Upload app.py

app.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationChain
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain.prompts import (
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+    ChatPromptTemplate,
+    MessagesPlaceholder
+)
+import streamlit as st
+from streamlit_chat import message
+from utils import *
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from the .env file
+load_dotenv()
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+if OPENAI_API_KEY is None:
+    raise ValueError("OpenAI API key is not found in the .env file")
+st.subheader("Insightly 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'] = []
+
+llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=OPENAI_API_KEY)
+
+if 'buffer_memory' not in st.session_state:
+            st.session_state.buffer_memory=ConversationBufferWindowMemory(k=3,return_messages=True)
+
+
+system_msg_template = SystemMessagePromptTemplate.from_template(template="""Answer the question as truthfully as possible using the provided context, 
+and if the answer is not contained within the text below, say 'I don't know'""")
+
+
+human_msg_template = HumanMessagePromptTemplate.from_template(template="{input}")
+
+prompt_template = ChatPromptTemplate.from_messages([system_msg_template, MessagesPlaceholder(variable_name="history"), human_msg_template])
+
+conversation = ConversationChain(memory=st.session_state.buffer_memory, prompt=prompt_template, llm=llm, verbose=True)
+
+
+
+
+# container for chat history
+response_container = st.container()
+# container for text box
+textcontainer = st.container()
+
+
+with textcontainer:
+    query = st.text_input("Query: ", key="input")
+    if query:
+        with st.spinner("typing..."):
+            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 = conversation.predict(input=f"Context:\n {context} \n\n Query:\n{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')