Create app.py

app.py

@@ -0,0 +1,408 @@
+import os
+import random
+import uuid
+import json
+import time
+from threading import Thread
+from typing import Iterable
+
+import gradio as gr
+import spaces
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+
+from transformers import (
+    Qwen2VLForConditionalGeneration,
+    Qwen2_5_VLForConditionalGeneration,
+    AutoModelForImageTextToText,
+    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 = 1024
+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 = "nanonets/Nanonets-OCR2-3B"
+processor_v = AutoProcessor.from_pretrained(MODEL_ID_V, trust_remote_code=True)
+model_v = Qwen2_5_VLForConditionalGeneration.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 = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct"
+processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
+model_x = Qwen2VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_X,
+    attn_implementation="kernels-community/flash-attn2",
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+MODEL_ID_A = "CohereForAI/aya-vision-8b"
+processor_a = AutoProcessor.from_pretrained(MODEL_ID_A, trust_remote_code=True)
+model_a = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID_A,
+    attn_implementation="kernels-community/flash-attn2",
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+MODEL_ID_W = "allenai/olmOCR-7B-0725"
+processor_w = AutoProcessor.from_pretrained(MODEL_ID_W, trust_remote_code=True)
+model_w = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_W,
+    attn_implementation="kernels-community/flash-attn2",
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+MODEL_ID_M = "reducto/RolmOCR"
+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_duration(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 based on the last argument."""
+    try:
+        return int(gpu_timeout)
+    except:
+        return 60
+
+
+@spaces.GPU(duration=calc_timeout_duration)
+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):
+    """
+    Generates responses using the selected model for image input.
+    Yields raw text and Markdown-formatted text.
+    """
+    if model_name == "RolmOCR-7B":
+        processor = processor_m
+        model = model_m
+    elif model_name == "Qwen2-VL-OCR-2B":
+        processor = processor_x
+        model = model_x
+    elif model_name == "Nanonets-OCR2-3B":
+        processor = processor_v
+        model = model_v
+    elif model_name == "Aya-Vision-8B":
+        processor = processor_a
+        model = model_a
+    elif model_name == "olmOCR-7B-0725":
+        processor = processor_w
+        model = model_w
+    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 = [
+    ["Perform OCR on the image precisely.", "examples/5.jpg"],
+    ["Run OCR on the image and ensure high accuracy.", "examples/4.jpg"],
+    ["Conduct OCR on the image with exact text recognition.", "examples/2.jpg"],
+    ["Perform precise OCR extraction on the image.", "examples/1.jpg"],
+    ["Convert this page to docling", "examples/3.jpg"],
+]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# **Multimodal OCR**", 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=11)
+            with gr.Accordion("(Result.md)", open=False):
+                markdown_output = gr.Markdown(label="(Result.Md)")
+
+            model_choice = gr.Radio(
+                choices=["Nanonets-OCR2-3B", "olmOCR-7B-0725", "RolmOCR-7B",
+                     "Aya-Vision-8B", "Qwen2-VL-OCR-2B"],
+                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"],
+                        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)