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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("Welcome to BhubBot")
+
+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_leave_policy_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("Cooking..."):
+            # 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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