Update app.py

app.py

@@ -1,185 +1,12 @@
 #!/usr/bin/env python
-from __future__ import annotations
+import gradio
 
-import os
-import random
-import time
+def my_inference_function(name):
+  return "Hello " + name + "!"
 
-import gradio as gr
-import numpy as np
-import PIL.Image
-
-from huggingface_hub import snapshot_download
-from diffusers import DiffusionPipeline
-
-from lcm_scheduler import LCMScheduler
-from lcm_ov_pipeline import OVLatentConsistencyModelPipeline
-
-from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel
-
-import os
-from tqdm import tqdm
-
-from concurrent.futures import ThreadPoolExecutor
-import uuid
-
-DESCRIPTION = '''# Latent Consistency Model OpenVino CPU
-Based on [Latency Consistency Model](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model) HF space
-
-<p>Running on CPU 🥶.</p>
-'''
-
-MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1"
-
-model_id = "Kano001/Dreamshaper_v7-Openvino"
-batch_size = 1
-width = int(os.getenv("IMAGE_WIDTH", "512"))
-height = int(os.getenv("IMAGE_HEIGHT", "512"))
-num_images = int(os.getenv("NUM_IMAGES", "1"))
-
-class CustomOVModelVaeDecoder(OVModelVaeDecoder):
-    def __init__(
-        self, model: openvino.runtime.Model, parent_model: OVBaseModel, ov_config: Optional[Dict[str, str]] = None, model_dir: str = None,
-    ):
-        super(OVModelVaeDecoder, self).__init__(model, parent_model, ov_config, "vae_decoder", model_dir)
-
-scheduler = LCMScheduler.from_pretrained(model_id, subfolder="scheduler")
-pipe = OVLatentConsistencyModelPipeline.from_pretrained(model_id, scheduler = scheduler, compile = False, ov_config = {"CACHE_DIR":""})
-
-# Inject TAESD
-
-taesd_dir = snapshot_download(repo_id="Kano001/taesd-openvino")
-pipe.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"), parent_model = pipe, model_dir = taesd_dir)
-
-pipe.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
-pipe.compile()
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-def save_image(img, profile: gr.OAuthProfile | None, metadata: dict):
-    unique_name = str(uuid.uuid4()) + '.png'
-    img.save(unique_name)
-    return unique_name
-
-def save_images(image_array, profile: gr.OAuthProfile | None, metadata: dict):
-    paths = []
-    with ThreadPoolExecutor() as executor:
-        paths = list(executor.map(save_image, image_array, [profile]*len(image_array), [metadata]*len(image_array)))
-    return paths
-
-def generate(
-    prompt: str,
-    seed: int = 0,
-    guidance_scale: float = 8.0,
-    num_inference_steps: int = 4,
-    randomize_seed: bool = False,
-    progress = gr.Progress(track_tqdm=True),
-    profile: gr.OAuthProfile | None = None,
-) -> PIL.Image.Image:
-    global batch_size
-    global width
-    global height
-    global num_images
-
-    seed = randomize_seed_fn(seed, randomize_seed)
-    np.random.seed(seed)
-    start_time = time.time()
-    result = pipe(
-        prompt=prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=num_images,
-        output_type="pil",
-    ).images
-    paths = save_images(result, profile, metadata={"prompt": prompt, "seed": seed, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
-    print(time.time() - start_time)
-    return paths, seed
-
-examples = [
-    "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
-    "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
-]
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-    with gr.Group():
-        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.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery", grid=[2]
-        )
-    with gr.Accordion("Advanced options", open=False):
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-            randomize=True
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed across runs", value=True)
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance scale for base",
-                minimum=2,
-                maximum=14,
-                step=0.1,
-                value=8.0,
-            )
-            num_inference_steps = gr.Slider(
-                label="Number of inference steps for base",
-                minimum=1,
-                maximum=8,
-                step=1,
-                value=4,
-            )
-    
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=result,
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
-
-    gr.on(
-        triggers=[
-            prompt.submit,
-            run_button.click,
-        ],
-        fn=generate,
-        inputs=[
-            prompt,
-            seed,
-            guidance_scale,
-            num_inference_steps,
-            randomize_seed
-        ],
-        outputs=[result, seed],
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue(api_open=False)
-    # demo.queue(max_size=20).launch()
-    demo.launch()
+gradio_interface = gradio.Interface(
+  fn = my_inference_function,
+  inputs = "text",
+  outputs = "text"
+)
+gradio_interface.launch()