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app.py

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+import streamlit as st
+from streamlit_chat import message
+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain
+from langchain.chains.conversation.memory import (ConversationSummaryMemory)
+import os
+
+os.environ["OPENAI_API_KEY"] = "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"
+
+if 'conversation' not in st.session_state:
+    st.session_state['conversation'] =None
+if 'messages' not in st.session_state:
+    st.session_state['messages'] =[]
+
+# Setting page title and header
+st.set_page_config(page_title="Chat GPT Clone", page_icon=":robot_face:")
+st.markdown("<h1 style='text-align: center;'>How can I assist you? </h1>", unsafe_allow_html=True)
+
+
+st.sidebar.title("😎")
+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)
+
+
+def getresponse(userInput):
+
+    if st.session_state['conversation'] is None:
+        llm = OpenAI(
+            temperature=0,
+            model_name='gpt-3.5-turbo-instruct'  #we can also use 'gpt-3.5-turbo'
+        )
+
+        st.session_state['conversation'] = ConversationChain(
+            llm=llm,
+            verbose=True,
+            memory=ConversationSummaryMemory(llm=llm)
+        )
+
+    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:
+            st.session_state['messages'].append(user_input)
+            model_response=getresponse(user_input)
+            st.session_state['messages'].append(model_response)
+            
+
+            with response_container:
+                for i in range(len(st.session_state['messages'])):
+                        if (i % 2) == 0:
+                            message(st.session_state['messages'][i], is_user=True, key=str(i) + '_user')
+                        else:
+                            message(st.session_state['messages'][i], key=str(i) + '_AI')
+        
+             
+
+        

requirements.txt

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+langchain
+langchain_openai
+langchain_core
+streamlit
+openai
+streamlit_chat