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# from transformers import pipeline
# insurance_context = """
# Insurance is a contract, represented by a policy, in which an individual or entity receives financial protection against losses.
# Common types include life insurance, health insurance, auto insurance, and home insurance.
# Life insurance provides a sum of money to beneficiaries upon the insured's death, while health insurance covers medical expenses.
# Auto insurance offers protection against vehicle-related accidents and damages. Home insurance covers damages to one’s property.
# """
# qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
# def ask_question(question, context=insurance_context):
# # Use the model to answer the question based on the context
# response = qa_pipeline({
# 'question': question,
# 'context': context
# })
# return response['answer']
# def chat():
# print("Hello! I'm your insurance Q&A chatbot. Ask me anything about insurance.")
# while True:
# user_input = input("You: ")
# if user_input.lower() in ["exit", "quit"]:
# print("Thank you for using the chatbot. Goodbye!")
# break
# answer = ask_question(user_input)
# print("Bot:", answer)
# chat()
# import streamlit as st
# from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
# # Load BlenderBot model and tokenizer
# model_name = "facebook/blenderbot-400M-distill"
# tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
# model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
# # Function to generate a response from BlenderBot
# def get_blenderbot_response(input_text):
# inputs = tokenizer(input_text, return_tensors="pt")
# reply_ids = model.generate(**inputs)
# response = tokenizer.decode(reply_ids[0], skip_special_tokens=True)
# return response
# # Streamlit app
# st.title("Insurance Q&A Chatbot")
# st.write("Ask any question about insurance, and I'll do my best to help!")
# # Chat history
# if "history" not in st.session_state:
# st.session_state.history = []
# # Input text box for user
# user_input = st.text_input("You:", "")
# # Respond to user input
# if user_input:
# # Add user question to history
# st.session_state.history.append({"user": user_input})
# # Generate bot response
# response = get_blenderbot_response(user_input)
# st.session_state.history.append({"bot": response})
# # Display chat history
# for message in st.session_state.history:
# if "user" in message:
# st.write("**You:**", message["user"])
# if "bot" in message:
# st.write("**Bot:**", message["bot"])
import streamlit as st
from rasa.core.agent import Agent
from rasa.shared.core.domain import Domain
from rasa.shared.core.tracker_store import InMemoryTrackerStore
from rasa.shared.nlu.interpreter import RasaNLUInterpreter
# Load Rasa model
domain = Domain.load("insurance_domain.yml")
interpreter = RasaNLUInterpreter("insurance_nlu.pkl")
tracker_store = InMemoryTrackerStore(domain)
agent = Agent.load("insurance_model", interpreter=interpreter, tracker_store=tracker_store)
# Define Streamlit app
st.title("Insurance Chatbot")
user_input = st.text_area("You:", height=200)
if st.button("Send"):
response = agent.handle_text(user_input)
st.text_area("Bot:", value=response[0].text, height=200, max_chars=None, key=None) |