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| from typing import Optional | |
| from transformers import AutoTokenizer | |
| import re | |
| # Updated to use DeepSeek model instead of Llama | |
| BASE_MODEL = "deepseek-ai/deepseek-coder-7b-instruct" | |
| MIN_TOKENS = 150 | |
| MAX_TOKENS = 160 | |
| MIN_CHARS = 300 | |
| CEILING_CHARS = MAX_TOKENS * 7 | |
| class Item: | |
| """ | |
| An Item is a cleaned, curated datapoint of a Product with a Price | |
| """ | |
| # Load tokenizer with error handling for better reliability | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) | |
| if tokenizer.pad_token is None: | |
| tokenizer.pad_token = tokenizer.eos_token | |
| except Exception as e: | |
| print(f"Warning: Could not load DeepSeek tokenizer: {e}") | |
| print("Using fallback tokenization mechanisms") | |
| tokenizer = None | |
| PREFIX = "Price is $" | |
| QUESTION = "How much does this cost to the nearest dollar?" | |
| REMOVALS = ['"Batteries Included?": "No"', '"Batteries Included?": "Yes"', '"Batteries Required?": "No"', '"Batteries Required?": "Yes"', "By Manufacturer", "Item", "Date First", "Package", ":", "Number of", "Best Sellers", "Number", "Product "] | |
| title: str | |
| price: float | |
| category: str | |
| token_count: int = 0 | |
| details: Optional[str] | |
| prompt: Optional[str] = None | |
| include = False | |
| def __init__(self, data, price): | |
| self.title = data['title'] | |
| self.price = price | |
| self.parse(data) | |
| def scrub_details(self): | |
| """ | |
| Clean up the details string by removing common text that doesn't add value | |
| """ | |
| details = self.details | |
| for remove in self.REMOVALS: | |
| details = details.replace(remove, "") | |
| return details | |
| def scrub(self, stuff): | |
| """ | |
| Clean up the provided text by removing unnecessary characters and whitespace | |
| Also remove words that are 7+ chars and contain numbers, as these are likely irrelevant product numbers | |
| """ | |
| stuff = re.sub(r'[:\[\]"{}【】\s]+', ' ', stuff).strip() | |
| stuff = stuff.replace(" ,", ",").replace(",,,",",").replace(",,",",") | |
| words = stuff.split(' ') | |
| select = [word for word in words if len(word)<7 or not any(char.isdigit() for char in word)] | |
| return " ".join(select) | |
| def parse(self, data): | |
| """ | |
| Parse this datapoint and if it fits within the allowed Token range, | |
| then set include to True | |
| """ | |
| contents = '\n'.join(data['description']) | |
| if contents: | |
| contents += '\n' | |
| features = '\n'.join(data['features']) | |
| if features: | |
| contents += features + '\n' | |
| self.details = data['details'] | |
| if self.details: | |
| contents += self.scrub_details() + '\n' | |
| if len(contents) > MIN_CHARS: | |
| contents = contents[:CEILING_CHARS] | |
| text = f"{self.scrub(self.title)}\n{self.scrub(contents)}" | |
| # Use tokenizer if available, otherwise fall back to character-based limits | |
| if self.tokenizer: | |
| try: | |
| tokens = self.tokenizer.encode(text, add_special_tokens=False) | |
| if len(tokens) > MIN_TOKENS: | |
| tokens = tokens[:MAX_TOKENS] | |
| text = self.tokenizer.decode(tokens) | |
| self.make_prompt(text) | |
| self.include = True | |
| except Exception as e: | |
| print(f"Tokenization error: {e}") | |
| self._fallback_parse(text) | |
| else: | |
| self._fallback_parse(text) | |
| def _fallback_parse(self, text): | |
| """ | |
| Fallback parsing mechanism when tokenizer isn't available | |
| """ | |
| # Simple character-based limit as fallback | |
| if len(text) > MIN_CHARS: | |
| self.make_prompt(text[:CEILING_CHARS//2]) # Conservative limit | |
| self.include = True | |
| self.token_count = len(text) // 4 # Rough token estimate | |
| def make_prompt(self, text): | |
| """ | |
| Set the prompt instance variable to be a prompt appropriate for training | |
| Format adjusted for DeepSeek model's expected chat format | |
| """ | |
| self.prompt = f"{self.QUESTION}\n\n{text}\n\n" | |
| self.prompt += f"{self.PREFIX}{str(round(self.price))}.00" | |
| # Calculate token count if tokenizer is available | |
| if self.tokenizer: | |
| try: | |
| self.token_count = len(self.tokenizer.encode(self.prompt, add_special_tokens=False)) | |
| except: | |
| self.token_count = len(self.prompt) // 4 # Rough estimate | |
| else: | |
| self.token_count = len(self.prompt) // 4 # Rough estimate | |
| def test_prompt(self): | |
| """ | |
| Return a prompt suitable for testing, with the actual price removed | |
| """ | |
| return self.prompt.split(self.PREFIX)[0] + self.PREFIX | |
| def deepseek_chat_format(self): | |
| """ | |
| Return the prompt formatted for DeepSeek's chat format | |
| """ | |
| if not self.prompt: | |
| return None | |
| base_prompt = self.prompt.split(self.PREFIX)[0] | |
| return f"<|im_start|>user\n{base_prompt}<|im_end|>\n<|im_start|>assistant\n{self.PREFIX}" | |
| def __repr__(self): | |
| """ | |
| Return a String version of this Item | |
| """ | |
| return f"<{self.title} = ${self.price}>" | |