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| import time | |
| import dotenv | |
| import streamlit as st | |
| from langchain_core.messages import HumanMessage, AIMessage | |
| from dotenv import load_dotenv | |
| from langchain_core.prompts import PromptTemplate | |
| from langchain.chains import RetrievalQA | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_community.vectorstores import Pinecone | |
| import pinecone | |
| from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_core.prompts import PromptTemplate | |
| from langchain_community.llms import CTransformers | |
| load_dotenv() | |
| st.set_page_config(page_title= "Medical chatbot", page_icon=":bot:") | |
| if "chat_history" not in st.session_state: | |
| st.session_state.chat_history = [] | |
| PINECONE_API_KEY = "1bae0d8e-019e-4e87-8080-ecf523e5f25f" | |
| def get_response(user_query): | |
| # Initilize the prompt | |
| # create prompt template, integrate chatHistory component as well | |
| prompt_template = """ | |
| Use the following pieces of information to answer the user's question. | |
| If you don't know the answer, just say that you don't know, don't try to make up an answer. | |
| Context: {context} | |
| Question: {question} | |
| Only return the helpful answer below nothing else. | |
| Helpful Answer: | |
| """ | |
| PROMPT = PromptTemplate(template = prompt_template, input_variables=["context", "question"]) | |
| chain_type_kwargs = {"prompt":PROMPT} | |
| llm = CTransformers(model="TheBloke/Llama-2-7B-Chat-GGML", model_type="llama", config={'max_new_tokens': 1024, 'temperature': 1}) | |
| index_name = "medical-chatbot" | |
| index=pinecone.Index(api_key=PINECONE_API_KEY, host="https://medical-chatbot-pv4ded8.svc.aped-4627-b74a.pinecone.io") | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| # Create Pinecone retriever | |
| vector_store = Pinecone(index, embeddings, text_key="text") | |
| qa = RetrievalQA.from_chain_type(llm, chain_type="stuff",retriever = vector_store.as_retriever(search_kwargs={"k": 2}), chain_type_kwargs=chain_type_kwargs) | |
| answer = qa.invoke({"query":user_query, "context": st.session_state.chat_history}) | |
| return answer | |
| # answer = vector_store.similarity_search(user_query, k=3) | |
| # return answer.stream().get("answer") | |
| # Sidebar content | |
| with st.sidebar: | |
| st.title('🤖 Medical Chatbot Project') | |
| st.markdown(""" | |
| Welcome to our Medical Chatbot project, an AI-powered application designed to assist users with their medical queries through intelligent interactions. | |
| """) | |
| st.header('⚠️ Disclaimer:') | |
| st.markdown(""" | |
| **Note**: This chatbot provides general medical info and is not a substitute for professional medical advice. Always consult your doctor for any health concerns. Do not delay seeking medical advice because of information from this app. | |
| """) | |
| st.header('🔑 Key Features:') | |
| st.markdown(""" | |
| - 👉**Custom LLM Model**: Utilizes a custom Large Language Model hosted on Hugging Face for tailored medical responses. | |
| - 👉**Hugging Face Integration**: Secure access using Hugging Face credentials for model authentication. | |
| - 👉**Streamlit Interface**: User-friendly and intuitive interface built with Streamlit. | |
| - 👉**LangChain for Prompt Management**: Ensures precise and contextually appropriate responses by managing prompts and chat history. | |
| - 👉**Pinecone for Vector Storage**: Efficient vector storage and retrieval for quick access to relevant information. | |
| """) | |
| st.header('🚀 Future Enhancements:') | |
| st.markdown(""" | |
| - **Real-time Model Loading**: On-demand model loading with progress indicators. | |
| - **Enhanced Medical Knowledge Base**: Continuous updates to keep the model current with the latest medical information. | |
| """) | |
| st.header(':building_construction: View Other projects') | |
| st.markdown(""" | |
| - 👉Github: https://github.com/Aniket2021448/ | |
| - 👉Portfolio: https://aniket2021448.github.io/My-portfolio/ | |
| """) | |
| st.header('🤗💬 Contact the developer') | |
| st.markdown(""" | |
| - 👉Email: aniketpanchal1257@gmail.com | |
| - 👉Portfolio: https://aniket2021448.github.io/My-portfolio/ | |
| """) | |
| st.title("Medical chatbot 🤖") | |
| st.write("Welcome to the medical chatbot. Please enter your symptoms below and I will try to help you.") | |
| st.write("It works, Just for a while, Because of free tier specifications It is slow.") | |
| st.write("Thanks for your time :sparkling_heart:") | |
| if "messages" not in st.session_state.keys(): | |
| st.session_state.chat_history.append({"bot": "How may I help you?"}) | |
| if "chat_history" in st.session_state: | |
| for message in st.session_state.chat_history: | |
