import pickle
from sklearn.feature_extraction.text import CountVectorizer
import gradio as gr

# Load Naive Bayes model and vectorizer
with open("naive_bayes_model.pkl", "rb") as model_file:
    clf = pickle.load(model_file)

with open("vectorizer.pkl", "rb") as vectorizer_file:
    vectorizer = pickle.load(vectorizer_file)

# Define responses dictionary
responses = {
    "activate_my_card": "To activate your card, log in to the app and navigate to the 'Activate Card' section.",
    "age_limit": "The minimum age to use this service is 18 years.",
    "apple_pay_or_google_pay": "Yes, you can use both Apple Pay and Google Pay with your card.",
    # Add remaining responses here...
}

# Define chatbot response logic
def chatbot_response(user_input):
    vectorized_input = vectorizer.transform([user_input])
    predicted_label = clf.predict(vectorized_input)[0]
    return responses.get(predicted_label, "Sorry, I couldn't understand your question.")

# Define UI description with sample questions
description = """
### FinTech Chatbot

This chatbot can assist you with a variety of fintech-related queries. Below are some sample questions you can ask:

- How do I activate my card?
- What is the minimum age required to use this service?
- Can I use Apple Pay with my card?
- Which ATMs can I use my card with?
- How can I set up automatic top-up for my account?
- Why hasn't my balance updated after making a bank transfer?
- Why can’t I add a beneficiary to my account?
- Can I cancel a transfer I’ve made?
- What should I do if my card is about to expire?
- How long does it take for a new card to arrive?
- My card has been stolen. What should I do?
- Why hasn’t the recipient received my transfer?
"""

# Define the Gradio interface
interface = gr.Interface(
    fn=chatbot_response,
    inputs=gr.Textbox(lines=2, placeholder="Ask your fintech question here..."),
    outputs="text",
    title="FinTech Chatbot",
    description=description
)

if __name__ == "__main__":
    # Launch the Gradio app
    interface.launch()