Delete app.py

app.py

@@ -1,406 +0,0 @@
-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()
-
-css = """
-#main-title h1 {
-    font-size: 2.3em !important;
-}
-#output-title h2 {
-    font-size: 2.2em !important;
-}
-
-/* RadioAnimated Styles */
-.ra-wrap{ width: fit-content; }
-.ra-inner{
-  position: relative; display: inline-flex; align-items: center; gap: 0; padding: 6px;
-  background: var(--neutral-200); border-radius: 9999px; overflow: hidden;
-}
-.ra-input{ display: none; }
-.ra-label{
-  position: relative; z-index: 2; padding: 8px 16px;
-  font-family: inherit; font-size: 14px; font-weight: 600;
-  color: var(--neutral-500); cursor: pointer; transition: color 0.2s; white-space: nowrap;
-}
-.ra-highlight{
-  position: absolute; z-index: 1; top: 6px; left: 6px;
-  height: calc(100% - 12px); border-radius: 9999px;
-  background: white; box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-  transition: transform 0.2s, width 0.2s;
-}
-.ra-input:checked + .ra-label{ color: black; }
-
-/* Dark mode adjustments for Radio */
-.dark .ra-inner { background: var(--neutral-800); }
-.dark .ra-label { color: var(--neutral-400); }
-.dark .ra-highlight { background: var(--neutral-600); }
-.dark .ra-input:checked + .ra-label { color: white; }
-
-#gpu-duration-container {
-    padding: 10px;
-    border-radius: 8px;
-    background: var(--background-fill-secondary);
-    border: 1px solid var(--border-color-primary);
-    margin-top: 10px;
-}
-"""
-
-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")
-
-print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
-print("torch.__version__ =", torch.__version__)
-print("torch.version.cuda =", torch.version.cuda)
-print("cuda available:", torch.cuda.is_available())
-print("cuda device count:", torch.cuda.device_count())
-if torch.cuda.is_available():
-    print("current device:", torch.cuda.current_device())
-    print("device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
-
-print("Using device:", device)
-
-class RadioAnimated(gr.HTML):
-    def __init__(self, choices, value=None, **kwargs):
-        if not choices or len(choices) < 2:
-            raise ValueError("RadioAnimated requires at least 2 choices.")
-        if value is None:
-            value = choices[0]
-
-        uid = uuid.uuid4().hex[:8]
-        group_name = f"ra-{uid}"
-
-        inputs_html = "\n".join(
-            f"""
-            <input class="ra-input" type="radio" name="{group_name}" id="{group_name}-{i}" value="{c}">
-            <label class="ra-label" for="{group_name}-{i}">{c}</label>
-            """
-            for i, c in enumerate(choices)
-        )
-
-        html_template = f"""
-        <div class="ra-wrap" data-ra="{uid}">
-          <div class="ra-inner">
-            <div class="ra-highlight"></div>
-            {inputs_html}
-          </div>
-        </div>
-        """
-
-        js_on_load = r"""
-        (() => {
-          const wrap = element.querySelector('.ra-wrap');
-          const inner = element.querySelector('.ra-inner');
-          const highlight = element.querySelector('.ra-highlight');
-          const inputs = Array.from(element.querySelectorAll('.ra-input'));
-
-          if (!inputs.length) return;
-
-          const choices = inputs.map(i => i.value);
-
-          function setHighlightByIndex(idx) {
-            const n = choices.length;
-            const pct = 100 / n;
-            highlight.style.width = `calc(${pct}% - 6px)`;
-            highlight.style.transform = `translateX(${idx * 100}%)`;
-          }
-
-          function setCheckedByValue(val, shouldTrigger=false) {
-            const idx = Math.max(0, choices.indexOf(val));
-            inputs.forEach((inp, i) => { inp.checked = (i === idx); });
-            setHighlightByIndex(idx);
-
-            props.value = choices[idx];
-            if (shouldTrigger) trigger('change', props.value);
-          }
-
-          setCheckedByValue(props.value ?? choices[0], false);
-
-          inputs.forEach((inp) => {
-            inp.addEventListener('change', () => {
-              setCheckedByValue(inp.value, true);
-            });
-          });
-        })();
-        """
-
-        super().__init__(
-            value=value,
-            html_template=html_template,
-            js_on_load=js_on_load,
-            **kwargs
-        )
-
-def apply_gpu_duration(val: str):
-    return int(val)
-
-MODEL_ID_V = "datalab-to/chandra"
-processor_v = AutoProcessor.from_pretrained(MODEL_ID_V, trust_remote_code=True)
-model_v = Qwen3VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_V,
-    #attn_implementation="kernels-community/flash-attn2",
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-MODEL_ID_X = "nanonets/Nanonets-OCR2-3B"
-processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
-model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_X,
-    #attn_implementation="kernels-community/flash-attn2",
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16,
-).to(device).eval()
-
-MODEL_PATH_D = "prithivMLmods/Dots.OCR-1.5-Transformers.v5.0" # -> alt of [prithivMLmods/Dots.OCR-Latest-BF16] / [rednote-hilab/dots.ocr]
-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()
-
-MODEL_ID_M = "allenai/olmOCR-2-7B-1025"
-processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_M,
-    #attn_implementation="kernels-community/flash-attn2",
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-def calc_timeout_image(model_name: str, text: str, image: Image.Image,
-                       max_new_tokens: int, temperature: float, top_p: float,
-                       top_k: int, repetition_penalty: float, gpu_timeout: int):
-    """Calculate GPU timeout duration for image inference."""
