word_finder.py

import os
import pandas as pd
from fuzzywuzzy import process
from openai import OpenAI
from dotenv import load_dotenv

# Load environment variables (OpenAI key)
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

# Initialize OpenAI client
client = OpenAI(api_key=openai_api_key)

# Load word list
word_df = pd.read_csv('spelling_words.csv', header=None)
word_list = word_df[0].tolist()

# Fuzzy search function
def search_word(user_input, word_list, top_n=5):
    matches = process.extract(user_input, word_list, limit=top_n)
    return matches

# Function for AI-powered responses (definitions, origins, etc.)
def get_ai_response(query):
    response = client.chat.completions.create(
        model="gpt-4-turbo",
        messages=[
            {"role": "system", "content": "You are an expert spelling bee coach. Provide clear definitions, word origins, usage examples, and helpful memory tips for kids."},
            {"role": "user", "content": query}
        ],
        temperature=0.3,
        max_tokens=200
    )
    return response.choices[0].message.content.strip()

# Main interaction loop
if __name__ == "__main__":
    print("🎉 Welcome to Spelling Bee Word Finder with AI! 🎉")
    while True:
        query = input("\nEnter a word or ask a question ('define ubiquitous', 'origin onomatopoeia') or type 'exit' to quit: ").strip()
        if query.lower() == "exit":
            print("Goodbye! Keep practicing 😊")
            break
        
        # Check if the query is asking for a definition or origin
        if query.lower().startswith(("define", "meaning", "origin", "use", "sentence", "explain")):
            print("\n🧠 AI Response:")
            print(get_ai_response(query))
        else:
            # Default fuzzy search for spelling matches
            results = search_word(query, word_list)
            print("\n📌 Top Matches (Fuzzy Search):")
            for match, score in results:
                print(f"{match} (Confidence: {score}%)")