commit 39a3fbe0aae44740f1be349846ebad661d9066db Author: avimallu Date: Sat Jan 4 12:17:14 2025 -0600 Initial stylized working version diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..43e0488 --- /dev/null +++ b/.gitignore @@ -0,0 +1,13 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv + +# Data Files +data/* diff --git a/.idea/SplitAI.iml b/.idea/SplitAI.iml new file mode 100644 index 0000000..3eb1ff5 --- /dev/null +++ b/.idea/SplitAI.iml @@ -0,0 +1,15 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..a2c8087 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..48ed481 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/ruff.xml b/.idea/ruff.xml new file mode 100644 index 0000000..45efb46 --- /dev/null +++ b/.idea/ruff.xml @@ -0,0 +1,8 @@ + + + + + \ No newline at end of file diff --git a/.idea/ryecharm-overrides.xml b/.idea/ryecharm-overrides.xml new file mode 100644 index 0000000..7a5522d --- /dev/null +++ b/.idea/ryecharm-overrides.xml @@ -0,0 +1,11 @@ + + + + + + \ No newline at end of file diff --git a/.idea/ryecharm.xml b/.idea/ryecharm.xml new file mode 100644 index 0000000..750def3 --- /dev/null +++ b/.idea/ryecharm.xml @@ -0,0 +1,6 @@ + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..35eb1dd --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 0000000..4f8e73d --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,70 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + { + "associatedIndex": 6 +} + + + + { + "keyToString": { + "RunOnceActivity.ShowReadmeOnStart": "true", + "RunOnceActivity.git.unshallow": "true", + "SHARE_PROJECT_CONFIGURATION_FILES": "true", + "git-widget-placeholder": "main", + "last_opened_file_path": "/Users/avimallu/Documents/SplitAI", + "settings.editor.selected.configurable": "preferences.pluginManager" + } +} + + + + + 1735487052741 + + + + \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..7cd88a3 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "SplitAI" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "gradio>=5.9.1", + "great-tables>=0.15.0", + "more-itertools>=10.5.0", + "ollama>=0.4.4", + "polars>=1.18.0", + "selenium>=4.27.1", + "surya-ocr>=0.8.1", +] + +[dependency-groups] +dev = [ + "pytest>=8.3.4", + "ruff>=0.8.4", +] diff --git a/src/app/gradio_ui.py b/src/app/gradio_ui.py new file mode 100644 index 0000000..1511fb4 --- /dev/null +++ b/src/app/gradio_ui.py @@ -0,0 +1,596 @@ +from datetime import datetime +from typing import Literal, TypedDict, TypeVar + +import gradio as gr +import polars as pl +from PIL.Image import Image + +from src.app.split_ai import ReceiptReader +from src.app.utils import css_code, head_html, spinner_html + +ComponentType = TypeVar("ComponentType") + +def calculate_splits( + item_names: list[str], + item_people: list[list[str]], + item_amounts: list[float], + total: float, + tip: float, + tax: float, + people_list: list[str], + tip_split_proportionally: bool, + tax_split_proportionally: bool, + cashback_discount: float, + return_detailed_table: bool = False +) -> gr.DataFrame: + """ + A simple, but long function to calculate splits for a provided receipt. + + Args: + item_names: Names of the items being split. + item_people: A list of people for each item who are splitting its cost. + item_amounts: Amounts of the items being split. + total: The total amount in the receipt + tip: The tip in the receipt + tax: The tax in the receipt + people_list: The total number of people splitting the receipt. + tip_split_proportionally: Indicator for whether the tip is split proportional to pre-tax/tip cost. + tax_split_proportionally: Indicator for whether the tax is split proportional to pre-tax/tip cost. + cashback_discount: The total will be reduced by this percentage value. + return_detailed_table: Indicator to return full calculation table or a simplified one. + + Returns: + A DataFrame form of the provided values along with their calculated splits or a simplified version. + """ + split_count = 0 + unsplit_names = [] + checkbox_count = len(item_people) + for name, split in zip(item_names, item_people): + if len(split) > 0: + split_count += 1 + else: + unsplit_names.append(name) + if split_count != checkbox_count: + gr.Warning( + f"Pending splits: {','.join(unsplit_names)}", + title="Can't show splits yet", + ) + return gr.DataFrame(pl.DataFrame(), visible=False) + else: + # Deliberately avoiding going the numpy route here since the data is very small anyway. + split_arrays: list[list[float]] = [] + for split in item_people: + split_array = [1 / len(split) if x in split else 0.0 for x in people_list] + split_arrays.append(split_array) + split_amounts: list[list[float]] = [] + for split_array, amount in zip(split_arrays, item_amounts): + split_amount = [amount * split for split in split_array] + split_amounts.append(split_amount) + + split_subtotals = [sum(x) for x in zip(*split_amounts)] + subtotal = total - tip - tax + split_tips = [ + x / subtotal * tip if tip_split_proportionally else tax / len(people_list) + for x in split_subtotals + ] + split_taxes = [ + x / subtotal * tax if tax_split_proportionally else tax / len(people_list) + for x in split_subtotals + ] + split_totals_pre_cashback = [ + split_subtotal + split_tip + split_tax + for split_subtotal, split_tip, split_tax in zip( + split_subtotals, split_tips, split_taxes + ) + ] + split_cashback = [ + - x * cashback_discount for x in split_totals_pre_cashback + ] + split_totals_post_cashback = [ + x * (1 - cashback_discount) for x in split_totals_pre_cashback + ] + first_col_names = list(item_names) + ["Subtotal", "Tip", "Tax", "Cashback", "Total"] + splits = split_amounts + [ + split_subtotals, + split_tips, + split_taxes, + split_cashback, + split_totals_post_cashback, + ] + horizontal_totals = list(item_amounts) + [ + subtotal, + tip, + tax, + sum(split_cashback), + sum(split_totals_post_cashback), + ] + full_calculation_df = ( + pl.DataFrame( + { + "Item": first_col_names, + "splits": splits, + "Total": horizontal_totals, + }, + schema={ + "Item": pl.String, + "splits": pl.List(pl.Float64), + "Total": pl.Float64, + }, + ) + .with_columns(pl.col("splits").list.to_struct(fields=people_list)) + .unnest("splits") + .with_columns(pl.col(pl.Float64).round(2)) + ) + if return_detailed_table: + return gr.DataFrame(full_calculation_df, visible=True) + else: + simple_calculation = ( + full_calculation_df + .filter(pl.col("Item").eq("Total")) + .select(pl.exclude("Total")) + .transpose(include_header=True, header_name="Person", column_names=["Split"]) + .filter(pl.col("Person").ne("Item")) + ) + return gr.DataFrame(simple_calculation, visible=True) + + +class Item(TypedDict): + name: str + amount: float + + +class ItemSplitter: + def __init__( + self, + item: Item, + people_list: list[str], + ) -> None: + self.people_list_state = people_list + self.item = item + self.no_interaction_kwargs = {"interactive": False, "min_width": 10} + self.interaction_kwargs = self.no_interaction_kwargs | { + "container": False, + "interactive": True, + } + + def name_textbox(self, item_name: str) -> gr.Textbox: + return gr.Textbox( + item_name, show_label=False, scale=8, **self.interaction_kwargs + ) + + def amount_number(self, item_amount: float) -> gr.Number: + return gr.Number( + value=item_amount, precision=2, scale=3, **self.interaction_kwargs + ) + + def split_status_button( + self, choices: list[str] | None = None, status: Literal["⚠️", "🆗"] = "⚠️" + ) -> gr.Button: + if choices is not None: + if len(choices) == 0: + status = "⚠️" + else: + status = "🆗" + else: + choices = [] + variant: Literal["huggingface", "primary"] = ( + "huggingface" if (status == "⚠️") | (len(choices) == 0) else "primary" + ) + return gr.Button( + value=status, variant=variant, scale=1, **self.no_interaction_kwargs + ) + + def delete_item(self, item_list: list[Item]) -> list[Item]: + item_list.remove(self.item) + return item_list + + def delete_item_button(self) -> gr.Button: + kwargs = self.no_interaction_kwargs | {"interactive": True} + return gr.Button(value="❌", variant="stop", **kwargs) + + def people_list_checkbox(self, people_list: list[str]) -> gr.CheckboxGroup: + return gr.CheckboxGroup(choices=people_list, **self.interaction_kwargs) + + def generate(self) -> tuple[gr.Textbox, gr.CheckboxGroup, gr.Number, gr.Button]: + return self.generate_mobile() + + def generate_mobile(self): + with gr.Row(variant="default", equal_height=True): + item_name_textbox = self.name_textbox(self.item["name"]) + item_amount_number = self.amount_number(self.item["amount"]) + split_status_button = self.split_status_button(status="⚠️") + delete_item_button = self.delete_item_button() + people_list_checkbox = self.people_list_checkbox(self.people_list_state) + people_list_checkbox.change( + lambda x: self.split_status_button(choices=x), + people_list_checkbox, + split_status_button, + ) + return ( + item_name_textbox, + people_list_checkbox, + item_amount_number, + delete_item_button, + ) + + +class SplitAIApp: + valid_split_variant: Literal["primary"] = "primary" + invalid_split_variant: Literal["huggingface"] = "huggingface" + + def __init__(self, llm_model: str): + self.receipt_reader = ReceiptReader(llm_model) + self.demo = self.create_app() + + @staticmethod + def prepare_calculate_splits_kwargs(num_records: int, *all_values) -> gr.DataFrame: + """ + This method is necessary because only a list[gr.Component] or similar can be sent as + `inputs` to an event listener. Therefore, it is unpacked here and prepared into a + dictionary based on how it is sent by the event. This method is specifically for + the `get_split_button.click` event listener. + + Args: + num_records: The number of items present to split. + *all_values: A list of components to forward to `calculate_splits`. + + Returns: + gr.DataFrame + """ + kwargs = { + "item_names": all_values[:num_records], + "item_people": all_values[num_records : num_records * 2], + "item_amounts": all_values[num_records * 2 : num_records * 3], + } + additional_kwargs = { + k: v + for k, v in zip( + [ + "total", + "tip", + "tax", + "people_list", + "tip_split_proportionally", + "tax_split_proportionally", + "cashback_discount", + "return_detailed_table", + ], + tuple(all_values[num_records * 3 :]), + ) + } + additional_kwargs["cashback_discount"] /= 100 + kwargs.update(additional_kwargs) + return calculate_splits(**kwargs) + + @staticmethod + def update_component_attributes(component: ComponentType, **kwargs) -> ComponentType: + """ + This requirement is in place because Gradio expects you to provide A NEW INSTANCE of + the component that you want to update with its attributes changed. It seems like it + doesn't replace the component, but updates it this way. Very weird behavior. + + Args: + component: The gradio component to update attributes for. + **kwargs: (attribute, value) pairs to update in child. + + Returns: + A new instance of child's class with the updated attributes. + """ + gradio_class = type(component) + try: + return gradio_class(**kwargs) + except Exception as err: + print( + f"The Gradio component {gradio_class} does not have one of the provided attribute keys." + ) + raise err + + @staticmethod + def validate_people_list(people_textbox) -> tuple[gr.Image, list]: + if "," in people_textbox and people_textbox[-1] != ",": + people_list = [x.strip() for x in people_textbox.split(",")] + return gr.Image(interactive=True), people_list + else: + gr.Warning("You need to enter a list of names separated by commas.") + return gr.Image(interactive=False), [] + + def create_app(self) -> gr.Blocks(): + # `head_html` required to prevent iOS from scaling the UI when clicking on a textbox. + with gr.Blocks( + css=css_code, + head=head_html, + theme="JohnSmith9982/small_and_pretty", + fill_width=True, + ) as split_app: + with gr.Column(): + self.people_textbox = gr.Textbox( + placeholder="Split names with a comma", + label="Who all are splitting this receipt?", + lines=1, + autofocus=True, + submit_btn="Submit", + ) + self.people_list = gr.State([]) + self.image_uploader = gr.Image( + show_label=False, scale=1, type="pil", interactive=False + ) + self.people_textbox.submit( + SplitAIApp.validate_people_list, + [self.people_textbox], + [self.image_uploader, self.people_list], + ) + with gr.Column(): + with gr.Column(): + with gr.Row(): + self.merchant = gr.Textbox( + interactive=True, + label="Merchant Name", + min_width=20, + visible=False, + scale=2, + ) + self.receipt_date = gr.DateTime( + interactive=True, + include_time=False, + type="datetime", + label="Date", + min_width=20, + visible=False, + scale=1, + ) + with gr.Row(): + self.total_amount = gr.Number( + interactive=True, + label="Total", + min_width=20, + visible=False, + precision=2, + ) + self.tip_amount = gr.Number( + interactive=True, + label="Tip", + min_width=20, + visible=False, + precision=2, + ) + self.tax_amount = gr.Number( + interactive=True, + label="Tax", + min_width=20, + visible=False, + precision=2, + ) + self.items = gr.State([]) + + @gr.render(inputs=[self.items, self.people_list]) + def render_items(items: list[Item], people_list: list[str]): + item_names = [] + item_peoples = [] + item_amounts = [] + for key, item in enumerate(items): + with gr.Column(variant="compact"): + splitter = ItemSplitter(item, people_list) + item_name, item_people, item_amount, delete_item_button = ( + splitter.generate() + ) + # This event needs to be defined outside the ItemSplitter class + # because it references a gr.State variable. All Gradio components + # can be properly pass ONLY via event listeners, as their state is + # managed by Gradio outside the flow of the Python app. + delete_item_button.click( + splitter.delete_item, self.items, self.items + ) + item_names.append(item_name) + item_peoples.append(item_people) + item_amounts.append(item_amount) + + self.split_tip_proportionally = gr.Checkbox( + value=True, + label="Split tip proportional to other costs", + info="If unchecked, will split equally.", + interactive=True, + ) + self.split_tax_proportionally = gr.Checkbox( + value=True, + label="Split tax proportional to other costs", + info="If unchecked, will split equally.", + interactive=True, + ) + self.add_cashback_discount = gr.Slider( + minimum=0, maximum=25, value=0, step=0.5, + label="Cashback discount to apply on total", + info="Choose a number between 0% and 100%.", + interactive=True, + ) + self.show_detailed_table = gr.Checkbox( + value=False, + label="Show a detailed calculation table", + info="If unchecked, will just show the splits.", + interactive=True, + ) + + with gr.Row(): + self.integrity_markdown = gr.Markdown( + show_label=False, value="", visible=False + ) + + with