Delete app.py

app.py

@@ -1,404 +0,0 @@
-import spaces
-import json
-import math
-import os
-import traceback
-from io import BytesIO
-from typing import Any, Dict, List, Optional, Tuple
-import re
-import time
-from threading import Thread
-from io import BytesIO
-import uuid
-import tempfile
-
-import gradio as gr
-import requests
-import torch
-from PIL import Image
-import fitz
-
-from transformers import (
-    Qwen2_5_VLForConditionalGeneration,
-    Qwen2VLForConditionalGeneration,
-    AutoModelForCausalLM,
-    AutoModelForVision2Seq,
-    AutoModelForImageTextToText,
-    AutoModel,
-    AutoProcessor,
-    TextIteratorStreamer,
-    AutoTokenizer,
-)
-
-from transformers.image_utils import load_image
-
-from reportlab.lib.pagesizes import A4
-from reportlab.lib.styles import getSampleStyleSheet
-from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer
-from reportlab.lib.units import inch
-
-# --- Constants and Model Setup ---
-MAX_INPUT_TOKEN_LENGTH = 4096
-device = torch.device("cuda" 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)
-
-# --- Model Loading ---
-MODEL_ID_M = "LiquidAI/LFM2-VL-450M"
-processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-model_m = AutoModelForImageTextToText.from_pretrained(
-    MODEL_ID_M, trust_remote_code=True, torch_dtype=torch.float16
-).to(device).eval()
-
-MODEL_ID_T = "LiquidAI/LFM2-VL-1.6B"
-processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
-model_t = AutoModelForImageTextToText.from_pretrained(
-    MODEL_ID_T, trust_remote_code=True, torch_dtype=torch.float16
-).to(device).eval()
-
-MODEL_ID_C = "HuggingFaceTB/SmolVLM-Instruct-250M"
-processor_c = AutoProcessor.from_pretrained(MODEL_ID_C, trust_remote_code=True)
-model_c = AutoModelForVision2Seq.from_pretrained(
-    MODEL_ID_C, trust_remote_code=True, torch_dtype=torch.float16, _attn_implementation="flash_attention_2"
-).to(device).eval()
-
-MODEL_ID_G = "echo840/MonkeyOCR-pro-1.2B"
-SUBFOLDER = "Recognition"
-processor_g = AutoProcessor.from_pretrained(
-    MODEL_ID_G, trust_remote_code=True, subfolder=SUBFOLDER
-)
-model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_G, trust_remote_code=True, subfolder=SUBFOLDER, torch_dtype=torch.float16
-).to(device).eval()
-
-MODEL_ID_I = "UCSC-VLAA/VLAA-Thinker-Qwen2VL-2B"
-processor_i = AutoProcessor.from_pretrained(MODEL_ID_I, trust_remote_code=True)
-model_i = Qwen2VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_I, trust_remote_code=True, torch_dtype=torch.float16
-).to(device).eval()
-
-MODEL_ID_A = "nanonets/Nanonets-OCR-s"
-processor_a = AutoProcessor.from_pretrained(MODEL_ID_A, trust_remote_code=True)
-model_a = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_A, trust_remote_code=True, torch_dtype=torch.float16
-).to(device).eval()
-
-MODEL_ID_X = "prithivMLmods/Megalodon-OCR-Sync-0713"
-processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
-model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_X, trust_remote_code=True, torch_dtype=torch.float16
-).to(device).eval()
-
-MODEL_ID_Z = "Vchitect/ShotVL-3B"
-processor_z = AutoProcessor.from_pretrained(MODEL_ID_Z, trust_remote_code=True)
-model_z = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_Z, trust_remote_code=True, torch_dtype=torch.float16
-).to(device).eval()
-
-# --- Moondream2 Model Loading ---
-MODEL_ID_MD = "vikhyatk/moondream2"
-REVISION_MD = "2025-06-21"
-moondream = AutoModelForCausalLM.from_pretrained(
-  MODEL_ID_MD,
-  revision=REVISION_MD,
-  trust_remote_code=True,
-  torch_dtype=torch.float16,
-  device_map="auto",
-)
-tokenizer_md = AutoTokenizer.from_pretrained(MODEL_ID_MD, revision=REVISION_MD)
-
-# --- SmolVLM2 Model Loading ---
-MODEL_ID_S2 = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
-processor_s2 = AutoProcessor.from_pretrained(MODEL_ID_S2, trust_remote_code=True)
-model_s2 = AutoModelForImageTextToText.from_pretrained(
-    MODEL_ID_S2,
-    torch_dtype=torch.float16,
-    trust_remote_code=True,
-    device_map="auto"
-).eval()
-
-
-# --- PDF Generation and Preview Utility Function ---
-def generate_and_preview_pdf(image: Image.Image, text_content: str, font_size: int, line_spacing: float, alignment: str, image_size: str):
-    """
-    Generates a PDF, saves it, and then creates image previews of its pages.
