Delete app.py

app.py

@@ -1,213 +0,0 @@
-import gradio as gr
-import torch
-import os
-import sys
-from PIL import Image, ImageDraw
-from transformers import AutoModel, AutoProcessor, AutoTokenizer, GenerationConfig
-from huggingface_hub import snapshot_download
-import spaces
-from typing import Optional, Tuple, Dict, Any, Iterable
-from gradio.themes import Soft
-from gradio.themes.utils import colors, fonts, sizes
-
-print("Downloading model snapshot to ensure all scripts are present...")
-model_dir = snapshot_download(repo_id="nvidia/NVIDIA-Nemotron-Parse-v1.1")
-print(f"Model downloaded to: {model_dir}")
-
-sys.path.append(model_dir)
-
-try:
-    from postprocessing import extract_classes_bboxes, transform_bbox_to_original, postprocess_text
-    print("Successfully imported postprocessing functions.")
-except ImportError as e:
-    print(f" Error importing postprocessing: {e}")
-    raise e
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-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; }
-"""
-
-print("Loading Model components...")
-
-processor = AutoProcessor.from_pretrained(model_dir, trust_remote_code=True)
-model = AutoModel.from_pretrained(
-    model_dir,
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
-).to(device).eval()
-
-try:
-    generation_config = GenerationConfig.from_pretrained(model_dir, trust_remote_code=True)
-except Exception as e:
-    print(f"Warning: Could not load GenerationConfig: {e}. Using default.")
-    generation_config = GenerationConfig(max_new_tokens=4096)
-
-print("Model loaded successfully.")
-
-@spaces.GPU
-def process_ocr_task(image):
-    """
-    Processes an image with NVIDIA-Nemotron-Parse-v1.1.
-    """
-    if image is None:
-        return "Please upload an image first.", None
-        
-    task_prompt = "</s><s><predict_bbox><predict_classes><output_markdown>"
-    
-    inputs = processor(images=[image], text=task_prompt, return_tensors="pt").to(device)
-    
-    if device.type == 'cuda':
-        inputs = {k: v.to(torch.bfloat16) if v.dtype == torch.float32 else v for k, v in inputs.items()}
-
-    print("Running inference...")
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs, 
-            generation_config=generation_config
-        )
-
-    generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]
-    
-    try:
-        classes, bboxes, texts = extract_classes_bboxes(generated_text)
-    except Exception as e:
-        print(f"Error extracting boxes: {e}")
-        return generated_text, image
-
-    bboxes = [transform_bbox_to_original(bbox, image.width, image.height) for bbox in bboxes]
-
-    table_format = 'latex'  
-    text_format = 'markdown'
-    blank_text_in_figures = False 
-
-    processed_texts = [
-        postprocess_text(
-            text, 
-            cls=cls, 
-            table_format=table_format, 
-            text_format=text_format, 
-            blank_text_in_figures=blank_text_in_figures
-        ) 
-        for text, cls in zip(texts, classes)
-    ]
-
-    result_image = image.copy()
-    draw = ImageDraw.Draw(result_image)
-    
-    color_map = {
-        "Table": "red",
-        "Figure": "blue",
-        "Text": "green",
-        "Title": "purple"
-    }
-
-    final_output_text = ""
-    
-    for cls, bbox, txt in zip(classes, bboxes, processed_texts):
-        color = color_map.get(cls, "red")
-        draw.rectangle([bbox[0], bbox[1], bbox[2], bbox[3]], outline=color, width=3)
-        
-        if cls == "Table":
-            final_output_text += f"\n\n--- [Table] ---\n{txt}\n-----------------\n"
-        elif cls == "Figure":
-            final_output_text += f"\n\n--- [Figure] ---\n(Figure Detected)\n-----------------\n"
-        else:
-            final_output_text += f"{txt}\n"
-
-    if not final_output_text.strip() and generated_text:
-        final_output_text = generated_text
-
-    return final_output_text, result_image
-
-with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
-    gr.Markdown("# **NVIDIA Nemotron Parse v1.1 [OCR/Parsing]**", elem_id="main-title")
-    gr.Markdown("Upload a document image to extract text, tables, and layout structures using NVIDIA's state-of-the-art Parse model.")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            image_input = gr.Image(type="pil", label="Upload Image", sources=["upload", "clipboard"])
-            submit_btn = gr.Button("Process Document", variant="primary")
-
-            examples = gr.Examples(
-                examples=["examples/1.jpg"],
-                inputs=image_input, 
-                label="Examples"
-            )
-
-        with gr.Column(scale=2):
-            output_text = gr.Textbox(label="Parsed Content (Markdown/LaTeX)", lines=8, show_copy_button=True)
-            output_image = gr.Image(label="Detected Layout & Bounding Boxes", type="pil")
-            
-    submit_btn.click(
-        fn=process_ocr_task, 
-        inputs=[image_input], 
-        outputs=[output_text, output_image]
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch(share=True, mcp_server=True, ssr_mode=False)