gr.Row(): + get_splits_button = gr.Button( + "Get Splits", variant="primary", scale=5, min_width=10 + ) + get_splits_button.click( + lambda *x: SplitAIApp.prepare_calculate_splits_kwargs( + len(item_names), *x + ), + inputs=( + item_names + + item_peoples + + item_amounts + + [ + self.total_amount, + self.tip_amount, + self.tax_amount, + self.people_list, + self.split_tip_proportionally, + self.split_tax_proportionally, + self.add_cashback_discount, + self.show_detailed_table, + ] + ), + outputs=self.display_result, + ) + + add_item_button = gr.Button("➕", variant="secondary", scale=1, min_width=10) + + def add_item( + items: list[Item], + ): + new_item_name = f"Item {len(items)+1}" + return items + [ + { + "name": new_item_name, + "amount": 0.0, + } + ] + + add_item_button.click( + add_item, + inputs=[self.items], + outputs=[self.items], + ) + + # Constantly keep track of whether totals match or not. + def integrity_checker(*args) -> gr.Markdown: + items = args[: len(args) - 3] + tip_amount, tax_amount, total_amount = args[len(args) - 3 :] + subtotal = sum(items) + if subtotal + tip_amount + tax_amount != total_amount: + return gr.Markdown( + f"⚠️ Looks like the total ({total_amount}) doesn't match the value of subtotal ({subtotal}) + tip ({tip_amount}) + tax ({tax_amount}) ⚠️", + show_label=False, + visible=True, + ) + else: + return gr.Markdown(visible=False) + + gr.on( + triggers=[x.change for x in item_amounts] + + [ + self.tip_amount.change, + self.tax_amount.change, + self.total_amount.change, + ], + fn=integrity_checker, + inputs=item_amounts + + [ + self.tip_amount, + self.tax_amount, + self.total_amount, + ], + outputs=[self.integrity_markdown], + ) + + self.display_result = gr.DataFrame(value=None, visible=False) + + self.spinner_html = gr.HTML( + spinner_html, + visible=False, + padding=False, + ) + + self.image_uploader.upload( + lambda: gr.HTML(visible=True), inputs=None, outputs=self.spinner_html + ).then( + self.process_image, + inputs=[self.image_uploader, self.items], + outputs=[ + self.merchant, + self.receipt_date, + self.total_amount, + self.tip_amount, + self.tax_amount, + self.items, + ], + show_progress="hidden", + ).then( + lambda: gr.HTML(visible=False), inputs=None, outputs=self.spinner_html + ) + + return split_app + + def process_image( + self, + image: Image, + items: gr.State, + ): # -> gr.State: + # gr.Info("Running OCR on image of receipt.") + # receipt_string = self.receipt_reader.get_ordered_text(image) + # gr.Info("Extracting components. Please be patient.") + # receipt_extracted = self.receipt_reader.extract_components(receipt_string) + receipt_extracted = { + "merchant": "FUBAR", + "receipt_date": datetime.now(), + "total": {"amount": 15}, + "tip": {"amount": 0}, + "tax": {"amount": 3}, + "item_amounts": [ + {"name": "PET TOY", "currency": "$", "amount": 2}, + {"name": "FLOPPY PUPPY", "currency": "$", "amount": 4}, + {"name": "SSSUPREME S", "currency": "$", "amount": 6}, + ], + } + key_value_updates = [ + { + "component": self.merchant, + "kwargs": {"value": receipt_extracted["merchant"], "visible": True}, + }, + { + "component": self.receipt_date, + "kwargs": { + "value": receipt_extracted["receipt_date"], + "visible": True, + }, + }, + { + "component": self.total_amount, + "kwargs": { + "value": receipt_extracted["total"]["amount"], + "visible": True, + }, + }, + { + "component": self.tip_amount, + "kwargs": { + "value": receipt_extracted["tip"]["amount"], + "visible": True, + }, + }, + { + "component": self.tax_amount, + "kwargs": { + "value": receipt_extracted["tax"]["amount"], + "visible": True, + }, + }, + ] + out = [ + self.update_component_attributes(x["component"], **x["kwargs"]) + for x in key_value_updates + ] + items += [ + {"name": x["name"], "amount": x["amount"]} + for x in receipt_extracted["item_amounts"] + ] + out += [items] + return out + + def launch(self, expose_to_local_network: bool = False): + if expose_to_local_network: + self.demo.queue().launch(server_name="0.0.0.0", server_port=7860) + else: + self.demo.queue().launch() + + +if __name__ == "__main__": + demo = SplitAIApp("qwen2.5") + demo.launch(True) diff --git a/src/app/split_ai.py b/src/app/split_ai.py new file mode 100644 index 0000000..6c4d122 --- /dev/null +++ b/src/app/split_ai.py @@ -0,0 +1,22 @@ +from src.llm.ollama import LLMExtractor, ReceiptExtracted +from src.ocr.surya import SuryaOCR +from PIL.Image import Image +from PIL.Image import open as PIL_open + + +class ReceiptReader: + def __init__(self, llm_model: str): + self.ocr_model = SuryaOCR() + self.llm_model = LLMExtractor(model=llm_model) + + def get_ordered_text(self, image: Image) -> str: + return self.ocr_model.ordered_ocr_text(image) + + def extract_components(self, receipt_string: str) -> ReceiptExtracted: + return self.llm_model.forward(receipt_string, enable_alerts=False) + + +if __name__ == "__main__": + image = PIL_open("data/pytest/image_upload_test.png") + sr = ReceiptReader() + print(sr.extract_components(image)) diff --git a/src/app/utils.py b/src/app/utils.py new file mode 100644 index 0000000..df3fa3b --- /dev/null +++ b/src/app/utils.py @@ -0,0 +1,52 @@ +head_html = """ + +""" + +css_code = """ +input { + font-size: 16px !important; +} +input[type="number"] { + inputmode: numeric; + pattern: "[0-9]*"; +} +#fullwidth-checkgroup .gr-checkgroup { + width: 100%; +} +footer { + display: none !important; +} + +.loader { + width: 48px; + height: 48px; + border: 5px solid #FFF; + border-bottom-color: transparent; + border-radius: 50%; + display: inline-block; + box-sizing: border-box; + animation: rotation 1s linear infinite; + } + + @keyframes rotation { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(360deg); + } + } +""" + +spinner_html = """ +
+ +
+""" \ No newline at end of file diff --git a/src/llm/ollama.py b/src/llm/ollama.py new file mode 100644 index 0000000..f484c59 --- /dev/null +++ b/src/llm/ollama.py @@ -0,0 +1,196 @@ +import json +import yaml +import gradio as gr +from ollama import chat +from pathlib import Path +from datetime import datetime +from pydantic import BaseModel +from typing import TypedDict, Optional, Literal, Any, Type, Annotated + + +class Message(TypedDict): + role: Literal["user", "assistant", "system", "tool"] + content: Optional[str] + images: Optional[list[str] | bytes | list[Path]] + + +class ReceiptMerchant(BaseModel): + name: str + +class ReceiptDate(BaseModel): + date: datetime + +def is_valid_currency_code(value: str) -> bool: + return len(value) == 3 and value.isupper() and value.isalpha() + +class ReceiptAmount(BaseModel): + currency: Annotated[str, is_valid_currency_code] + amount: float + +class Amount(TypedDict): + currency: Annotated[str, is_valid_currency_code] + amount: float + +class ReceiptItemized(BaseModel): + class ReceiptLineItemAmount(BaseModel): + name: str + currency: Annotated[str, is_valid_currency_code] + amount: float + ItemizedReceipt: list[ReceiptLineItemAmount] + +class ItemizedAmounts(TypedDict): + name: str + currency: Annotated[str, is_valid_currency_code] + amount: float + +class ReceiptExtracted(TypedDict): + merchant: str + receipt_date: datetime + total_amount: Amount + tip_amount: Amount + tax_amount: Amount + item_amounts: list[ItemizedAmounts] + +class LLMExtractor: + def __init__(self, model: str, prompt_path: str | Path = "./src/llm/prompts.yaml"): + """ + + Args: + model: A model name that has been downloaded by ``ollama``. + prompt_path: A str or Path to a YAML file with various prompts. + """ + self.model = model + self.prompt_path = Path(prompt_path) + with open(self.prompt_path, "r") as f: + self.prompts = yaml.safe_load(f) + + def get_chat_response( + self, + messages: list[Message], + structured_output_format: Type[BaseModel] | None = None, + ) -> Any: + """ + + Args: + messages: The conversation so far. + structured_output_format: The dictionary format of the pydantic model schema. Can be ``None``. + + Returns: + Depends on the model chosen, and if `structured_output_format` is provided. + """ + response = chat( + model=self.model, messages=messages, format=structured_output_format.model_json_schema(), + options={"temperature": 0} + ) + message = response.message.content + if structured_output_format is not None: + return json.loads(message) + else: + return message + + def load_substituted_prompt(self, prompt_name: str, **kwargs) -> str: + prompt = self.prompts[prompt_name] + for patt, repl in kwargs.items(): + prompt = prompt.replace(f"[[ {patt} ]]", repl) + return prompt + + def extract_merchant_name(self, receipt_string: str) -> str: + messages: list[Message] = [ + { + "role": "system", + "content": self.load_substituted_prompt( + "extract_merchant_name", **{"receipt_string": receipt_string} + ), + }, + ] + return self.get_chat_response(messages, ReceiptMerchant)['name'] + + def extract_receipt_date(self, receipt_string: str) -> datetime: + messages: list[Message] = [ + { + "role": "system", + "content": self.load_substituted_prompt( + "extract_receipt_date", **{"receipt_string": receipt_string} + ), + }, + ] + receipt_date = self.get_chat_response(messages, ReceiptDate)['date'] + return datetime.fromisoformat(receipt_date) + + def extract_receipt_total_amount(self, receipt_string: str) -> Amount: + messages: list[Message] = [ + { + "role": "system", + "content": self.load_substituted_prompt( + "extract_receipt_total_amount", **{"receipt_string": receipt_string} + ), + }, + ] + currency_amount = self.get_chat_response(messages, ReceiptAmount) + return currency_amount + + def extract_receipt_tax_amount(self, receipt_string: str) -> Amount: + messages: list[Message] = [ + { + "role": "system", + "content": self.load_substituted_prompt( + "extract_receipt_tax_amount", **{"receipt_string": receipt_string} + ), + }, + ] + currency_amount = self.get_chat_response(messages, ReceiptAmount) + return currency_amount + + def extract_receipt_tip_amount(self, receipt_string: str) -> Amount: + messages: list[Message] = [ + { + "role": "system", + "content": self.load_substituted_prompt( + "extract_receipt_tip_amount", **{"receipt_string": receipt_string} + ), + }, + ] + currency_amount = self.get_chat_response(messages, ReceiptAmount) + return currency_amount + + def extract_receipt_items(self, receipt_string: str) -> list[ItemizedAmounts]: + messages: list[Message] = [ + { + "role": "system", + "content": self.load_substituted_prompt( + "extract_receipt_items", **{"receipt_string": receipt_string} + ), + }, + ] + itemized_amounts = self.get_chat_response(messages, ReceiptItemized)['ItemizedReceipt'] + return itemized_amounts + + def forward(self, receipt_string, enable_alerts:bool=False) -> ReceiptExtracted: + merchant = self.extract_merchant_name(receipt_string) + # gr.Info("Extracted merchant name.") + receipt_date = self.extract_receipt_date(receipt_string) + # gr.Info("Extracted receipt date.") + total_amount = self.extract_receipt_total_amount(receipt_string) + # gr.Info("Extracted total amount.") + tip_amount = self.extract_receipt_tip_amount(receipt_string) + # gr.Info("Extracted tip amount.") + tax_amount = self.extract_receipt_tax_amount(receipt_string) + # gr.Info("Extracted tax amount. Extracting individual items - this will take time.") + item_amounts = self.extract_receipt_items(receipt_string) + # gr.Info("Extracted individual items. Populating items now!") + return { + "merchant": merchant, + "receipt_date": receipt_date, + "total": total_amount, + "tip": tip_amount, + "tax": tax_amount, + "item_amounts": item_amounts + } + # return { + # "merchant": self.extract_merchant_name(receipt_string), + # "receipt_date": self.extract_receipt_date(receipt_string), + # "total": self.extract_receipt_total_amount(receipt_string), + # "tip": self.extract_receipt_tip_amount(receipt_string), + # "tax": self.extract_receipt_tax_amount(receipt_string), + # "item_amounts": self.extract_receipt_items(receipt_string) + # } diff --git a/src/llm/prompts.yaml b/src/llm/prompts.yaml new file mode 100644 index 0000000..d98da4d --- /dev/null +++ b/src/llm/prompts.yaml @@ -0,0 +1,89 @@ +extract_merchant_name: > + You are an experience cashier who is trying to make sense of a receipt string that a computing system has run OCR on. + + You are trying to identify the merchant name. + For restaurants, this will be the restaurant name. + For a grocery store or supermarket, this will be the supermarket's name. + For other retailers, it will be the retailer's name. + If you can't find a name, provide `null` as the response. + + The receipt string is provided below: + + ``` + [[ receipt_string ]] + ``` + + Extract the name of the merchant from this receipt using your knowledge of the industry and the receipt itself. + +extract_receipt_date: > + You are an experience cashier who is trying to make sense of a receipt string that a computing system has run OCR on. + + You are trying to identify the date (in YYYY-MM-DD format) on the receipt. + If you can't find a date, provide `null` as the response. + + The receipt string is provided below: + + ``` + [[ receipt_string ]] + ``` + + Extract the date of this receipt using your knowledge of the industry and the receipt itself. + +extract_receipt_total_amount: > + You are an experience cashier who is trying to make sense of a receipt string that a computing system has run OCR on. + + You are trying to identify the total amount on the receipt, as well as the three letter currency code (ISO 4217). + If you cannot find the total amount, provide '0' as the total amount. + + The receipt string is provided below: + + ``` + [[ receipt_string ]] + ``` + + Extract the currency and amount of the total in this receipt using your knowledge of the industry + and the receipt itself. + +extract_receipt_tax_amount: > + You are an experience cashier who is trying to make sense of a receipt string that a computing system has run OCR on. + + You are trying to identify the overall tax amount on the receipt, as well as the three letter currency code (ISO 4217). + If you cannot find the tax amount, provide '0' for the tax amount. + + The receipt string is provided below: + + ``` + [[ receipt_string ]] + ``` + + Extract the currency and amount of the overall tax in this receipt using your knowledge of the industry + and the receipt itself. + +extract_receipt_tip_amount: > + You are an experience cashier who is trying to make sense of a receipt string that a computing system has run OCR on. + + You are trying to identify the tip paid (including gratuity, but not tax) on the receipt, as well as the three letter + currency code (ISO 4217). If there is no tip, provide '0' as the tip. + + The receipt string is provided below: + + ``` + [[ receipt_string ]] + ``` + + Extract the currency and amount of the overall tip in this receipt using your knowledge of the industry + and the receipt itself. + +extract_receipt_items: > + You are an experience cashier who is trying to make sense of a receipt string that a computing system has run OCR on. + + You are trying to identify each of the items present, and the amount paid for those items. + + The receipt string is provided below: + + ``` + [[ receipt_string ]] + ``` + + Extract a list of items by their name, currency (3 letter code) and amount paid for it using your knowledge of the industry + and the receipt itself. \ No newline at end of file diff --git a/src/ocr/surya.py b/src/ocr/surya.py new file mode 100644 index 0000000..fddb4b0 --- /dev/null +++ b/src/ocr/surya.py @@ -0,0 +1,185 @@ +from pathlib import Path +from PIL.Image import open as PIL_open +from PIL.Image import Image +from surya.ocr import run_ocr +from surya.model.detection.model import ( + load_model as load_det_model, + load_processor as load_det_processor, +) +from surya.model.recognition.model import load_model as load_rec_model +from surya.model.recognition.processor import load_processor as load_rec_processor +from typing import Literal, TypedDict, overload, Optional +from collections import defaultdict +from more_itertools import bucket + +Polygon = list[list[float]] +BBox = list[float] + + +class SuryaOCRResult(TypedDict): + polygon: Polygon + confidence: Optional[float] + text: str + bbox: BBox + + +class SplitAIOCRResult(TypedDict): + polygons: list[Polygon] + texts: list[str] + bboxes: list[BBox] + + +class SuryaOCR: + def __init__(self): + self.det_processor = load_det_processor() + self.det_model = load_det_model() + self.rec_model = load_rec_model() + self.rec_processor = load_rec_processor() + + @overload + def ocr_image(self, image: Image, return_format: Literal["polygon"]) -> list[Polygon]: ... + @overload + def ocr_image(self, image: Image, return_format: Literal["text"]) -> list[str]: ... + @overload + def ocr_image(self, image: Image, return_format: Literal["bbox"]) -> list[BBox]: ... + @overload + def ocr_image(self, image: Image, return_format: Literal["confidence"]) -> list[BBox]: ... + @overload + def ocr_image(self, image: Image, return_format: None) -> SplitAIOCRResult: ... + def ocr_image( + self, + image: Image, + return_format: Literal["polygon", "text", "bbox", "confidence"] | None, + ) -> SplitAIOCRResult | list[Polygon] | list[str] | list[BBox]: + """ + Specify either a path to an image or image file itself (as a PIL image) to run OCR on. + If both are provided, then the image file is prioritized. + + Args: + image: the PIL image for Surya to process + return_format: Specify one of the allowed values to return only this key from SuryaOCR's output. + + Returns: + OCRResult, in Surya's format. See `` for details. + """ + images = [image] + langs = [["en"]] + + ocr_output = run_ocr( + images, + langs, + self.det_model, + self.det_processor, + self.rec_model, + self.rec_processor, + ) + ocr_as_list: list[SuryaOCRResult] = ocr_output[0].model_dump()["text_lines"] + if isinstance(return_format, str): + return [x[return_format] for x in ocr_as_list] + else: + polygons = [x["polygon"] for x in ocr_as_list] + texts = [x["text"] for x in ocr_as_list] + bboxes = [x["bbox"] for x in ocr_as_list] + return { + "polygons": polygons, + "texts": texts, + "bboxes": bboxes, + } + + def ordered_ocr_text(self, image: Image) -> str: + split_ocr_result = self.ocr_image(image, None) + ordered_text: list[str] = [] + for group in SuryaOCR.order_polygons(split_ocr_result["polygons"]): + line = [split_ocr_result["texts"][x] for x in group] + ordered_text += [" ".join(line)] + return "\n".join(ordered_text) + + @staticmethod + def get_centroid_of_bounding_polygon( + polygon: Polygon, + ) -> tuple[float, float]: + """ + Function name is self-explanatory. Invariant of rotation. Any bounding polygon whose centroid differs + significantly to another one is unlikely to be in the same line. + + Args: + polygon: `[[x_1, y_1], [x_2, y_2], [x_3, y_3], [x_4, y_4]]` coordinates of + the polygon, clockwise from top left. + + Returns: + The centroid of the bounding polygon. + """ + (x_1, y_1), (x_2, y_2), (x_3, y_3), (x_4, y_4) = tuple( + tuple(x) for x in polygon + ) + + return (x_1 + x_4) / 2, (y_1 + y_2) / 2 + + @staticmethod + def get_height_of_bounding_polygon(polygon: Polygon) -> float: + """ + + Args: + polygon: `[[x_1, y_1], [x_2, y_2], [x_3, y_3], [x_4, y_4]]` coordinates of + the polygon, clockwise from top left. + Returns: + The height of the bounding polygon. + """ + (x_1, y_1), (x_2, y_2), (x_3, y_3), (x_4, y_4) = tuple( + tuple(x) for x in polygon + ) + + return y_4 - y_1 + + @staticmethod + def order_polygons(polygons: list[Polygon]) -> list[list[int]]: + """ + Given a list of bounding polygons (from an OCR framework) for