| if "bot" in message: | |
| with st.chat_message("AI"): | |
| st.markdown(message["bot"]) | |
| st.session_state.chat_history = [] | |
| if "chat_history" in st.session_state: | |
| for message in st.session_state.chat_history: | |
| if "user" in message: | |
| with st.chat_message("Human"): | |
| st.markdown(message["user"]) | |
| elif "bot" in message: | |
| with st.chat_message("AI"): | |
| st.markdown(message["bot"]) | |
| user_query = st.chat_input("Enter your symptoms here") | |
| if user_query is not None and user_query != "": | |
| with st.chat_message("Human"): | |
| st.markdown(user_query) | |
| st.session_state.chat_history.append({"user": user_query}) | |
| with st.chat_message("AI"): | |
| with st.spinner("Thinking..."): | |
| ai_response = get_response(user_query) | |
| result = ai_response["result"] | |
| st.markdown(result) | |
| st.session_state.chat_history.append({"bot": result}) | |
| # import os | |
| # import time | |
| # import dotenv | |
| # import streamlit as st | |
| # from dotenv import load_dotenv | |
| # from langchain import PromptTemplate | |
| # from langchain.chains import RetrievalQA | |
| # from langchain.embeddings import HuggingFaceEmbeddings | |
| # from langchain.vectorstores import Pinecone | |
| # import pinecone | |
| # from langchain.llms import CTransformers | |
| # # Load environment variables | |
| # load_dotenv() | |
| # # Initialize Streamlit page config | |
| # st.set_page_config(page_title="Medical Chatbot", page_icon=":bot:") | |
| # # Initialize chat history in session state | |
| # if "chat_history" not in st.session_state: | |
| # st.session_state.chat_history = [] | |
| # PINECONE_API_KEY = os.getenv("PINECONE_API_KEY") | |
| # HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN") | |
| # # Cache models and vector store initialization | |
| # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| # @st.cache_resource | |
| # def initialize_models(): | |
| # # Load language model | |
| # llm = CTransformers(model="model/llama-2-7b-chat.ggmlv3.q4_0.bin", model_type="llama", config={'max_new_tokens': 1024, 'temperature': 1}) | |
| # # Initialize Pinecone index | |
| # index = pinecone.Index(api_key=PINECONE_API_KEY, host="https://medical-chatbot-pv4ded8.svc.aped-4627-b74a.pinecone.io") | |
| # # Initialize embeddings | |
| # # Create Pinecone retriever | |
| # vector_store = Pinecone(index, embeddings, text_key="text") | |
| # return llm, vector_store | |
| # llm, vector_store = initialize_models() | |
| # # Define prompt template | |
| # prompt_template = """ | |
| # Use the following pieces of information to answer the user's question. | |
| # If you don't know the answer, just say that I don't know, don't try to make up an answer. | |
| # Context: {context} | |
| # Question: {question} | |
| # Only return the helpful answer below nothing else. | |
| # Helpful Answer: | |
| # """ | |
| # PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) | |
| # # Cache QA chain initialization | |
| # @st.cache_resource | |
| # def _initialize_qa(_llm, _vector_store): | |
| # return RetrievalQA.from_chain_type( | |
| # _llm, | |
| # chain_type="stuff", | |
| # retriever=_vector_store.as_retriever(search_kwargs={"k": 2}), | |
| # chain_type_kwargs={"prompt": PROMPT} | |
| # ) | |
| # qa = _initialize_qa(llm, vector_store) | |
| # def get_response(user_query): | |
| # # chat_context = "\n".join([f"User: {msg['user']}" if 'user' in msg else f"Bot: {msg['bot']}" for msg in st.session_state.chat_history]) | |
| # answer = qa.invoke({"query": user_query, "context": st.session_state.chat_history}) | |
| # return answer | |
| # # Function to simulate typing effect | |
| # # def type_effect(text): | |
| # # for char in text: | |
| # # st.write(char, end="") | |
| # # time.sleep(0.05) | |
| # # st.write("") | |
| # # Streamlit UI | |
| # st.title("Medical Chatbot") | |
| # st.write("Welcome to the medical chatbot. Please enter your symptoms below and I will try to help you.") | |
| # # Display chat history | |
| # for message in st.session_state.chat_history: | |
| # if "user" in message: | |
| # with st.chat_message("Human"): | |
| # st.markdown(message["user"]) | |
| # elif "bot" in message: | |
| # with st.chat_message("AI"): | |
| # st.markdown(message["bot"]) | |
| # # Chat input and response handling | |
| # user_query = st.chat_input("Enter your symptoms here") | |
| # if user_query: | |
| # with st.chat_message("Human"): | |
| # st.markdown(user_query) | |
| # st.session_state.chat_history.append({"user": user_query}) | |
| # with st.chat_message("AI"): | |
| # ai_response = get_response(user_query) | |
| # result = ai_response["result"] | |
| # st.markdown(result) | |
| # st.session_state.chat_history.append({"bot": result}) | |