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import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import random
# Initialize GPT-2 model and tokenizer
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
# Define some sample data for the chatbot
products = {
'T-shirt': {'sizes': ['S', 'M', 'L', 'XL'], 'colors': ['Red', 'Blue', 'Green'], 'price': 25},
'Jacket': {'sizes': ['M', 'L', 'XL'], 'colors': ['Black', 'Grey'], 'price': 50},
'Sneakers': {'sizes': ['7', '8', '9', '10'], 'colors': ['White', 'Black'], 'price': 60}
}
# Simulate order tracking
orders = {
'12345': {'status': 'Shipped', 'arrival_date': '2024-12-22'},
'67890': {'status': 'In Transit', 'arrival_date': '2024-12-23'}
}
# Simulate some discounts
discounts = {
'WINTER20': '20% off on winter apparel!',
'WELCOME10': '10% off for first-time customers!'
}
# Function to generate response using GPT-2
def generate_gpt2_response(user_input):
inputs = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Chatbot logic
def chatbot():
st.title("E-Commerce Chatbot 🛍️")
st.write("Hi, I'm your shopping assistant! How can I help you today?")
user_input = st.text_input("You: ", "")
if user_input:
# Handle product queries
if "size" in user_input.lower() or "color" in user_input.lower():
st.write("Sure! Here are the available options:")
for product, details in products.items():
st.write(f"**{product}**: Sizes: {', '.join(details['sizes'])}, Colors: {', '.join(details['colors'])}, Price: ${details['price']}")
# Handle order tracking
elif "track order" in user_input.lower():
order_number = st.text_input("Please enter your order number:", "")
if order_number:
if order_number in orders:
order_info = orders[order_number]
st.write(f"Your order is {order_info['status']}. It will arrive by {order_info['arrival_date']}.")
else:
st.write("Sorry, we couldn't find that order. Please check your order number.")
# Handle product recommendations
elif "recommend" in user_input.lower():
st.write("Here are some products I recommend based on your browsing:")
recommended = random.sample(list(products.keys()), 2)
for product in recommended:
st.write(f"- {product}")
# Handle discounts
elif "discount" in user_input.lower():
discount_code = st.text_input("Enter your discount code:", "")
if discount_code and discount_code in discounts:
st.write(f"Great! You can use the code {discount_code} for: {discounts[discount_code]}")
elif discount_code:
st.write("Sorry, that discount code is invalid.")
# Advanced conversation via GPT-2
else:
gpt2_response = generate_gpt2_response(user_input)
st.write(f"Bot: {gpt2_response}")
else:
st.write("Start by asking about products, tracking your order, or getting discounts!")
# Run the chatbot function
if __name__ == '__main__':
chatbot()
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