Upload app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 from streamlit_chat import message
-from langchain_openai import ChatOpenAI
+from langchain import OpenAI
 from langchain.chains import ConversationChain
 from langchain.chains.conversation.memory import (ConversationBufferMemory, 
                                                   ConversationSummaryMemory, 
@@ -8,14 +8,12 @@ from langchain.chains.conversation.memory import (ConversationBufferMemory,
                
                                                   )
 
-
-# 3 variables in session_state
 if 'conversation' not in st.session_state:
     st.session_state['conversation'] =None
 if 'messages' not in st.session_state:
     st.session_state['messages'] =[]
 if 'API_Key' not in st.session_state:
-    st.session_state['OPENAI_API_KEY'] =''
+    st.session_state['API_Key'] =''
 
 # Setting page title and header
 st.set_page_config(page_title="Chat GPT Clone", page_icon=":robot_face:")
@@ -23,53 +21,54 @@ st.markdown("<h1 style='text-align: center;'>How can I assist you? </h1>", unsaf
 
 
 st.sidebar.title("😎")
-st.session_state['OPENAI_API_KEY']= st.sidebar.text_input("What's your API key?",type="password")
+st.session_state['API_Key']= st.sidebar.text_input("What's your API key?",type="password")
 summarise_button = st.sidebar.button("Summarise the conversation", key="summarise")
 if summarise_button:
     summarise_placeholder = st.sidebar.write("Nice chatting with you my friend ❤️:\n\n"+st.session_state['conversation'].memory.buffer)
     #summarise_placeholder.write("Nice chatting with you my friend ❤️:\n\n"+st.session_state['conversation'].memory.buffer)
 
+#import os
+#os.environ["OPENAI_API_KEY"] = "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"
 
 def getresponse(userInput, api_key):
 
     if st.session_state['conversation'] is None:
 
-        # create a LLM
-        llm = ChatOpenAI(
+        llm = OpenAI(
             temperature=0,
             openai_api_key=api_key,
-            model_name='gpt-3.5-turbo'
+            model_name='text-davinci-003'  #we can also use 'gpt-3.5-turbo'
         )
 
-        # store the placeholder as "conversation" in session_state
         st.session_state['conversation'] = ConversationChain(
             llm=llm,
             verbose=True,
             memory=ConversationSummaryMemory(llm=llm)
         )
 
-    # call st.session_state['conversation'] to create dialogues
     response=st.session_state['conversation'].predict(input=userInput)
     print(st.session_state['conversation'].memory.buffer)
+    
 
     return response
 
+
+
 response_container = st.container()
 # Here we will have a container for user input text box
 container = st.container()
 
+
 with container:
     with st.form(key='my_form', clear_on_submit=True):
         user_input = st.text_area("Your question goes here:", key='input', height=100)
         submit_button = st.form_submit_button(label='Send')
 
         if submit_button:
-            # store the user input text into st.session_state['messages']
             st.session_state['messages'].append(user_input)
-            # getresponse is the function we just created, a wrapper of predict()
-            # model_response = getresponse(user_input,st.session_state['API_Key'])
-            model_response = getresponse(user_input,st.session_state['OPENAI_API_KEY'])
+            model_response=getresponse(user_input,st.session_state['API_Key'])
             st.session_state['messages'].append(model_response)
+            
 
             with response_container:
                 for i in range(len(st.session_state['messages'])):
@@ -77,3 +76,7 @@ with container:
                             message(st.session_state['messages'][i], is_user=True, key=str(i) + '_user')
                         else:
                             message(st.session_state['messages'][i], key=str(i) + '_AI')
+        
+             
+
+