-    Returns the path to the PDF and a list of paths to the preview images.
-    """
-    if image is None or not text_content or not text_content.strip():
-        raise gr.Error("Cannot generate PDF. Image or text content is missing.")
-
-    # --- 1. Generate the PDF ---
-    temp_dir = tempfile.gettempdir()
-    pdf_filename = os.path.join(temp_dir, f"output_{uuid.uuid4()}.pdf")
-    doc = SimpleDocTemplate(
-        pdf_filename,
-        pagesize=A4,
-        rightMargin=inch, leftMargin=inch,
-        topMargin=inch, bottomMargin=inch
-    )
-    styles = getSampleStyleSheet()
-    style_normal = styles["Normal"]
-    style_normal.fontSize = int(font_size)
-    style_normal.leading = int(font_size) * line_spacing
-    style_normal.alignment = {"Left": 0, "Center": 1, "Right": 2, "Justified": 4}[alignment]
-
-    story = []
-
-    img_buffer = BytesIO()
-    image.save(img_buffer, format='PNG')
-    img_buffer.seek(0)
-    
-    page_width, _ = A4
-    available_width = page_width - 2 * inch
-    image_widths = {
-        "Small": available_width * 0.3,
-        "Medium": available_width * 0.6,
-        "Large": available_width * 0.9,
-    }
-    img_width = image_widths[image_size]
-    img = RLImage(img_buffer, width=img_width, height=image.height * (img_width / image.width))
-    story.append(img)
-    story.append(Spacer(1, 12))
-
-    cleaned_text = re.sub(r'#+\s*', '', text_content).replace("*", "")
-    text_paragraphs = cleaned_text.split('\n')
-    
-    for para in text_paragraphs:
-        if para.strip():
-            story.append(Paragraph(para, style_normal))
-
-    doc.build(story)
-
-    # --- 2. Render PDF pages as images for preview ---
-    preview_images = []
-    try:
-        pdf_doc = fitz.open(pdf_filename)
-        for page_num in range(len(pdf_doc)):
-            page = pdf_doc.load_page(page_num)
-            pix = page.get_pixmap(dpi=150)
-            preview_img_path = os.path.join(temp_dir, f"preview_{uuid.uuid4()}_p{page_num}.png")
-            pix.save(preview_img_path)
-            preview_images.append(preview_img_path)
-        pdf_doc.close()
-    except Exception as e:
-        print(f"Error generating PDF preview: {e}")
-        
-    return pdf_filename, preview_images
-
-
-# --- Core Application Logic ---
-@spaces.GPU
-def process_document_stream(
-    model_name: str, 
-    image: Image.Image, 
-    prompt_input: str, 
-    max_new_tokens: int,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-    repetition_penalty: float
-):
-    """
-    Main generator function that handles model inference tasks with advanced generation parameters.
