Delete streamlit_base.py

streamlit_base.py

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-import langchain
-from langchain.embeddings.openai import OpenAIEmbeddings
-# from langchain.vectorstores import Chroma
-from langchain.vectorstores import FAISS
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.chains import VectorDBQA
-from langchain.chains import RetrievalQA
-from langchain.document_loaders import DirectoryLoader
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-from langchain.evaluation.qa import QAGenerateChain
-import magic
-import os
-import streamlit as st
-from streamlit_chat import message
-
-st.title("AI Chatbot")
-
-if 'responses' not in st.session_state:
-    st.session_state['responses'] = ["How can I assist you?"]
-
-if 'requests' not in st.session_state:
-    st.session_state['requests'] = []
-
-openai_api_key = os.getenv("OPENAI_API_KEY", "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")    
-embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-new_db = FAISS.load_local("faiss_index_diagnostics_RCV", embeddings)
-llm = OpenAI(openai_api_key=openai_api_key, temperature=0.0)
-
-# if 'buffer_memory' not in st.session_state:
-memory= ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-retriever = new_db.as_retriever()
-chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type="stuff", memory= memory,retriever=retriever, verbose=False)
-
-# container for chat history
-response_container = st.container()
-# container for text box
-textcontainer = st.container()
-
-
-with textcontainer:
-    query = st.text_input(label="Please Enter Your Prompt Here: ", placeholder="Ask me")
-    if query:
-        with st.spinner("Generating..."):
-            # conversation_string = get_conversation_string()
-            # st.code(conversation_string)
-            # refined_query = query_refiner(conversation_string, query)
-            # st.subheader("Refined Query:")
-            # st.write(refined_query)
-            # context = find_match(refined_query)
-            # print(context)  
-            response = chain.run(query)
-        st.session_state.requests.append(query)
-        st.session_state.responses.append(response) 
-with response_container:
-    if st.session_state['responses']:
-
-        for i in range(len(st.session_state['responses'])):
-            message(st.session_state['responses'][i],key=str(i))
-            if i < len(st.session_state['requests']):
-                message(st.session_state["requests"][i], is_user=True,key=str(i)+ '_user')
-
-# with st.expander('Message history'):
-#     st.info(memory.buffer)
-
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