Update app.py

app.py

@@ -1,83 +1,84 @@
-import streamlit as st
-from streamlit_chat import message
-# from langchain.llms import OpenAI   #This import has been replaced by the below import please
-from langchain_openai import OpenAI
-from langchain.chains import ConversationChain
-from langchain.chains.conversation.memory import (ConversationBufferMemory, 
-                                                  ConversationSummaryMemory, 
-                                                  ConversationBufferWindowMemory
-               
-                                                  )
-
-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['API_Key'] =''
-
-# 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("😎")
-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:
-
-        llm = OpenAI(
-            temperature=0,
-            openai_api_key=api_key,
-            model_name='gpt-3.5-turbo-instruct'  # 'text-davinci-003' model is depreciated now, so we are using the openai's recommended model
-        )
-
-        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['API_Key'])
-            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')
-        
-             
-
-        
+import streamlit as st
+from streamlit_chat import message
+# from langchain.llms import OpenAI   #This import has been replaced by the below import please
+from langchain_openai import OpenAI
+from langchain.chains import ConversationChain
+from langchain.chains.conversation.memory import (ConversationBufferMemory, 
+                                                  ConversationSummaryMemory, 
+                                                  ConversationBufferWindowMemory
+               
+                                                  )
+
+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['API_Key'] =''
+
+# 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("😎")
+groq_api_key=st.sidebar.text_input(label="Groq 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)
+
+
+def getresponse(userInput, api_key):
+
+    if st.session_state['conversation'] is None:
+
+        '''llm = OpenAI(
+            temperature=0,
+            openai_api_key=api_key,
+            model_name='gpt-3.5-turbo-instruct'  # 'text-davinci-003' model is depreciated now, so we are using the openai's recommended model
+        )'''
+
+        llm=ChatGroq(model="Gemma2-9b-It",groq_api_key=groq_api_key)
+
+
+        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['API_Key'])
+            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')
+        
+             
+
+