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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.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.llms import CTransformers | |
| from langchain.chains import ConversationalRetrievalChain | |
| DB_FAISS_PATH = 'vectorstore/db_faiss' | |
| background_image_path = 'image1.jpg' | |
| # Set the background image and color | |
| #Loading the model | |
| def load_llm(): | |
| # Load the locally downloaded model here | |
| llm = CTransformers(model='TheBloke/Llama-2-7B-Chat-GGML',model_file='llama-2-7b-chat.ggmlv3.q8_0.bin',max_new_tokens=512,temperature=0.1,gpu_layers=50) | |
| return llm | |
| st.title("☔ ☔Chat with CSV using Llama2 ☔ ☔") | |
| st.markdown("<h3 style='text-align: center; color: white;'>Built by <a href=https://github.com/Sakil786/CSVQConnect>♻️ CSVQConnect GitHub ♻️ </a></h3>", unsafe_allow_html=True) | |
| # Your background image URL goes here | |
| #background_image_url = 'https://www.bing.com/images/search?view=detailV2&ccid=lFAWXtbv&id=BB57AC3541361FF3844CAA706B667014CB515B92&thid=OIP.lFAWXtbvpchf66BryfJQ1QHaE8&mediaurl=https%3a%2f%2fimage.freepik.com%2ffree-photo%2ftwo-llamas-andean-highland-bolivia_107467-2006.jpg&exph=418&expw=626&q=llama2+image&simid=608011097331751957&FORM=IRPRST&ck=F66D65F1AFAAA4BBCF9986ADF8ED1643&selectedIndex=4' | |
| background_image_path = 'image1.jpg' | |
| # Set the background image and color | |
| 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') | |
| 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 ! What is your query 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="Search answer from 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") |