app.py

# import subprocess  # 🥲 need for flash attention in QWEN model 

# subprocess.run(
#     "pip install flash-attn --no-build-isolation",
#     env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
#     shell=True,
# )

import spaces
import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor
# from transformers import Qwen2VLForConditionalGeneration  # Uncomment when adding QWEN back
# from qwen_vl_utils import process_vision_info  # Uncomment when adding QWEN back
import torch
import os
import json
from pydantic import BaseModel
from typing import Tuple

os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

# Load Molmo model
model = AutoModelForCausalLM.from_pretrained(
    'allenai/Molmo-7B-D-0924',
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)
processor = AutoProcessor.from_pretrained(
    'allenai/Molmo-7B-D-0924',
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

# # Load Qwen model (commented out for now)
# qwen_model = Qwen2VLForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen2-VL-7B-Instruct",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )
# qwen_processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

class GeneralRetrievalQuery(BaseModel):
    broad_topical_query: str
    broad_topical_explanation: str
    specific_detail_query: str
    specific_detail_explanation: str
    visual_element_query: str
    visual_element_explanation: str

def get_retrieval_prompt(prompt_name: str) -> Tuple[str, GeneralRetrievalQuery]:
    if prompt_name != "general":
        raise ValueError("Only 'general' prompt is available in this version")

    prompt = """You are an AI assistant specialized in document retrieval tasks. Given an image of a document page, your task is to generate retrieval queries that someone might use to find this document in a large corpus.

Please generate 3 different types of retrieval queries:

1. A broad topical query: This should cover the main subject of the document.
2. A specific detail query: This should focus on a particular fact, figure, or point made in the document.
3. A visual element query: This should reference a chart, graph, image, or other visual component in the document, if present. Don't just reference the name of the visual element but generate a query which this illustration may help answer or be related to.

Important guidelines:
- Ensure the queries are relevant for retrieval tasks, not just describing the page content.
- Frame the queries as if someone is searching for this document, not asking questions about its content.
- Make the queries diverse and representative of different search strategies.

For each query, also provide a brief explanation of why this query would be effective in retrieving this document.

Format your response as a JSON object with the following structure:

{
  "broad_topical_query": "Your query here",
  "broad_topical_explanation": "Brief explanation",
  "specific_detail_query": "Your query here",
  "specific_detail_explanation": "Brief explanation",
  "visual_element_query": "Your query here",
  "visual_element_explanation": "Brief explanation"
}

If there are no relevant visual elements, replace the third query with another specific detail query.

Here is the document image to analyze:
<image>

Generate the queries based on this image and provide the response in the specified JSON format."""

    return prompt, GeneralRetrievalQuery

prompt, pydantic_model = get_retrieval_prompt("general")

# def _prep_data_for_input_qwen(image):
#     messages = [
#         {
#             "role": "user",
#             "content": [
#                 {
#                     "type": "image",
#                     "image": image,
#                 },
#                 {"type": "text", "text": prompt},
#             ],
#         }
#     ]
#
#     text = qwen_processor.apply_chat_template(
#         messages, tokenize=False, add_generation_prompt=True
#     )
#
#     image_inputs, video_inputs = process_vision_info(messages)
#
#     return qwen_processor(
#         text=[text],
#         images=image_inputs,
#         videos=video_inputs,
#         padding=True,
#         return_tensors="pt",
#     )

def _prep_data_for_input(image):
    return processor.process(
        images=[image],
        text=prompt
    )

@spaces.GPU
def generate_response(image):
    inputs = _prep_data_for_input(image)
    inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
    output = model.generate_from_batch(
        inputs,
        gr.GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
        tokenizer=processor.tokenizer
    )
    generated_tokens = output[0, inputs['input_ids'].size(1):]
    output_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

    try:
        return json.loads(output_text)
    except Exception:
        gr.Warning("Failed to parse JSON from output")
        return {}

title = "ColPali fine-tuning Query Generator"
description = """[ColPali](https://huggingface.co/papers/2407.01449) is a very exciting new approach to multimodal document retrieval which aims to replace existing document retrievers which often rely on an OCR step with an end-to-end multimodal approach. 

To train or fine-tune a ColPali model, we need a dataset of image-text pairs which represent the document images and the relevant text queries which those documents should match. 
To make the ColPali models work even better we might want a dataset of query/image document pairs related to our domain or task. 

One way in which we might go about generating such a dataset is to use a VLM to generate synthetic queries for us. 
This space uses the [allenai/Molmo-7B-D-0924](https://huggingface.co/allenai/Molmo-7B-D-0924) model to generate queries for a document, based on an input document image. 

**Note** there is a lot of scope for improving to prompts and the quality of the generated queries! If you have any suggestions for improvements please [open a Discussion](https://huggingface.co/spaces/davanstrien/ColPali-Query-Generator/discussions/new)!

This [blog post](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html) gives an overview of how you can use this kind of approach to generate a full dataset for fine-tuning ColPali models. 

If you want to convert a PDF(s) to a dataset of page images you can try out the [ PDFs to Page Images Converter](https://huggingface.co/spaces/Dataset-Creation-Tools/pdf-to-page-images-dataset) Space.
"""

examples = [
    "examples/Approche_no_13_1977.pdf_page_22.jpg",
    "examples/SRCCL_Technical-Summary.pdf_page_7.jpg",
]

demo = gr.Interface(
    fn=generate_response,
    inputs=gr.Image(type="pil"),
    outputs=gr.Json(),
    title=title,
    description=description,
    examples=examples,
)
demo.launch()