Update app.py

app.py

@@ -2,153 +2,206 @@ import gradio as gr
 import numpy as np
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+# # import spaces #[uncomment to use ZeroGPU]
+# from diffusers import DiffusionPipeline
+# import torch
+
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+# model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+
+# if torch.cuda.is_available():
+#     torch_dtype = torch.float16
+# else:
+#     torch_dtype = torch.float32
+
+# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+# pipe = pipe.to(device)
+
+# MAX_SEED = np.iinfo(np.int32).max
+# MAX_IMAGE_SIZE = 1024
+
+
+# # @spaces.GPU #[uncomment to use ZeroGPU]
+# def infer(
+#     prompt,
+#     negative_prompt,
+#     seed,
+#     randomize_seed,
+#     width,
+#     height,
+#     guidance_scale,
+#     num_inference_steps,
+#     progress=gr.Progress(track_tqdm=True),
+# ):
+#     if randomize_seed:
+#         seed = random.randint(0, MAX_SEED)
+
+#     generator = torch.Generator().manual_seed(seed)
+
+#     image = pipe(
+#         prompt=prompt,
+#         negative_prompt=negative_prompt,
+#         guidance_scale=guidance_scale,
+#         num_inference_steps=num_inference_steps,
+#         width=width,
+#         height=height,
+#         generator=generator,
+#     ).images[0]
+
+#     return image, seed
+
+
+# examples = [
+#     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+#     "An astronaut riding a green horse",
+#     "A delicious ceviche cheesecake slice",
+# ]
+
+# css = """
+# #col-container {
+#     margin: 0 auto;
+#     max-width: 640px;
+# }
+# """
+
+# with gr.Blocks(css=css) as demo:
+#     with gr.Column(elem_id="col-container"):
+#         gr.Markdown(" # Text-to-Image Gradio Template")
+
+#         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, variant="primary")
+
+#         result = gr.Image(label="Result", show_label=False)
+
+#         with gr.Accordion("Advanced Settings", open=False):
+#             negative_prompt = gr.Text(
+#                 label="Negative prompt",
+#                 max_lines=1,
+#                 placeholder="Enter a negative prompt",
+#                 visible=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,  # Replace with defaults that work for your model
+#                 )
+
+#                 height = gr.Slider(
+#                     label="Height",
+#                     minimum=256,
+#                     maximum=MAX_IMAGE_SIZE,
+#                     step=32,
+#                     value=1024,  # Replace with defaults that work for your model
+#                 )
+
+#             with gr.Row():
+#                 guidance_scale = gr.Slider(
+#                     label="Guidance scale",
+#                     minimum=0.0,
+#                     maximum=10.0,
+#                     step=0.1,
+#                     value=0.0,  # Replace with defaults that work for your model
+#                 )
+
+#                 num_inference_steps = gr.Slider(
+#                     label="Number of inference steps",
+#                     minimum=1,
+#                     maximum=50,
+#                     step=1,
+#                     value=2,  # Replace with defaults that work for your model
+#                 )
+
+#         gr.Examples(examples=examples, inputs=[prompt])
+#     gr.on(
+#         triggers=[run_button.click, prompt.submit],
+#         fn=infer,
+#         inputs=[
+#             prompt,
+#             negative_prompt,
+#             seed,
+#             randomize_seed,
+#             width,
+#             height,
+#             guidance_scale,
+#             num_inference_steps,
+#         ],
+#         outputs=[result, seed],
+#     )
+
+import requests
+import os
+import gradio as gr
+from huggingface_hub import update_repo_visibility, whoami, upload_folder, create_repo, upload_file, hf_hub_download, update_repo_visibility, file_exists, list_models
+import subprocess
+
+import gradio as gr
+import re
+import uuid
+from typing import Optional
+import json
+import time
+from pathlib import Path
+
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import Repository, HfApi
+api = HfApi()
+
+def slowly_reverse(word, progress=gr.Progress()):
+    progress(0, desc="Starting")
+    time.sleep(1)
+    progress(0.05)
+    new_string = ""
+    for letter in progress.tqdm(word, desc="Reversing"):
+        time.sleep(0.25)
+        new_string = letter + new_string  
+    return new_string
+#demo = gr.Interface(slowly_reverse, gr.Text(), gr.Text())
 
 with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        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, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=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,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+    with gr.Column():
+        input_text = gr.Text("Whatever", interactive = True)
+        output_text = gr.Text("Output")
+    submit_btn = gr.Button("Upload to Hugging Face", interactive=True)
+    upload_progress = gr.Progress(0)
+    output = gr.Markdown(label="Upload Progress")
+
+    def run_test(word, progress):
+        vout = slowly_reverse(word, progress)
+        return None, vout
+    
+    submit_btn.click(
+        fn=upload_civit_to_hf,
+        inputs=[input_text, upload_progress],
+        outputs=[output, output_text]
     )
 
 if __name__ == "__main__":
+    scheduler = BackgroundScheduler()
+    scheduler.add_job(restart_space, 'interval', seconds=3600)
+    scheduler.start()
+    
+    demo.queue(default_concurrency_limit=5)
     demo.launch()
+