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| # utils.py | |
| from fuzzywuzzy import fuzz | |
| import re | |
| def extract_keyword(text: str, symptoms: list = None) -> str: | |
| """ | |
| Extracts a primary keyword from a given text, prioritizing symptom matches if available, | |
| otherwise, the first relevant word. | |
| Handles both "Question: " prefixed strings and direct symptom strings. | |
| """ | |
| if text.startswith("Question: "): | |
| # Remove "Question: " prefix and process question text | |
| question = text[10:].strip() | |
| words = question.split() | |
| if not words: | |
| return "Unknown" | |
| # Common words to skip - expanded list | |
| common_words = { | |
| 'what', 'is', 'are', 'how', 'why', 'can', 'do', 'does', 'i', 'have', 'my', 'a', 'an', 'the', | |
| 'in', 'of', 'and', 'or', 'for', 'with', 'from', 'about', 'some', 'any', 'this', 'that', | |
| 'there', 'be', 'to', 'me', 'am', 'feel', 'feeling', 'experiencing', 'symptoms', 'issue', | |
| 'problem', 'cause', 'causes', 'tell', 'me', 'more', 'information', 'on', 'about', 'a', | |
| 'an', 'the', 'my', 'your', 'its', 'their', 'our', 'his', 'her', 'its', 'them', 'us', 'you', | |
| 'i', 'we', 'he', 'she', 'it', 'they', 'this', 'that', 'these', 'those', 'which', 'who', | |
| 'whom', 'whose', 'where', 'when', 'why', 'how', 'what', 'if', 'then', 'else', 'or', 'and', | |
| 'but', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', | |
| 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', | |
| 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', | |
| 'here', 'there', 'when', 'where', 'why', 'all', 'any', 'both', 'each', 'few', 'more', 'most', | |
| 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', | |
| 'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now' | |
| } | |
| # Fuzzy matching against symptoms (if provided) | |
| if symptoms: | |
| best_match = None | |
| highest_score = 0 | |
| # Prioritize multi-word symptoms if they match well | |
| for symptom_phrase in sorted(symptoms, key=len, reverse=True): | |
| for i in range(len(words)): | |
| for j in range(i + 1, len(words) + 1): | |
| phrase_from_question = " ".join(words[i:j]).lower() | |
| score = fuzz.token_sort_ratio(phrase_from_question, symptom_phrase.lower()) | |
| if score > 85 and score > highest_score: | |
| best_match = symptom_phrase | |
| highest_score = score | |
| if best_match: | |
| return best_match.capitalize() | |
| # Fallback: pick the first non-common word longer than 2 characters | |
| for word in words: | |
| word_lower = word.lower() | |
| if word_lower not in common_words and len(word_lower) > 2: | |
| if not re.match(r'^\d+$', word_lower) and not re.match(r'^\w$', word_lower): | |
| return word.capitalize() | |
| # Last resort: if no good keyword, take the first non-common word | |
| for word in words: | |
| word_lower = word.lower() | |
| if word_lower not in common_words: | |
| return word.capitalize() | |
| return words[0].capitalize() if words else "Unknown" | |
| else: | |
| # For symptom checker inputs (comma-separated symptoms) | |
| # The history stores the raw selected symptoms here, so we just return the first one or the full list if short | |
| symptom_list_str = text.strip() | |
| if symptom_list_str: | |
| # If it's a short list of symptoms, return the whole thing | |
| if len(symptom_list_str.split(',')) <= 3: | |
| return symptom_list_str.capitalize() | |
| else: # Otherwise, just the first symptom | |
| first_symptom = symptom_list_str.split(',')[0].strip() | |
| return first_symptom.capitalize() | |
| return "Unknown" |