-    """
-    if image is None:
-        yield "Please upload an image.", ""
-        return
-    if not prompt_input or not prompt_input.strip():
-        yield "Please enter a prompt.", ""
-        return
-
-    if model_name == "Moondream2(vision)":
-        image_embeds = moondream.encode_image(image)
-        answer = moondream.answer_question(
-            image_embeds=image_embeds,
-            question=prompt_input,
-            tokenizer=tokenizer_md,
-            max_new_tokens=max_new_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-        )
-        yield answer, answer
-        return
-        
-    elif model_name == "SmolVLM2-2.2B-Instruct(smol)":
-        processor, model = processor_s2, model_s2
-        messages = [{
-            "role": "user",
-            "content": [
-                {"type": "text", "text": prompt_input},
-                {"type": "image"},
-            ]
-        }]
-        inputs = processor.apply_chat_template(
-            messages,
-            images=[image],
-            add_generation_prompt=True,
-            tokenize=True,
-            return_dict=True,
-            return_tensors="pt",
-        ).to(model.device)
-
-        # Convert float32 tensors to float16 if necessary
-        for k, v in inputs.items():
-            if v.dtype == torch.float32:
-                inputs[k] = v.to(torch.float16)
-
-        generated_ids = model.generate(
-            **inputs,
-            do_sample=True,
-            max_new_tokens=max_new_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            repetition_penalty=repetition_penalty,
-        )
-        generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
-        final_output = generated_texts[0].split("Assistant:")[-1].strip()
-        yield final_output, final_output
-        return
-
-    # Assign processor and model for other models
-    if model_name == "LFM2-VL-450M(fast)": processor, model = processor_m, model_m
-    elif model_name == "LFM2-VL-1.6B(fast)": processor, model = processor_t, model_t
-    elif model_name == "ShotVL-3B(cinematic)": processor, model = processor_z, model_z
-    elif model_name == "SmolVLM-Instruct-250M(smol)": processor, model = processor_c, model_c
-    elif model_name == "MonkeyOCR-pro-1.2B(ocr)": processor, model = processor_g, model_g
-    elif model_name == "VLAA-Thinker-Qwen2VL-2B(reason)": processor, model = processor_i, model_i
-    elif model_name == "Nanonets-OCR-s(ocr)": processor, model = processor_a, model_a
-    elif model_name == "Megalodon-OCR-Sync-0713(ocr)": processor, model = processor_x, model_x
-    else:
-        yield "Invalid model selected.", ""
-        return
-
-    # Common streaming logic for the rest of the models
-    messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt_input}]}]
-    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, truncation=True, max_length=MAX_INPUT_TOKEN_LENGTH).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
-        buffer = buffer.replace("<|im_end|>", "")
-        time.sleep(0.01)
-        yield buffer , buffer
-
-    yield buffer, buffer
-
-
-# --- Gradio UI Definition ---
-def create_gradio_interface():
-    """Builds and returns the Gradio web interface."""
-    css = """
-    .main-container { max-width: 1400px; margin: 0 auto; }
-    .process-button { border: none !important; color: white !important; font-weight: bold !important; background-color: blue !important;}
-    .process-button:hover { background-color: darkblue !important; transform: translateY(-2px) !important; box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; }
-    #gallery { min-height: 400px; }
-    """
-    with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo:
-        gr.HTML("""
-        <div class="title" style="text-align: center">
-            <h1>Tiny VLMs Lab🧪</h1>
-            <p style="font-size: 1.1em; color: #6b7280; margin-bottom: 0.6em;">
-                Advanced Vision-Language Model for Image Content and Layout Extraction
-            </p>
-        </div>
-        """)
-
-        with gr.Row():
-            # Left Column (Inputs)
-            with gr.Column(scale=1):
-                model_choice = gr.Dropdown(
-                    choices=["SmolVLM2-2.2B-Instruct(smol)", "LFM2-VL-450M(fast)", "LFM2-VL-1.6B(fast)", "SmolVLM-Instruct-250M(smol)", "Moondream2(vision)", "ShotVL-3B(cinematic)", "Megalodon-OCR-Sync-0713(ocr)", 
-                             "VLAA-Thinker-Qwen2VL-2B(reason)", "MonkeyOCR-pro-1.2B(ocr)", "Nanonets-OCR-s(ocr)"],
-                    label="Select Model", value= "SmolVLM2-2.2B-Instruct(smol)"
-                )
-                prompt_input = gr.Textbox(label="Query Input", placeholder="✦︎ Enter your query", value="Describe the image!")
