update app

app.py

@@ -1,100 +1,22 @@
 import os
 import sys
-import random
-import uuid
-import json
 import time
 from threading import Thread
-from typing import Iterable
-from huggingface_hub import snapshot_download
 
 import gradio as gr
 import spaces
 import torch
-import numpy as np
 from PIL import Image
-import cv2
 
 from transformers import (
     Qwen2_5_VLForConditionalGeneration,
     Qwen3VLForConditionalGeneration,
-    AutoModelForImageTextToText,
     AutoModelForCausalLM,
     AutoProcessor,
     TextIteratorStreamer,
 )
 
-from transformers.image_utils import load_image
-from gradio.themes import Soft
-from gradio.themes.utils import colors, fonts, sizes
-
-colors.steel_blue = colors.Color(
-    name="steel_blue",
-    c50="#EBF3F8",
-    c100="#D3E5F0",
-    c200="#A8CCE1",
-    c300="#7DB3D2",
-    c400="#529AC3",
-    c500="#4682B4",
-    c600="#3E72A0",
-    c700="#36638C",
-    c800="#2E5378",
-    c900="#264364",
-    c950="#1E3450",
-)
-
-class SteelBlueTheme(Soft):
-    def __init__(
-        self,
-        *,
-        primary_hue: colors.Color | str = colors.gray,
-        secondary_hue: colors.Color | str = colors.steel_blue,
-        neutral_hue: colors.Color | str = colors.slate,
-        text_size: sizes.Size | str = sizes.text_lg,
-        font: fonts.Font | str | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
-        ),
-        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
-        ),
-    ):
-        super().__init__(
-            primary_hue=primary_hue,
-            secondary_hue=secondary_hue,
-            neutral_hue=neutral_hue,
-            text_size=text_size,
-            font=font,
-            font_mono=font_mono,
-        )
-        super().set(
-            background_fill_primary="*primary_50",
-            background_fill_primary_dark="*primary_900",
-            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
-            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
-            button_primary_text_color="white",
-            button_primary_text_color_hover="white",
-            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
-            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
-            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_800)",
-            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)",
-            button_secondary_text_color="black",
-            button_secondary_text_color_hover="white",
-            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
-            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
-            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
-            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
-            slider_color="*secondary_500",
-            slider_color_dark="*secondary_600",
-            block_title_text_weight="600",
-            block_border_width="3px",
-            block_shadow="*shadow_drop_lg",
-            button_primary_shadow="*shadow_drop_lg",
-            button_large_padding="11px",
-            color_accent_soft="*primary_100",
-            block_label_background_fill="*primary_200",
-        )
-
-steel_blue_theme = SteelBlueTheme()
+from theme import steel_blue_theme
 
 css = """
 #main-title h1 {
@@ -122,43 +44,6 @@ if torch.cuda.is_available():
 
 print("Using device:", device)
 
-# CACHE_PATH = "./model_cache"
-# if not os.path.exists(CACHE_PATH):
-#     os.makedirs(CACHE_PATH)
-#
-# model_path_d_local = snapshot_download(
-#     repo_id='rednote-hilab/dots.ocr',
-#     local_dir=os.path.join(CACHE_PATH, 'dots.ocr'),
-#     max_workers=20,
-#     local_dir_use_symlinks=False
-# )
-#
-# config_file_path = os.path.join(model_path_d_local, "configuration_dots.py")
-#
-# if os.path.exists(config_file_path):
-#     with open(config_file_path, 'r') as f:
-#         input_code = f.read()
-#
-#     lines = input_code.splitlines()
-#     if "class DotsVLProcessor" in input_code and not any("attributes = " in line for line in lines):
-#         output_lines = []
-#         for line in lines:
-#             output_lines.append(line)
-#             if line.strip().startswith("class DotsVLProcessor"):
-#                 output_lines.append("    attributes = [\"image_processor\", \"tokenizer\"]")
-#
-#         with open(config_file_path, 'w') as f:
-#             f.write('\n'.join(output_lines))
-#         print("Patched configuration_dots.py successfully.")
-#
-#sys.path.append(model_path_d_local)
-
-MAX_MAX_NEW_TOKENS = 4096
-DEFAULT_MAX_NEW_TOKENS = 2048
-MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
 # Load Chandra-OCR
 MODEL_ID_V = "datalab-to/chandra"
 processor_v = AutoProcessor.from_pretrained(MODEL_ID_V, trust_remote_code=True)
@@ -197,6 +82,7 @@ model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
     torch_dtype=torch.float16
 ).to(device).eval()
 
+
 @spaces.GPU
 def generate_image(model_name: str, text: str, image: Image.Image,
                    max_new_tokens: int, temperature: float, top_p: float,
@@ -260,6 +146,7 @@ def generate_image(model_name: str, text: str, image: Image.Image,
         time.sleep(0.01)
         yield buffer, buffer
 
+
 image_examples = [
     ["OCR the content perfectly.", "examples/3.jpg"],
     ["Perform OCR on the image.", "examples/1.jpg"],
@@ -280,27 +167,30 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
             )
             
             with gr.Accordion("Advanced options", open=False):
-                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
+                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1,
+                                           value=DEFAULT_MAX_NEW_TOKENS)
                 temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.7)
                 top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
                 top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
-                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.1)
+                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05,
+                                               value=1.1)
                 
         with gr.Column(scale=3):
-                gr.Markdown("## Output", elem_id="output-title")
-                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=11, show_copy_button=True)
-                with gr.Accordion("(Result.md)", open=False):
-                    markdown_output = gr.Markdown(label="(Result.Md)")
-
-                model_choice = gr.Radio(
-                    choices=["Nanonets-OCR2-3B", "Chandra-OCR", "Dots.OCR", "olmOCR-2-7B-1025"],
-                    label="Select Model",
-                    value="Nanonets-OCR2-3B"
-                )
+            gr.Markdown("## Output", elem_id="output-title")
+            output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=11, show_copy_button=True)
+            with gr.Accordion("(Result.md)", open=False):
+                markdown_output = gr.Markdown(label="(Result.Md)")
+
+            model_choice = gr.Radio(
+                choices=["Nanonets-OCR2-3B", "Chandra-OCR", "Dots.OCR", "olmOCR-2-7B-1025"],
+                label="Select Model",
+                value="Nanonets-OCR2-3B"
+            )
 
     image_submit.click(
         fn=generate_image,
-        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k,
+                repetition_penalty],
         outputs=[output, markdown_output]
     )