-    try:
-        return int(gpu_timeout)
-    except:
-        return 60
-
-@spaces.GPU(duration=calc_timeout_image)
-def generate_image(model_name: str, text: str, image: Image.Image,
-                   max_new_tokens: int, temperature: float, top_p: float,
-                   top_k: int, repetition_penalty: float, gpu_timeout: int = 60):
-    """
-    Generates responses using the selected model for image input.
-    Yields raw text and Markdown-formatted text.
-    """
-    if model_name == "olmOCR-2-7B-1025":
-        processor = processor_m
-        model = model_m
-    elif model_name == "Nanonets-OCR2-3B":
-        processor = processor_x
-        model = model_x
-    elif model_name == "Chandra-OCR":
-        processor = processor_v
-        model = model_v
-    elif model_name == "Dots.OCR":
-        processor = processor_d
-        model = model_d
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
-    if image is None:
-        yield "Please upload an image.", "Please upload an image."
-        return
-
-    messages = [{
-        "role": "user",
-        "content": [
-            {"type": "image"},
-            {"type": "text", "text": text},
-        ]
-    }]
-    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-
-    inputs = processor(
-        text=[prompt_full],
-        images=[image],
-        return_tensors="pt",
-        padding=True).to(device)
-
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-    generation_kwargs = {
-        **inputs,
-        "streamer": streamer,
-        "max_new_tokens": max_new_tokens,
-        "do_sample": True,
-        "temperature": temperature,
-        "top_p": top_p,
-        "top_k": top_k,
-        "repetition_penalty": repetition_penalty,
-    }
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        buffer = buffer.replace("<|im_end|>", "")
-        time.sleep(0.01)
-        yield buffer, buffer
-
-image_examples = [
-    ["Convert to Markdown.", "examples/3.jpg"],
-    ["Perform OCR on the image. [Markdown]", "examples/1.jpg"],
-    ["Extract the contents. [Markdown].", "examples/2.jpg"],
-]
-
-with gr.Blocks() 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=290)
-
-            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=0.7)
-                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)
-                
-        with gr.Column(scale=3):
-            gr.Markdown("## Output", elem_id="output-title")
-            output = gr.Textbox(label="Raw Output Stream", interactive=True, lines=15)
-            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"
-            )
-            
-            with gr.Row(elem_id="gpu-duration-container"):
-                with gr.Column():
-                    gr.Markdown("**GPU Duration (seconds)**")
-                    radioanimated_gpu_duration = RadioAnimated(
-                        choices=["60", "90", "120", "180", "240", "300"],
-                        value="60",
-                        elem_id="radioanimated_gpu_duration"
-                    )
-                    gpu_duration_state = gr.Number(value=60, visible=False)
-            
-            gr.Markdown("*Note: Higher GPU duration allows for longer processing but consumes more GPU quota.*")
-            
-    radioanimated_gpu_duration.change(
-        fn=apply_gpu_duration,
-        inputs=radioanimated_gpu_duration,
-        outputs=[gpu_duration_state],
-        api_visibility="private"
-    )
-
-    image_submit.click(
-        fn=generate_image,
-        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty, gpu_duration_state],
-        outputs=[output, markdown_output]
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=50).launch(css=css, theme=steel_blue_theme, mcp_server=True, ssr_mode=False, show_error=True)