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import streamlit as st |
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from streamlit_chat import message |
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import tempfile |
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from langchain.document_loaders.csv_loader import CSVLoader |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.vectorstores import FAISS |
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from langchain.llms import CTransformers |
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from langchain.chains import ConversationalRetrievalChain |
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from dl_hf_model import dl_hf_model |
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from ctransformers import AutoModelForCausalLM |
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from langchain_g4f import G4FLLM |
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from g4f import Provider, models |
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import requests |
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DB_FAISS_PATH = 'vectorstore/db_faiss' |
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def load_llm(): |
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llm = G4FLLM( |
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model=models.gpt_35_turbo, |
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provider=Provider.DeepAi, |
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) |
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return llm |
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hide_streamlit_style = """ |
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<style> |
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#MainMenu {visibility: hidden;} |
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footer {visibility: hidden;} |
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</style> |
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""" |
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st.markdown(hide_streamlit_style, unsafe_allow_html=True) |
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st.title("Coloring Anime ChatBot") |
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csv_url = "https://huggingface.co/spaces/uyen13/chatbot/raw/main/testchatdata.csv" |
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tmp_file_path = "testchatdata.csv" |
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response = requests.get(csv_url) |
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if response.status_code == 200: |
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with open(tmp_file_path, 'wb') as file: |
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file.write(response.content) |
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else: |
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raise Exception(f"Failed to download the CSV file from {csv_url}") |
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loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','}) |
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data = loader.load() |
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embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'}) |
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db = FAISS.from_documents(data, embeddings) |
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db.save_local(DB_FAISS_PATH) |
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llm = load_llm() |
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chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever()) |
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def conversational_chat(query): |
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result = chain({"question": query, "chat_history": st.session_state['history']}) |
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st.session_state['history'].append((query, result["answer"])) |
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return result["answer"] |
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if 'history' not in st.session_state: |
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st.session_state['history'] = [] |
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if 'generated' not in st.session_state: |
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st.session_state['generated'] = ["Hello ! Ask me about this page like coloring book,how to buy ... π€"] |
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if 'past' not in st.session_state: |
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st.session_state['past'] = ["your chat here"] |
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response_container = st.container() |
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container = st.container() |
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with container: |
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with st.form(key='my_form', clear_on_submit=True): |
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user_input = st.text_input("ChatBox", placeholder="Ask anything... ", key='input') |
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submit_button = st.form_submit_button(label='Send') |
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if submit_button and user_input: |
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output = conversational_chat(user_input) |
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st.session_state['past'].append(user_input) |
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st.session_state['generated'].append(output) |
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if st.session_state['generated']: |
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with response_container: |
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for i in range(len(st.session_state['generated'])): |
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message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile") |
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message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs") |