File size: 3,315 Bytes
f9c5a74 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
import spaces
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 subprocess
# Install flash-attn if not already installed
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
# Load the RAG Model and the Qwen2-VL-2B-Instruct model
RAG = RAGMultiModalModel.from_pretrained("vidore/colpali")
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)
@spaces.GPU()
def process_pdf_and_query(pdf_file, user_query):
# Convert the PDF to images
images = convert_from_path(pdf_file.name) # pdf_file.name gives the file path
num_images = len(images)
# Indexing the PDF in RAG
RAG.index(
input_path=pdf_file.name,
index_name="image_index", # index will be saved at index_root/index_name/
store_collection_with_index=False,
overwrite=True
)
# Search the query in the RAG model
results = RAG.search(user_query, k=1)
if not results:
return "No results found.", num_images
# Retrieve the page number and process image
image_index = results[0]["page_num"] - 1
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": images[image_index],
},
{"type": "text", "text": user_query},
],
}
]
# Generate text with the Qwen model
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")
# Generate the output response
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
# Define the Gradio Interface
pdf_input = gr.File(label="Upload PDF") # Single PDF file input
query_input = gr.Textbox(label="Enter your query", placeholder="Ask a question about the PDF") # User query input
output_text = gr.Textbox(label="Model Answer") # Output for the model's answer
output_images = gr.Textbox(label="Number of Images in PDF") # Output for number of images
# Launch the Gradio app
demo = gr.Interface(
fn=process_pdf_and_query,
inputs=[pdf_input, query_input], # List of inputs
outputs=[output_text, output_images], # List of outputs
title="Multimodal RAG with Image Query - By Pejman Ebrahimi"
)
demo.launch(debug=True) # Start the interface
|