Update app.py

app.py

@@ -10,32 +10,65 @@ import asyncio
 from PIL import Image
 from gradio_client import Client, handle_file
 
+
 translator = Translator()
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 basemodel = "black-forest-labs/FLUX.1-schnell"
 MAX_SEED = np.iinfo(np.int32).max
 
+
 CSS = "footer {visibility: hidden;}"
 JS = "function () {gradioURL = window.location.href;if (!gradioURL.endsWith('?__theme=dark')) {window.location.replace(gradioURL + '?__theme=dark');}}"
 
+
 def enable_lora(lora_add):
-    if not lora_add: return basemodel
-    else: return lora_add
+    if not lora_add:
+        return basemodel
+    else:
+        return lora_add
+
 
 def get_upscale_finegrain(prompt, img_path, upscale_factor):
     client = Client("finegrain/finegrain-image-enhancer")
-    result = client.predict(input_image=handle_file(img_path), prompt=prompt, negative_prompt="", seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, tile_width=112, tile_height=144, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process")
+    result = client.predict(
+        input_image=handle_file(img_path),
+        prompt=prompt,
+        negative_prompt="",
+        seed=42,
+        upscale_factor=upscale_factor,
+        controlnet_scale=0.6,
+        controlnet_decay=1,
+        condition_scale=6,
+        tile_width=112,
+        tile_height=144,
+        denoise_strength=0.35,
+        num_inference_steps=18,
+        solver="DDIM",
+        api_name="/process"
+    )
     return result[1]
 
+
 async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
-    if seed == -1: seed = random.randint(0, MAX_SEED)
+    if seed == -1:
+        seed = random.randint(0, MAX_SEED)
     seed = int(seed)
     text = str(translator.translate(prompt, 'English')) + "," + lora_word
-    client = AsyncInferenceClient()
-    try: image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
-    except Exception as e: raise gr.Error(f"Error in {e}")
+    async with AsyncInferenceClient() as client:
+        try:
+            image = await client.text_to_image(
+                prompt=text,
+                height=height,
+                width=width,
+                guidance_scale=scales,
+                num_inference_steps=steps,
+                model=model,
+            )
+        except Exception as e:
+            raise gr.Error(f"Error in {e}")
     return image, seed
 
+
 async def gen(prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor):
     model = enable_lora(lora_add)
     image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
@@ -45,14 +78,19 @@ async def gen(prompt, lora_add, lora_word, width, height, scales, steps, seed, u
         combined_image.paste(image, (0, 0))
         combined_image.paste(upscaled_image, (image.width, 0))
         return combined_image, seed
-    else: return image, seed
+    else:
+        return image, seed
+
 
 with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
     gr.HTML("<h1><center>Flux Lab Light</center></h1>")
     with gr.Row():
         with gr.Column(scale=4):
-            with gr.Row(): img = gr.Image(type="filepath", label='Comparison Image', height=600)
-            with gr.Row(): prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6); sendBtn = gr.Button(scale=1, variant='primary')
+            with gr.Row():
+                img = gr.Image(type="filepath", label='Comparison Image', height=600)
+            with gr.Row():
+                prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6)
+                sendBtn = gr.Button(scale=1, variant='primary')
         with gr.Accordion("Advanced Options", open=True):
             with gr.Column(scale=1):
                 width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=768)
@@ -63,4 +101,19 @@ with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
             lora_add = gr.Textbox(label="Add Flux LoRA", info="Copy the HF LoRA model name here", lines=1, placeholder="Please use Warm status model")
             lora_word = gr.Textbox(label="Add Flux LoRA Trigger Word", info="Add the Trigger Word", lines=1, value="")
             upscale_factor = gr.Radio(label="UpScale Factor", choices=[0, 2, 3, 4], value=0, scale=2)
-    gr.on(triggers=[prompt.submit, sendBtn.click], fn=gen, inputs=[prompt, lora_add, lora_word, width
+    gr.on(
+        triggers=[prompt.submit, sendBtn.click],
+        fn=gen,
+        inputs=[
+            prompt,
+            lora_add,
+            lora_word,
+            width,
+            height,
+            scales,
+            steps,
+            seed,
+            upscale_factor
+        ],
+        outputs=[img, seed]
+    )