
-		guidance_scale=12,
-		width=1024,
-		height=512,
-		seed=-1,
-		lora_weight=1,
+		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"
     )
 
     return result
 
-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")
+
+
+
+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 = [custom_model, weight_name, prompt_in, inf_steps, guidance_scale, width, height, seed, lora_weight],
+        outputs = [image_out, last_used_seed]
+    )
+
 
 demo.launch()