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import spaces | |
import os | |
import gradio as gr | |
from pdf2image import convert_from_path | |
from byaldi import RAGMultiModalModel | |
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor | |
from qwen_vl_utils import process_vision_info | |
import torch | |
import torchvision | |
import subprocess | |
def install_poppler(): | |
try: | |
subprocess.run(["pdfinfo"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
except FileNotFoundError: | |
print("Poppler not found. Installing...") | |
subprocess.run("apt-get update", shell=True) | |
subprocess.run("apt-get install -y poppler-utils", shell=True) | |
install_poppler() | |
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) | |
RAG = RAGMultiModalModel.from_pretrained("vidore/colpali-v1.2") | |
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", | |
trust_remote_code=True, torch_dtype=torch.bfloat16).cuda().eval() | |
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True) | |
def process_pdf_and_query(pdf_file, user_query): | |
images = convert_from_path(pdf_file.name) | |
num_images = len(images) | |
RAG.index( | |
input_path=pdf_file.name, | |
index_name="image_index", | |
store_collection_with_index=False, | |
overwrite=True | |
) | |
results = RAG.search(user_query, k=1) | |
if not results: | |
return "No results found.", num_images | |
image_index = results[0]["page_num"] - 1 | |
messages = [ | |
{ | |
"role": "user", | |
"content": [ | |
{ | |
"type": "image", | |
"image": images[image_index], | |
}, | |
{"type": "text", "text": user_query}, | |
], | |
} | |
] | |
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
image_inputs, video_inputs = process_vision_info(messages) | |
inputs = processor( | |
text=[text], | |
images=image_inputs, | |
videos=video_inputs, | |
padding=True, | |
return_tensors="pt", | |
) | |
inputs = inputs.to("cuda") | |
generated_ids = model.generate(**inputs, max_new_tokens=50) | |
generated_ids_trimmed = [ | |
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | |
] | |
output_text = processor.batch_decode( | |
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False | |
) | |
return output_text[0], num_images | |
css = """ | |
body { | |
font-family: Arial, sans-serif; | |
background-color: #2b2b2b; | |
color: #e0e0e0; | |
} | |
.container { | |
max-width: 800px; | |
margin: 0 auto; | |
padding: 20px; | |
background-color: #363636; | |
border-radius: 10px; | |
box-shadow: 0 0 10px rgba(0,0,0,0.3); | |
} | |
.title { | |
font-size: 24px; | |
font-weight: bold; | |
text-align: center; | |
margin-bottom: 20px; | |
color: #50fa7b; | |
} | |
.submit-btn { | |
background-color: #50fa7b; | |
color: #282a36; | |
padding: 10px 20px; | |
border: none; | |
border-radius: 5px; | |
cursor: pointer; | |
font-size: 16px; | |
font-weight: bold; | |
} | |
.submit-btn:hover { | |
background-color: #45c967; | |
} | |
.duplicate-button { | |
background-color: #8be9fd; | |
color: #282a36; | |
padding: 10px 20px; | |
border: none; | |
border-radius: 5px; | |
cursor: pointer; | |
font-size: 16px; | |
font-weight: bold; | |
margin-top: 20px; | |
} | |
.duplicate-button:hover { | |
background-color: #79c7d8; | |
} | |
a { | |
color: #8be9fd; | |
text-decoration: none; | |
} | |
a:hover { | |
text-decoration: underline; | |
} | |
""" | |
explanation = """ | |
<div style="background-color: #44475a; padding: 15px; border-radius: 5px; margin-bottom: 20px; color: #f8f8f2;"> | |
<h3 style="color: #50fa7b;">About Multimodal RAG</h3> | |
<p>Multimodal RAG (Retrieval-Augmented Generation) combines text and image processing to provide more context-aware responses. This demo uses:</p> | |
<ul> | |
<li><strong style="color: #ffb86c;">ColPali</strong>: A multimodal retriever for efficient information retrieval from images and text.</li> | |
<li><strong style="color: #ffb86c;">Byaldi</strong>: A new library by answer.ai that simplifies the use of ColPali.</li> | |
<li><strong style="color: #ffb86c;">Qwen/Qwen2-VL-2B-Instruct</strong>: A large language model capable of processing both text and visual inputs.</li> | |
</ul> | |
<p>This combination allows for more accurate and context-aware responses to queries about uploaded PDFs.</p> | |
</div> | |
""" | |
footer = """ | |
<div style="text-align: center; margin-top: 20px; color: #f8f8f2;"> | |
<a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> | | |
<a href="https://github.com/arad1367" target="_blank">GitHub</a> | | |
<a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a> | | |
<a href="https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct" target="_blank">Qwen/Qwen2-VL-2B-Instruct</a> | | |
<a href="https://github.com/AnswerDotAI/byaldi" target="_blank">Byaldi</a> | | |
<a href="https://github.com/illuin-tech/colpali" target="_blank">ColPali</a> | |
<br> | |
Made with π by Pejman Ebrahimi | |
</div> | |
""" | |
with gr.Blocks(css=css, theme='freddyaboulton/dracula_revamped') as demo: | |
gr.HTML('<h1 style="text-align: center; font-size: 32px;"><a href="https://github.com/arad1367" target="_blank" style="text-decoration: none; color: #50fa7b;">Multimodal RAG with Image Query - By Pejman Ebrahimi (Please Like the Space)</a></h1>') | |
gr.HTML(explanation) | |
pdf_input = gr.File(label="Upload PDF") | |
query_input = gr.Textbox(label="Enter your query", placeholder="Ask a question about the PDF") | |
submit_btn = gr.Button("Submit", elem_classes="submit-btn") | |
output_text = gr.Textbox(label="Model Answer") | |
output_images = gr.Textbox(label="Number of Images in PDF") | |
submit_btn.click(process_pdf_and_query, inputs=[pdf_input, query_input], outputs=[output_text, output_images]) | |
gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") | |
gr.HTML(footer) | |
demo.launch(debug=True) |