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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' | |
| #Loading the model | |
| def load_llm(): | |
| llm = CTransformers( | |
| model= "TheBloke/Llama-2-7B-Chat-GGML", | |
| max_new_tokens=512, | |
| temperature=0.5 | |
| ) | |
| return llm | |
| st.image("https://huggingface.co/spaces/wiwaaw/summary/resolve/main/banner.png") | |
| st.title("Chat with CSV using Llama2") | |
| uploaded_file = st.sidebar.file_uploader("Upload your data", type="csv") | |
| if uploaded_file is not None: | |
| 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': ',', # default value | |
| }) | |
| data = loader.load() | |
| 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 to 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") |