Delete app.py

app.py

@@ -1,275 +0,0 @@
-import os
-import sys
-from threading import Thread
-from typing import Iterable
-from huggingface_hub import snapshot_download
-
-import gradio as gr
-import spaces
-import torch
-from PIL import Image
-from transformers import (
-    Qwen2_5_VLForConditionalGeneration,
-    AutoModelForImageTextToText,
-    AutoModelForCausalLM,
-    AutoProcessor,
-    TextIteratorStreamer,
-)
-
-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_700)",
-            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
-            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()
-
-css = """
-#main-title h1 {
-    font-size: 2.3em !important;
-}
-#output-title h2 {
-    font-size: 2.1em !important;
-}
-"""
-
-
-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 = 1440
-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 Nanonets-OCR2-3B
-MODEL_ID_M = "nanonets/Nanonets-OCR2-3B"
-processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_M,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Load Nanonets-OCR2-1.5B-exp
-MODEL_ID_N = "strangervisionhf/excess_layer_pruned-nanonets-1.5b" # -> https://huggingface.co/nanonets/Nanonets-OCR2-1.5B-exp
-processor_n = AutoProcessor.from_pretrained(MODEL_ID_N, trust_remote_code=True)
-model_n = AutoModelForImageTextToText.from_pretrained(
-    MODEL_ID_N,
-    trust_remote_code=True,
-    torch_dtype=torch.float16,
-    attn_implementation="flash_attention_2"
-).to(device).eval()
-
-
-# Load Dots.OCR from the local, patched directory
-MODEL_PATH_D = model_path_d_local
-processor_d = AutoProcessor.from_pretrained(MODEL_PATH_D, trust_remote_code=True)
-model_d = AutoModelForCausalLM.from_pretrained(
-    MODEL_PATH_D,
-    attn_implementation="flash_attention_2",
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    trust_remote_code=True
-).eval()
-
-# Load PaddleOCR
-MODEL_ID_P = "strangervisionhf/paddle" # -> https://huggingface.co/PaddlePaddle/PaddleOCR-VL
-processor_p = AutoProcessor.from_pretrained(MODEL_ID_P, trust_remote_code=True)
-model_p = AutoModelForCausalLM.from_pretrained(
-    MODEL_ID_P,
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16
-).to(device).eval()
-
-@spaces.GPU
-def generate_image(model_name: str, text: str, image: Image.Image,
-                   max_new_tokens: int = 1024,
-                   temperature: float = 0.6,
-                   top_p: float = 0.9,
-                   top_k: int = 50,
-                   repetition_penalty: float = 1.2):
-    """Generate responses for image input using the selected model."""
-    if model_name == "Nanonets-OCR2-3B":
-        processor, model = processor_m, model_m
-    elif model_name == "Nanonets-OCR2-1.5B(exp)":
-        processor, model = processor_n, model_n
-    elif model_name == "Dots.OCR":
-        processor, model = processor_d, model_d
-    elif model_name == "PaddleOCR":
-        processor, model = processor_p, model_p
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
-    if image is None:
-        yield "Please upload an image.", "Please upload an image."
-        return
-
-    images = [image.convert("RGB")]
-
-    if model_name == "PaddleOCR":
-        messages = [
-            {"role": "user", "content": text}
-        ]
-    else:
-        messages = [
-            {
-                "role": "user",
-                "content": [{"type": "image"}] + [{"type": "text", "text": text}]
-            }
-        ]
-        
-    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
-    inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
-
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-    generation_kwargs = {
-        **inputs,
-        "streamer": streamer,
-        "max_new_tokens": max_new_tokens,
-        "temperature": temperature,
-        "top_p": top_p,
-        "top_k": top_k,
-        "repetition_penalty": repetition_penalty,
-        "do_sample": True
-    }
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text.replace("<|im_end|>", "").replace("<end_of_utterance>", "")
-        yield buffer, buffer
-
-image_examples = [
-    ["Perform OCR on the image.", "examples/1.jpg"],
-    ["Phrase the document [page].", "examples/2.jpg"],
-    ["OCR the content perfectly.", "examples/3.jpg"],
-]
-
-with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
-    gr.Markdown("# **Multimodal OCR3**", elem_id="main-title")
-    with gr.Row():
-        with gr.Column(scale=2):
-            image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
-            image_upload = gr.Image(type="pil", label="Upload Image", height=320)
-            image_submit = gr.Button("Submit", variant="primary")
-            gr.Examples(examples=image_examples, inputs=[image_query, image_upload])
-
-            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)
-                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
-                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=1.2)
-
-        with gr.Column(scale=3):
-            gr.Markdown("## Output", elem_id="output-title")
-            raw_output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=11, show_copy_button=True)
-            with gr.Accordion("[Result.md]", open=False):
-                formatted_output = gr.Markdown(label="Formatted Result")
-
-            model_choice = gr.Radio(
-                choices=["Nanonets-OCR2-3B", "Dots.OCR", "Nanonets-OCR2-1.5B(exp)", "PaddleOCR"],
-                label="Select Model",
-                value="Nanonets-OCR2-3B"
-            )
-            gr.Markdown("Note: Currently, PaddleOCR VL only supports OCR inference. Structured OCR document parsing transformer inference is coming soon. [Report – Bug/Issue](https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR3/discussions/1)")
-            
-    image_submit.click(
-        fn=generate_image,
-        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-        outputs=[raw_output, formatted_output]
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=50).launch(mcp_server=True, ssr_mode=False, show_error=True)