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 from ctransformers import AutoModelForCausalLM from langchain_g4f import G4FLLM from g4f import Provider, models # import spacy import requests # Define the path for generated embeddings DB_FAISS_PATH = 'vectorstore/db_faiss' # Load the model of choice def load_llm(): # url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin" # 2.87G # model_loc, file_size = dl_hf_model(url) # llm = CTransformers( # model=model_loc, # temperature=0.2, # model_type="llama", # top_k=10, # top_p=0.9, # repetition_penalty=1.0, # max_new_tokens=512, # adjust as needed # seed=42, # reset=True, # reset history (cache) # stream=False, # # threads=cpu_count, # # stop=prompt_prefix[1:2], # ) llm = G4FLLM( model=models.gpt_35_turbo, provider=Provider.DeepAi, ) return llm hide_streamlit_style = """ """ st.markdown(hide_streamlit_style, unsafe_allow_html=True) # Set the title for the Streamlit app st.title("Zendo美女チャットボックス") csv_url = "https://huggingface.co/spaces/uyen13/chatgirl/raw/main/testchatdata.csv" # csv_url="https://docs.google.com/uc?export=download&id=1fQ2v2n9zQcoi6JoOU3lCBDHRt3a1PmaE" # Define the path where you want to save the downloaded file tmp_file_path = "testchatdata.csv" # Download the CSV file response = requests.get(csv_url) if response.status_code == 200: with open(tmp_file_path, 'wb') as file: file.write(response.content) else: raise Exception(f"Failed to download the CSV file from {csv_url}") # Load CSV data using CSVLoader loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','}) data = loader.load() # Create embeddings using Sentence Transformers embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'}) # Create a FAISS vector store and save embeddings db = FAISS.from_documents(data, embeddings) db.save_local(DB_FAISS_PATH) # Load the language model llm = load_llm() # Create a conversational chain chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever()) # Initialize spaCy with the Japanese model # nlp = spacy.load("ja_core_news_sm") # Function for conversational chat 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"] # Initialize chat history if 'history' not in st.session_state: st.session_state['history'] = [] # Initialize messages if 'generated' not in st.session_state: st.session_state['generated'] = ["こんにちは!zendo美女です。何かお探しですか?... 🤗"] if 'past' not in st.session_state: st.session_state['past'] = ["チャットはここから"] # Create containers for chat history and user input response_container = st.container() container = st.container() # User input form with container: with st.form(key='my_form', clear_on_submit=True): user_input = st.text_input("ChatBox", placeholder="質問をご記入ください... ", 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) # Display chat history 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")