Updated app.py

.gitignore

@@ -0,0 +1 @@
+/.streamlit

app.py

@@ -2,45 +2,69 @@ import streamlit as st
 from langchain_community.llms import Replicate
 from langchain.chains import ConversationChain
 from langchain.memory import ConversationSummaryMemory
-from langchain.prompts import ChatPromptTemplate
 import os
 
 # Setting page title and header
 st.set_page_config(page_title="Chat GPT Clone", page_icon="🤖")
 st.markdown("<h1 style='text-align: center;'>How can I assist you? </h1>", unsafe_allow_html=True)
 
-replicate_api = st.secrets['REPLICATE_API_TOKEN']
+# Initialize session state if not already done
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "assistant", "content": "Hey there! Feel free to ask me anything. What can I do for you?"}]
+if "conversation" not in st.session_state:
+    st.session_state["conversation"] = None
+if "REPLICATE_API_TOKEN" not in st.session_state:
+    st.session_state["REPLICATE_API_TOKEN"] = ""
+
+def clear_chat_history():
+    # Clear the chat history
+    st.session_state.messages = [{"role": "assistant", "content": "Okay, let's start over, what are we talking about now?"}]
+    
+    # Reset the conversation memory (clear previous memory)
+    if 'conversation' in st.session_state:
+        # Reinitialize the memory to reset the conversation memory state
+        llm = Replicate(
+            model="meta/meta-llama-3-8b-instruct",
+            model_kwargs={
+                "temperature": 0.01,
+                "top_p": 0.9,
+                "max_length": 128,
+            },
+        )
+        # Recreate the conversation chain with a fresh memory
+        st.session_state['conversation'] = ConversationChain(
+            llm=llm,
+            verbose=True,
+            memory=ConversationSummaryMemory(llm=llm),
+        )
+        
 # Sidebar for API key input and summarisation
 with st.sidebar:
-    # Check if API key is provided via st.secrets
-    if 'REPLICATE_API_TOKEN' in st.secrets:
-        if not (replicate_api.startswith('r8_') and len(replicate_api)==40):
-            replicate_api = st.text_input('Enter your Replicate API token:', type= 'password')
-            st.warning('Invalid or no API key! Please try again')
-        else:
-            st.success('Try asking now!')
+    # Get Replicate API Token
+    replicate_api = st.secrets.get('REPLICATE_API_TOKEN', '')
+    if not replicate_api or not (replicate_api.startswith('r8_') and len(replicate_api) == 40):
+        replicate_api = st.text_input('Enter your Replicate API token:', type='password')
+        if not replicate_api:
+            st.warning('API token is required for this app to work!')
     else:
-        replicate_api = st.text_input('Enter your Replicate API token:', type= 'password')
-        
+        st.success('Replicate API token is valid!')
+    
+    # Set environment variable for Replicate
     os.environ['REPLICATE_API_TOKEN'] = replicate_api
-            
-    # Button to summarise the conversation
-    summarise_button = st.button("Summarise the conversation", key="summarise_button")
+    st.session_state['REPLICATE_API_TOKEN'] = replicate_api
 
-    # Check if the summarise button was clicked
+    # Summarise button logic
+    summarise_button = st.button("Summarise the conversation", key="summarise_button")
     if summarise_button:
         if st.session_state.get('conversation') and st.session_state['conversation'].memory:
-            # Generate the summary
             summary = st.session_state['conversation'].memory.buffer
-            st.session_state['summary'] = summary  # Store in session state
+            st.session_state['summary'] = summary
             st.write("Summary:\n\n" + summary)
         else:
-            # Handle case where no conversation memory exists
-            summary = "No conversation to summarise leh!"
-            st.write(summary)
+            st.write("No conversation to summarise.")
 
-    # Add a download button if a summary exists
-    if st.session_state.get('summary'):
+    # Add download button for summary
+    if 'summary' in st.session_state:
         st.download_button(
             label="Download Summary",
             data=st.session_state['summary'],
@@ -48,61 +72,52 @@ with st.sidebar:
             mime="text/plain",
         )
 
-# Initialize conversation state if not already done
-if 'conversation' not in st.session_state:
-    st.session_state['conversation'] = None
-if 'messages' not in st.session_state:
-    st.session_state['messages'] = []
-if 'REPLICATE_API_TOKEN' not in st.session_state:
-    st.session_state['REPLICATE_API_TOKEN'] = ''
+    clear_chat = st.button('Clear Chat History', on_click=clear_chat_history)
 
-# Function to get response from OpenAI model
-def get_response(user_input, replicate_api):
-    if st.session_state['conversation'] is None:
-        llm = Replicate(
-            model=("meta/meta-llama-3-8b-instruct"),
-            model_kwargs={
-                "temperature": 0.01,
-                "top_p": 0.9,
-                "max_length": 128,
-            },
-        )
-        st.session_state['conversation'] = ConversationChain(
-            llm=llm,
-            verbose=True,
-            memory=ConversationSummaryMemory(llm=llm),
-        )
-    # Call the conversation chain
+# Initialize conversation state if not done already
+if 'conversation' not in st.session_state or st.session_state['conversation'] is None:
+    llm = Replicate(
+        model="meta/meta-llama-3-8b-instruct",
+        model_kwargs={
+            "temperature": 0.01,
+            "top_p": 0.9,
+            "max_length": 128,
+        },
+    )
+    st.session_state['conversation'] = ConversationChain(
+        llm=llm,
+        verbose=True,
+        memory=ConversationSummaryMemory(llm=llm),
+    )
+
+# Function to get the response from the LLM
+def get_response(user_input):
     response_dict = st.session_state['conversation'].invoke(input=user_input)
-    
-    # Extract the response field only
-    return response_dict.get("response", "No response generated leh!")
+    return response_dict.get("response", "No response generated!")
 
 # Function to get user input
 def get_text():
-    chat_input = st.chat_input("Say Hello")
-    return chat_input
+    return st.chat_input("Say something!")
 
-# Display chat history
+# Display the chat history (including the initial message)
 if st.session_state.messages:
     for msg in st.session_state.messages:
         with st.chat_message(msg["role"]):
             st.markdown(msg["content"])
 
-# Get user input
+# Handle user input and conversation flow
 user_input = get_text()
-
 if user_input:
     try:
-        # Get the response from the LLM
-        response = get_response(user_input, st.session_state['REPLICATE_API_TOKEN'])
-        
-        # Append the user message to session state
+        # Append user input to messages
         st.session_state.messages.append({"role": "user", "content": user_input})
         with st.chat_message("user"):
             st.markdown(user_input)
 
-        # Append and display the LLM response
+        # Get response from LLM
+        response = get_response(user_input)
+
+        # Append LLM response to messages
         st.session_state.messages.append({"role": "LLM", "content": response})
         with st.chat_message("LLM"):
             st.markdown(response)