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| # import streamlit as st | |
| # from streamlit_chat import message | |
| # import tempfile | |
| # from langchain.document_loaders.csv_loader import CSVLoader | |
| # from langchain_community.embeddings import HuggingFaceEmbeddings | |
| # from langchain_community.vectorstores import FAISS | |
| # from langchain_community.llms import CTransformers | |
| # from langchain.chains import ConversationalRetrievalChain | |
| # DB_FAISS_PATH = "vectorstore/db_faiss" | |
| # def load_llm(): | |
| # llm = CTransformers( | |
| # model="llama-2-7b-chat.ggmlv3.q2_K.bin", | |
| # model_type="llama", | |
| # max_new_tokens=512, | |
| # temperature=0.5, | |
| # ) | |
| # return llm | |
| # st.title("Chat with your CSV") | |
| # st.markdown( | |
| # "<h3 style='text-align: center; color: white;>Built by Viswanath S R</h3>", | |
| # unsafe_allow_html=True, | |
| # ) | |
| # uploaded_file = st.sidebar._file_uploader("Upload your CSV file", type="csv") | |
| # if uploaded_file: | |
| # with tempfile.NamedTemporaryFile(delete=False) as tmp_file: | |
| # tmp_file.write(uploaded_file.getvalue()) | |
| # tmp_file_path = tmp_file.name | |
| # loader = CSVLoader( | |
| # file_path=tmp_file_path, encoding="utf-8", csv_args={"delimiter": ","} | |
| # ) | |
| # data = loader.load() | |
| # # st.json(data) | |
| # embeddings = HuggingFaceEmbeddings( | |
| # model_name="sentence_transformers/all-MiniLM-L6-v2", | |
| # model_kwargs={"device": "cpu"}, | |
| # ) | |
| # db = FAISS.from_documents(data, embeddings) | |
| # db.save_local(DB_FAISS_PATH) | |
| # llm = load_llm() | |
| # chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever()) | |
| # def conversational_chat(query): | |
| # result = chain({"question": query, "chat_history": st.session_state["history"]}) | |
| # st.session_state["history"].append({query, result["answer"]}) | |
| # return result["answer"] | |
| # if "history" not in st.session_state: | |
| # st.session_state["history"] = [] | |
| # if "generated" not in st.session_state: | |
| # st.session_state["generated"] = [ | |
| # "Hello! Ask me anything about" + uploaded_file.name | |
| # ] | |
| # if "past" not in st.session_state: | |
| # st.session_state["past"] = ["Hey!"] | |
| # response_container = st.container() | |
| # container = st.container() | |
| # with container: | |
| # with st.form(key="my_form", clear_on_submit=True): | |
| # user_input = st.text_input( | |
| # "Query : ", placeholder="Talk with your data", key="input" | |
| # ) | |
| # submit_button = st.form_submit_button(label="Chat") | |
| # if submit_button and user_input: | |
| # output = conversational_chat(user_input) | |
| # st.session_state["past"].append(user_input) | |
| # st.session_state["generated"].append(output) | |
| # if st.session_state["generated"]: | |
| # with response_container: | |
| # for i in range(len(st.session_state["generated"])): | |
| # message( | |
| # st.session_state["past"][i], | |
| # is_user=True, | |
| # key=str(i) + "_user", | |
| # avatar_style="big-smile", | |
| # ) | |
| # message( | |
| # st.session_state["generated"][i], key=str(i), avatar_style="thumbs" | |
| # ) | |
| # import streamlit as st | |
| # from streamlit_chat import message | |
| # import tempfile | |
| # from langchain.document_loaders.csv_loader import CSVLoader | |
| # from langchain_community.embeddings import HuggingFaceEmbeddings | |
| # from langchain_community.vectorstores import FAISS | |
| # from langchain_community.llms import CTransformers | |
| # from langchain.chains import ConversationalRetrievalChain | |
| import streamlit as st | |
| from streamlit_chat import message | |
| import tempfile | |
| from langchain.document_loaders.csv_loader import CSVLoader | |
| #from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| #from langchain.vectorstores import FAISS | |
| from langchain_community.vectorstores import FAISS | |
| #from langchain.llms import CTransformers | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain_community.llms import CTransformers | |
| DB_FAISS_PATH = "vectorstore/db_faiss" | |
| # Loading the model | |
| def load_llm(): | |
| # Load the locally downloaded model here | |
| llm = CTransformers( | |
| model="TheBloke/Llama-2-7B-Chat-GGML", | |
| #model_type="llama", | |
| max_new_tokens=512, | |
| temperature=0.5, | |
| ) | |
| return llm | |
| st.title("Chat with CSV Files") | |
| st.markdown( | |
| "<h3 style='text-align: center; color: white;'>Built by B R</h3>", | |
| unsafe_allow_html=True, | |
| ) | |
| uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv") | |
| if uploaded_file: | |
| # use tempfile because CSVLoader only accepts a file_path | |
| with tempfile.NamedTemporaryFile(delete=False) as tmp_file: | |
| tmp_file.write(uploaded_file.getvalue()) | |
| tmp_file_path = tmp_file.name | |
| loader = CSVLoader( | |
| file_path=tmp_file_path, encoding="utf-8", csv_args={"delimiter": ","} | |
| ) | |
| data = loader.load() | |
| # st.json(data) | |
| embeddings = HuggingFaceEmbeddings( | |
| model_name="sentence-transformers/all-MiniLM-L6-v2", | |
| model_kwargs={"device": "cpu"}, | |
| ) | |
| db = FAISS.from_documents(data, embeddings) | |
| db.save_local(DB_FAISS_PATH) | |
| llm = load_llm() | |
| chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever()) | |
| def conversational_chat(query): | |
| result = chain({"question": query, "chat_history": st.session_state["history"]}) | |
| st.session_state["history"].append((query, result["answer"])) | |
| return result["answer"] | |
| if "history" not in st.session_state: | |
| st.session_state["history"] = [] | |
| if "generated" not in st.session_state: | |
| st.session_state["generated"] = [ | |
| "Hello ! Ask me anything about " + uploaded_file.name | |
| ] | |
| if "past" not in st.session_state: | |
| st.session_state["past"] = ["Hey !"] | |
| # container for the chat history | |
| response_container = st.container() | |
| # container for the user's text input | |
| container = st.container() | |
| with container: | |
| with st.form(key="my_form", clear_on_submit=True): | |
| user_input = st.text_input( | |
| "Query:", placeholder="Talk with your csv data here", key="input" | |
| ) | |
| submit_button = st.form_submit_button(label="Send") | |
| if submit_button and user_input: | |
| output = conversational_chat(user_input) | |
| st.session_state["past"].append(user_input) | |
| st.session_state["generated"].append(output) | |
| if st.session_state["generated"]: | |
| with response_container: | |
| for i in range(len(st.session_state["generated"])): | |
| message( | |
| st.session_state["past"][i], | |
| is_user=True, | |
| key=str(i) + "_user", | |
| avatar_style="big-smile", | |
| ) | |
| message( | |
| st.session_state["generated"][i], key=str(i), avatar_style="thumbs" | |
| ) | |