Update app.py

app.py

@@ -1,8 +1,10 @@
+import random
+
 import gradio as gr
 import numpy as np
-import random
 import spaces
 import torch
+from dataset_viber import CollectorInterface
 from diffusers import DiffusionPipeline
 
 dtype = torch.bfloat16
@@ -19,104 +21,53 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_in
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
-            prompt = prompt, 
-            width = width,
-            height = height,
-            num_inference_steps = num_inference_steps, 
-            generator = generator,
+            prompt=prompt,
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
             guidance_scale=0.0
-    ).images[0] 
-    return image, seed
- 
+    ).images[0]
+    return image
+
 examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
+    ["a tiny astronaut hatching from an egg on the moon", 0, True, 1024, 1024, 4],
+    ["a cat holding a sign that says hello world", 0, True, 1024, 1024, 4],
+    ["an anime illustration of a wiener schnitzel", 0, True, 1024, 1024, 4],
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
 }
 """
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [schnell]
+description = """# FLUX.1 [schnell]
 12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
 [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-            
-            with gr.Row():
-                
-  
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=4,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
-        )
+"""
 
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
-        outputs = [result, seed]
-    )
+interface = CollectorInterface(
+    fn=infer,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter your prompt")
+    ],
+    outputs=[
+        gr.Image(label="Result"),
+    ],
+    additional_inputs=[
+        gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0),
+        gr.Checkbox(label="Randomize seed", value=True),
+        gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024),
+        gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024),
+        gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=4),
+    ],
+    title="FLUX.1 [schnell] - with Dataset Viber data collection",
+    description=description,
+    examples=examples,
+    css=css,
+    dataset_name="image-generation-flux1-schnell"
+)
 
-demo.launch()
+interface.launch()