From b5d26c4f7cc532cb725fc96b529b61e1b8811a46 Mon Sep 17 00:00:00 2001 From: Cameron Pfiffer Date: Fri, 8 Nov 2024 12:37:17 -0800 Subject: [PATCH] Add a receipt processing cookbook (#1249) I added a receipt processing cookbook. - Uses Qwen or Pixtral - General purpose message templating, no messy model-specific token adding - Easy function for compressing images down for lower processing/memory requirements Should help illustrate a simple use case for vision models. --- docs/cookbook/images/trader-joes-receipt.jpg | Bin 0 -> 430638 bytes docs/cookbook/index.md | 1 + docs/cookbook/receipt-digitization.md | 296 +++++++++++++++++++ mkdocs.yml | 1 + 4 files changed, 298 insertions(+) create mode 100644 docs/cookbook/images/trader-joes-receipt.jpg create mode 100644 docs/cookbook/receipt-digitization.md diff --git a/docs/cookbook/images/trader-joes-receipt.jpg b/docs/cookbook/images/trader-joes-receipt.jpg new file mode 100644 index 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zLU!$w$sK7)+3pgdArXT7sY*vjsa+M(d^AqQ71>?r_8^GCf})gdm5s3lX(n#QF|i)R zIU9Si%z{;Y@%Qk0}>R#sA!rT7m*x+|iFCt8 z^H2uh7x|00J8a z)yiyJctV^C`?-DHi?d?#61Y4^fe|fX8!JTAnj%}YLv$Z)JWP(oa2xnXl+Te(+t84N zAhZ(ldlfdxdSbEYPYqdIvF!9fmz1=;20CP2X!=JxX)i>h@+X#Ov&2LnVq*E-Dj^L> UC!$H{&tzX(PZ4?|^k1X@*;Pv=jsO4v literal 0 HcmV?d00001 diff --git a/docs/cookbook/index.md b/docs/cookbook/index.md index 6d279bff5..c36b98969 100644 --- a/docs/cookbook/index.md +++ b/docs/cookbook/index.md @@ -14,4 +14,5 @@ This part of the documentation provides a few cookbooks that you can browse to g - [ReAct Agent](react_agent.md): Build an agent with open weights models using regex-structured generation. - [Earnings reports to CSV](earnings-reports.md): Extract data from earnings reports to CSV using regex-structured generation. - [Vision-Language Models](atomic_caption.md): Use Outlines with vision-language models for tasks like image captioning and visual reasoning. +- [Receipt Digitization](receipt-digitization.md): Extract information from a picture of a receipt using structured generation. - [Structured Generation from PDFs](read-pdfs.md): Use Outlines with vision-language models to read PDFs and produce structured output. diff --git a/docs/cookbook/receipt-digitization.md b/docs/cookbook/receipt-digitization.md new file mode 100644 index 000000000..67830fa81 --- /dev/null +++ b/docs/cookbook/receipt-digitization.md @@ -0,0 +1,296 @@ +# Receipt Data Extraction with VLMs + +## Setup + +You'll need to install the dependencies: + +```bash +pip install outlines torch==2.4.0 transformers accelerate pillow rich +``` + +## Import libraries + +Load all the necessary libraries: + +```python +# LLM stuff +import outlines +import torch +from transformers import AutoProcessor +from pydantic import BaseModel, Field +from typing import Literal, Optional, List + +# Image stuff +from PIL import Image +import requests + +# Rich for pretty printing +from rich import print +``` + +## Choose a model + +This example has been tested with `mistral-community/pixtral-12b` ([HF link](https://huggingface.co/mistral-community/pixtral-12b)) and `Qwen/Qwen2-VL-7B-Instruct` ([HF link](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)). + +We recommend Qwen-2-VL as we have found it to be more accurate than Pixtral. + +If you want to use Qwen-2-VL, you can do the following: + +```python +# To use Qwen-2-VL: +from transformers import Qwen2VLForConditionalGeneration +model_name = "Qwen/Qwen2-VL-7B-Instruct" +model_class = Qwen2VLForConditionalGeneration +``` + +If you want to use Pixtral, you can do the following: + +```python +# To use Pixtral: +from transformers import LlavaForConditionalGeneration +model_name="mistral-community/pixtral-12b" +model_class=LlavaForConditionalGeneration +``` + +## Load the model + +Load the model into memory: + +```python +model = outlines.models.transformers_vision( + model_name, + model_class=model_class, + model_kwargs={ + "device_map": "auto", + "torch_dtype": torch.bfloat16, + }, + processor_kwargs={ + "device": "cuda", # set to "cpu" if you don't have a GPU + }, +) +``` + +## Image processing + +Images can be quite large. In GPU-poor environments, you may need to resize the image to a smaller size. + +Here's a helper function to do that: + +```python +def load_and_resize_image(image_path, max_size=1024): + """ + Load and resize an image while maintaining aspect ratio + + Args: + image_path: Path to the image file + max_size: Maximum dimension (width or height) of the output image + + Returns: + PIL Image: Resized image + """ + image = Image.open(image_path) + + # Get current dimensions + width, height = image.size + + # Calculate scaling factor + scale = min(max_size / width, max_size / height) + + # Only resize if image is larger than max_size + if scale < 1: + new_width = int(width * scale) + new_height = int(height * scale) + image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) + + return image +``` + +You can change the resolution of the image by changing the `max_size` argument. Small max sizes will make the image more blurry, but processing will be faster and require less memory. + +## Load an image + +Load an image and resize it. We've provided a sample image of a Trader Joe's receipt, but you can use any image you'd like. + +Here's what the image looks like: + +![Trader Joe's receipt](./images/trader-joes-receipt.jpg) + +```python +# Path to the image +image_path = "https://dottxt-ai.github.io/outlines/main/cookbook/images/trader-joes-receipt.png" + +# Download the image +response = requests.get(image_path) +with open("receipt.png", "wb") as f: + f.write(response.content) + +# Load + resize the image +image = load_and_resize_image("receipt.png") +``` + +## Define the output structure + +We'll define a Pydantic model to describe the data we want to extract from the image. + +In our case, we want to extract the following information: + +- The store name +- The store address +- The store number +- A list of items, including the name, quantity, price per unit, and total price +- The tax +- The total +- The date +- The payment method + +Most fields are optional, as not all receipts contain all information. + +```python +class Item(BaseModel): + name: str + quantity: Optional[int] + price_per_unit: Optional[float] + total_price: Optional[float] + +class ReceiptSummary(BaseModel): + store_name: str + store_address: str + store_number: Optional[int] + items: List[Item] + tax: Optional[float] + total: Optional[float] + # Date is in the format YYYY-MM-DD. We can apply a regex pattern to ensure it's formatted correctly. + date: Optional[str] = Field(pattern=r'\d{4}-\d{2}-\d{2}', description="Date in the format YYYY-MM-DD") + payment_method: Literal["cash", "credit", "debit", "check", "other"] +``` + +## Prepare the prompt + +We'll use the `AutoProcessor` to convert the image and the text prompt into a format that the model can understand. Practically, +this is the code that adds user, system, assistant, and image tokens to the prompt. + +```python +# Set up the content you want to send to the model +messages = [ + { + "role": "user", + "content": [ + { + # The image is provided as a PIL Image object + "type": "image", + "image": image, + }, + { + "type": "text", + "text": f"""You are an expert at extracting information from receipts. + Please extract the information from the receipt. Be as detailed as possible -- + missing or misreporting information is a crime. + + Return the information in the following JSON schema: + {ReceiptSummary.model_json_schema()} + """}, + ], + } +] + +# Convert the messages to the final prompt +processor = AutoProcessor.from_pretrained(model_name) +prompt = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True +) +``` + +If you are curious, the final prompt that is sent to the model looks (roughly) like this: + +``` +<|im_start|>system +You are a helpful assistant.