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| from transformers import pipeline | |
| import pandas as pd | |
| qa_pipeline = pipeline("question-answering", model='deepset/roberta-base-squad2') | |
| def chatbot(question): | |
| with open(r"ayurdata.txt", "r", encoding="utf-8") as file: | |
| context = file.read() | |
| answer = qa_pipeline(question=question, context=context) | |
| return answer | |
| def prints(questions): | |
| response = chatbot(questions) | |
| return response['answer'] | |
| import streamlit as st | |
| st.title("Ford Assistant AI Bot") | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| st.session_state.messages.append({ | |
| 'role':'assistant', | |
| 'content':"Hi! I'm your AI Bot" | |
| }) | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| prompt = st.chat_input("What is up?") | |
| if prompt: | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| st.session_state.messages.append({"role":"user","content":prompt}) | |
| response = f"ChatBot: {prints(prompt)}" | |
| with st.chat_message("assistant"): | |
| st.markdown(response) | |
| st.session_state.messages.append({"role":"assistant","content":response}) |