-                image_input = gr.Image(label="Upload Image", type="pil", sources=['upload'])
-                
-                with gr.Accordion("Advanced Settings", open=False):
-                    max_new_tokens = gr.Slider(minimum=512, maximum=8192, value=4096, step=256, label="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)
-                    
-                    gr.Markdown("### PDF Export Settings")
-                    font_size = gr.Dropdown(choices=["8", "10", "12", "14", "16", "18"], value="12", label="Font Size")
-                    line_spacing = gr.Dropdown(choices=[1.0, 1.15, 1.5, 2.0], value=1.15, label="Line Spacing")
-                    alignment = gr.Dropdown(choices=["Left", "Center", "Right", "Justified"], value="Justified", label="Text Alignment")
-                    image_size = gr.Dropdown(choices=["Small", "Medium", "Large"], value="Medium", label="Image Size in PDF")
-
-                process_btn = gr.Button("🚀 Process Image", variant="primary", elem_classes=["process-button"], size="lg")
-                clear_btn = gr.Button("🗑️ Clear All", variant="secondary")
-
-            # Right Column (Outputs)
-            with gr.Column(scale=2):
-                with gr.Tabs() as tabs:
-                    with gr.Tab("📝 Extracted Content"):
-                        raw_output_stream = gr.Textbox(label="Raw Model Output Stream", interactive=False, lines=15, show_copy_button=True)
-                        with gr.Row():
-                            examples = gr.Examples(
-                                examples=["examples/1.png", "examples/2.png", "examples/3.png", "examples/4.png", "examples/5.png"],
-                                inputs=image_input, label="Examples"
-                            )
-                        gr.Markdown("[Report-Bug💻](https://huggingface.co/spaces/prithivMLmods/OCR-Comparator/discussions)")
-                    
-                    with gr.Tab("📰 README.md"):
-                        with gr.Accordion("(Result.md)", open=True): 
-                            markdown_output = gr.Markdown()
-
-                    with gr.Tab("📋 PDF Preview"):
-                        generate_pdf_btn = gr.Button("📄 Generate PDF & Render", variant="primary")
-                        pdf_output_file = gr.File(label="Download Generated PDF", interactive=False)
-                        pdf_preview_gallery = gr.Gallery(label="PDF Page Preview", show_label=True, elem_id="gallery", columns=2, object_fit="contain", height="auto")
-
-        # Event Handlers
-        def clear_all_outputs():
-            return None, "", "Raw output will appear here.", "", None, None
-
-        process_btn.click(
-            fn=process_document_stream,
-            inputs=[model_choice, image_input, prompt_input, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-            outputs=[raw_output_stream, markdown_output]
-        )
-        
-        generate_pdf_btn.click(
-            fn=generate_and_preview_pdf,
-            inputs=[image_input, raw_output_stream, font_size, line_spacing, alignment, image_size],
-            outputs=[pdf_output_file, pdf_preview_gallery]
-        )
-
-        clear_btn.click(
-            clear_all_outputs,
-            outputs=[image_input, prompt_input, raw_output_stream, markdown_output, pdf_output_file, pdf_preview_gallery]
-        )
-    return demo
-
-if __name__ == "__main__":
-    demo = create_gradio_interface()
-    demo.queue(max_size=50).launch(share=True, ssr_mode=False, show_error=True)