recognized text, attempt + to reorder them (left-to-right, top-to-bottom) that their text contents are expected to + be read. + + This is intended to work independent of the orientation of the receipts, but currently + is at a PoC stage where it is assumed that the receipt is horizontally positioned. + + Args: + polygons: Bounding polygons of recognized text. + + Returns: + A ``list[int]`` with the estimated line numbers. + """ + x_midpoints, y_midpoints = zip( + *[SuryaOCR.get_centroid_of_bounding_polygon(x) for x in polygons] + ) + heights = [SuryaOCR.get_height_of_bounding_polygon(x) for x in polygons] + threshold = 0.6 + y_ranges = [ + ( + midpoint - height * threshold * 0.5, + midpoint, + midpoint + height * threshold * 0.5, + ) + for midpoint, height in zip(y_midpoints, heights) + ] + + # Assign line groups + line_groups: dict[int, int | None] = defaultdict(lambda: None) + for idx, i_range in enumerate(y_ranges): + if idx not in line_groups: + line_groups[idx] = idx + for jdx, j_range in [ + (jdx, j_range) for jdx, j_range in enumerate(y_ranges) if jdx > idx + ]: + if (i_range[0] <= j_range[1] <= i_range[2]) and jdx not in line_groups: + line_groups[jdx] = idx + + # Reorder by x_midpoints within a group: + line_groups_reversed = [(group, key) for key, group in line_groups.items()] + bucketed_line_groups = bucket(line_groups_reversed, key=lambda x: x[0]) + ordered_text = [] + for key in sorted(list(bucketed_line_groups)): + idx_group = list(bucketed_line_groups[key]) + sorted_list = sorted(idx_group, key=lambda x: x_midpoints[x[1]]) + sorted_idx = [x[1] for x in sorted_list] + ordered_text += [sorted_idx] + + return ordered_text diff --git a/tests/test_ollama.py b/tests/test_ollama.py new file mode 100644 index 0000000..02d017d --- /dev/null +++ b/tests/test_ollama.py @@ -0,0 +1,54 @@ +from src.llm.ollama import LLMExtractor +from datetime import date + + +def test_ollama_response(): + messages: list[Message] = [{"role": "user", "content": "Hello"}] + output = get_chat_response(model="gemma2:27b", messages=messages) + assert isinstance(output, str) + + +def test_ollama_structured_response(): + class ModelData(BaseModel): + name: str + knowledge_cutoff: date + + messages: list[Message] = [{"role": "user", "content": "Tell me about yourself"}] + output = get_chat_response( + model="gemma2:27b", + messages=messages, + structured_output_format=ModelData.model_json_schema(), + ) + assert ModelData.model_validate(output) + + +def test_ollama_image_upload(): + messages: list[Message] = [ + { + "role": "user", + "content": "Run OCR on this image.", + "images": ["./data/pytest/image_upload_test.png"], + } + ] + output = get_chat_response( + model="llava:34b", + messages=messages, + ) + print(output) + assert False + + +def test_llm_extractor(): + llm_extractor = LLMExtractor(model="gemma2:27b") + with open("data/pytest/receipt_ocr_test.txt", "r") as f: + receipt_ocr_text = "".join(f.readlines()) + assert llm_extractor.extract_merchant_name(receipt_ocr_text) == 'Walmart' + assert llm_extractor.extract_receipt_date(receipt_ocr_text) == date.fromisoformat('2017-07-28') + assert llm_extractor.extract_receipt_total_amount(receipt_ocr_text) == {'currency': 'USD', 'amount': 98.21} + assert llm_extractor.extract_receipt_tip_amount(receipt_ocr_text) == {'currency': 'USD', 'amount': 0} + assert llm_extractor.extract_receipt_tax_amount(receipt_ocr_text) == {'currency': 'USD', 'amount': 4.59} + items = llm_extractor.extract_receipt_items(receipt_ocr_text) + # The exact items in these cannot be always identical as there will be some inherent variance in LLM output. + # Thus, we check if a few control totals match, at least approximately. + assert len(items) == 26 + assert round(sum(x["amount"] for x in items), 0) == 94 diff --git a/tests/test_surya.py b/tests/test_surya.py new file mode 100644 index 0000000..f2ea082 --- /dev/null +++ b/tests/test_surya.py @@ -0,0 +1,7 @@ +from src.ocr.surya import SuryaOCR + + +def test_ocr_sample_image(): + image_path = "data/pytest/image_upload_test.png" + output = ocr_image(image_path=image_path) + assert output is not None diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..485d205 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1789 @@ +version = 1 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version < '3.12' and platform_system == 'Darwin'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and platform_system == 'Linux'", + "(python_full_version < '3.12' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.12' and platform_system != 'Darwin' and platform_system != 'Linux')", + "python_full_version == '3.12.*' and platform_system == 'Darwin'", + "python_full_version >= '3.13' and platform_system == 'Darwin'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_system == 'Linux'", + "python_full_version >= '3.13' and 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