import tkinter as tk
import openai
import pandas as pd
import random
import os
from openpyxl import load_workbook

# Your OpenAI API Key
openai.api_key = ''

# Initialize the questions dataframe
if os.path.isfile('C:/Users/User/Desktop/Venus/customer_questions.xlsx'):
    df_questions = pd.read_excel('C:/Users/User/Desktop/Venus/customer_questions.xlsx')
else:
    df_questions = pd.DataFrame(columns=['customer_id', 'question'])

# Define your product database
db_products = pd.DataFrame({
    'product_id': ['001', '002', '003'],
    'name': ['Jeans', 'Shirt', 'Jacket'],
    'size': ['M', 'S', 'L'],
    'color': ['Blue', 'Red', 'Black'],
    'material': ['Denim', 'Cotton', 'Leather'],
    'shipping': ['Free shipping', '$3 shipping', '$5 shipping'],
})

# Function to convert your product database to text
def db_to_text():
    text_db = ""
    for i, row in db_products.iterrows():
        text_db += f"Product ID: {row['product_id']}\n"
        text_db += f"Name: {row['name']}\n"
        text_db += f"Size: {row['size']}\n"
        text_db += f"Color: {row['color']}\n"
        text_db += f"Material: {row['material']}\n"
        text_db += f"Shipping: {row['shipping']}\n\n"
    return text_db

# Function to get an answer from GPT-3.5
# Function to get an answer from GPT-3.5
def get_answer(question, db_text, previous_questions):
    # Start with a system message
    messages = [
        {"role": "system", "content": "You are a knowledgeable assistant with access to the following product database:\n" + db_text},
        {"role": "system", "content": "The assistant should provide useful information based on the user's most recent question, using the previous questions for context."},
    ]

    # Add previous user questions
    for prev_question in previous_questions:
        messages.append({"role": "user", "content": prev_question})

    # Add current question last
    messages.append({"role": "user", "content": question})

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=messages
    )

    return response['choices'][0]['message']['content']


# Function to ask a question
def ask_question():
    global customer_id
    global previous_questions
    global df_questions
    
    question = question_entry.get()
    question_entry.delete(0, 'end')

    df_questions = pd.concat([df_questions, pd.DataFrame({'customer_id': [customer_id], 'question': [question]})], ignore_index=True)
    df_questions.to_excel('C:/Users/User/Desktop/Venus/customer_questions.xlsx', index=False)

    db_text = db_to_text()
    answer = get_answer(question, db_text, previous_questions)
    answer_label['text'] = answer
    previous_questions.append(question)

# Function to create a new customer ID
def new_customer():
    global customer_id
    global previous_questions

    customer_id = random.randint(1, 1000)
    previous_questions = []

# Create a root window
root = tk.Tk()

# Create the question entry field
question_entry = tk.Entry(root, width=100)
question_entry.pack()

# Create the ask question button
ask_button = tk.Button(root, text='Ask Question', command=ask_question)
ask_button.pack()

# Create the answer label
answer_label = tk.Label(root, text='', wraplength=500)  # wraplength is in pixels
answer_label.pack()

# Create the new customer button
new_customer_button = tk.Button(root, text='New Customer', command=new_customer)
new_customer_button.pack()

# Initialize a new customer
new_customer()

# Run the tkinter event loop
root.mainloop()