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| import streamlit as st | |
| from streamlit_chat import message | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.llms import Replicate | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.document_loaders import PyPDFLoader | |
| from langchain.document_loaders import TextLoader | |
| from langchain.document_loaders import Docx2txtLoader | |
| from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| import os | |
| from dotenv import load_dotenv | |
| import tempfile | |
| load_dotenv() | |
| def initialize_session_state(): | |
| 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 about your file"] | |
| if 'past' not in st.session_state: | |
| st.session_state['past'] = ["Hey! 👋"] | |
| def conversation_chat(query, chain, history): | |
| result = chain({"question": query, "chat_history": history}) | |
| history.append((query, result["answer"])) | |
| return result["answer"] | |
| def display_chat_history(chain): | |
| reply_container = st.container() | |
| container = st.container() | |
| with container: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| with st.form(key='my_form', clear_on_submit=True): | |
| user_input = st.text_input("Question:", placeholder="Ask about your Documents", key='input') | |
| submit_button = st.form_submit_button(label='Send') | |
| with col2: | |
| if st.session_state['generated']: | |
| for i in range(len(st.session_state['generated'])): | |
| message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="thumbs") | |
| message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji") | |
| def create_conversational_chain(vector_store): | |
| load_dotenv() | |
| llm = Replicate( | |
| streaming=True, | |
| model="replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781", | |
| callbacks=[StreamingStdOutCallbackHandler()], | |
| input={"temperature": 0.01, "max_length": 500, "top_p": 1}) | |
| memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff', | |
| retriever=vector_store.as_retriever(search_kwargs={"k": 2}), | |
| memory=memory) | |
| return chain | |
| def main(): | |
| load_dotenv() | |
| initialize_session_state() | |
| st.title("ChatBot ") | |
| st.sidebar.title("Document Processing") | |
| uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True) | |
| if uploaded_files: | |
| text = [] | |
| for file in uploaded_files: | |
| file_extension = os.path.splitext(file.name)[1] | |
| with tempfile.NamedTemporaryFile(delete=False) as temp_file: | |
| temp_file.write(file.read()) | |
| temp_file_path = temp_file.name | |
| loader = None | |
| if file_extension == ".pdf": | |
| loader = PyPDFLoader(temp_file_path) | |
| elif file_extension == ".docx" or file_extension == ".doc": | |
| loader = Docx2txtLoader(temp_file_path) | |
| elif file_extension == ".txt": | |
| loader = TextLoader(temp_file_path) | |
| if loader: | |
| text.extend(loader.load()) | |
| os.remove(temp_file_path) | |
| text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len) | |
| text_chunks = text_splitter.split_documents(text) | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", | |
| model_kwargs={'device': 'cpu'}) | |
| vector_store = FAISS.from_documents(text_chunks, embedding=embeddings) | |
| chain = create_conversational_chain(vector_store) | |
| display_chat_history(chain) | |
| if __name__ == "__main__": | |
| main() | |