| import os |
| import pandas as pd |
| from rapidfuzz import process, fuzz |
|
|
| |
| |
| |
|
|
| BASE_DIR = os.path.dirname( |
| os.path.dirname( |
| os.path.abspath(__file__) |
| ) |
| ) |
|
|
| PROVERB_FILE = os.path.join( |
| BASE_DIR, |
| "data", |
| "proverbs.csv" |
| ) |
|
|
| |
| |
| |
|
|
| def load_proverbs(): |
|
|
| try: |
|
|
| df = pd.read_csv( |
| PROVERB_FILE, |
| encoding="utf-8" |
| ) |
|
|
| df.columns = ( |
| df.columns |
| .str.strip() |
| ) |
|
|
| df.fillna("", inplace=True) |
|
|
| print("Proverbs loaded successfully") |
| print("Columns:", df.columns.tolist()) |
| print("Records:", len(df)) |
|
|
| return df |
|
|
| except Exception as e: |
|
|
| print( |
| f"Proverb loading error: {e}" |
| ) |
|
|
| return pd.DataFrame() |
|
|
| |
| |
| |
|
|
| proverb_df = load_proverbs() |
|
|
| |
| |
| |
|
|
| def search_proverb(text, df=None): |
|
|
| if df is None: |
| df = proverb_df |
|
|
| if df.empty: |
| return "Proverb database unavailable." |
|
|
| if not text: |
| return "Please enter a proverb or keyword." |
|
|
| query = text.strip() |
|
|
| if not query: |
| return "Please enter a proverb or keyword." |
|
|
| |
| |
| |
|
|
| required_columns = ["Kiembu", "English"] |
|
|
| for col in required_columns: |
|
|
| if col not in df.columns: |
|
|
| return ( |
| f"Missing column '{col}' " |
| f"in proverbs.csv" |
| ) |
|
|
| |
| |
| |
|
|
| exact_match = df[ |
| df["Kiembu"] |
| .astype(str) |
| .str.lower() |
| .str.strip() |
| == query.lower() |
| ] |
|
|
| if not exact_match.empty: |
|
|
| proverb = exact_match.iloc[0]["Kiembu"] |
| meaning = exact_match.iloc[0]["English"] |
|
|
| return f""" |
| ### Proverb Found |
| |
| **Kiembu** |
| |
| {proverb} |
| |
| **English Meaning** |
| |
| {meaning} |
| """ |
|
|
| |
| |
| |
|
|
| choices = ( |
| df["Kiembu"] |
| .astype(str) |
| .tolist() |
| ) |
|
|
| best_match = process.extractOne( |
| query, |
| choices, |
| scorer=fuzz.WRatio |
| ) |
|
|
| if not best_match: |
|
|
| return "No proverb found." |
|
|
| proverb = best_match[0] |
| score = best_match[1] |
|
|
| if score < 70: |
|
|
| return ( |
| "No close proverb found.\n\n" |
| f"Best similarity score: {score}" |
| ) |
|
|
| row = df[ |
| df["Kiembu"] |
| .astype(str) |
| == proverb |
| ] |
|
|
| if row.empty: |
| return "No proverb found." |
|
|
| meaning = row.iloc[0]["English"] |
|
|
| return f""" |
| ### Suggested Proverb |
| |
| **Kiembu** |
| |
| {proverb} |
| |
| **English Meaning** |
| |
| {meaning} |
| """ |
