Spaces:
Running
Running
| # 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) |