<|im_end|> +<|im_start|>user +<|vision_start|><|image_pad|><|vision_end|> +You are an expert at extracting information from receipts. +Please extract the information from the receipt. Be as detailed as +possible -- missing or misreporting information is a crime. + +Return the information in the following JSON schema: + + +<|im_end|> +<|im_start|>assistant +``` + +## Run the model + +```python +# Prepare a function to process receipts +receipt_summary_generator = outlines.generate.json( + model, + ReceiptSummary, + + # Greedy sampling is a good idea for numeric + # data extraction -- no randomness. + sampler=outlines.samplers.greedy() +) + +# Generate the receipt summary +result = receipt_summary_generator(prompt, [image]) +print(result) +``` + +## Output + +The output should look like this: + +``` +ReceiptSummary( + store_name="Trader Joe's", + store_address='401 Bay Street, San Francisco, CA 94133', + store_number=0, + items=[ + Item(name='BANANA EACH', quantity=7, price_per_unit=0.23, total_price=1.61), + Item(name='BAREBELLS CHOCOLATE DOUG', quantity=1, price_per_unit=2.29, total_price=2.29), + Item(name='BAREBELLS CREAMY CRISP', quantity=1, price_per_unit=2.29, total_price=2.29), + Item(name='BAREBELLS CHOCOLATE DOUG', quantity=1, price_per_unit=2.29, total_price=2.29), + Item(name='BAREBELLS CARAMEL CASHEW', quantity=2, price_per_unit=2.29, total_price=4.58), + Item(name='BAREBELLS CREAMY CRISP', quantity=1, price_per_unit=2.29, total_price=2.29), + Item(name='SPINDRIFT ORANGE MANGO 8', quantity=1, price_per_unit=7.49, total_price=7.49), + Item(name='Bottle Deposit', quantity=8, price_per_unit=0.05, total_price=0.4), + Item(name='MILK ORGANIC GALLON WHOL', quantity=1, price_per_unit=6.79, total_price=6.79), + Item(name='CLASSIC GREEK SALAD', quantity=1, price_per_unit=3.49, total_price=3.49), + Item(name='COBB SALAD', quantity=1, price_per_unit=5.99, total_price=5.99), + Item(name='PEPPER BELL RED XL EACH', quantity=1, price_per_unit=1.29, total_price=1.29), + Item(name='BAG FEE.', quantity=1, price_per_unit=0.25, total_price=0.25), + Item(name='BAG FEE.', quantity=1, price_per_unit=0.25, total_price=0.25) + ], + tax=0.68, + total=41.98, + date='2023-11-04', + payment_method='debit', + +) +``` + +Voila! You've successfully extracted information from a receipt using an LLM. + +## Bonus: roasting the user for their receipt + +You can roast the user for their receipt by adding a `roast` field to the end of the `ReceiptSummary` model. + +```python +class ReceiptSummary(BaseModel): + ... + roast: str +``` + +which gives you a result like + +``` +ReceiptSummary( + ... + roast="You must be a fan of Trader Joe's because you bought enough + items to fill a small grocery bag and still had to pay for a bag fee. + Maybe you should start using reusable bags to save some money and the + environment." +) +``` + +Qwen is not particularly funny, but worth a shot. diff --git a/mkdocs.yml b/mkdocs.yml index 7272e4a63..7d48bf392 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -122,6 +122,7 @@ nav: - Vision-Language Models: cookbook/atomic_caption.md - Structured Generation from PDFs: cookbook/read-pdfs.md - Earnings reports to CSV: cookbook/earnings-reports.md + - Digitizing receipts with vision models: cookbook/receipt-digitization.md - Run on the cloud: - BentoML: cookbook/deploy-using-bentoml.md - Cerebrium: cookbook/deploy-using-cerebrium.md