Delete streamlit-chat-context-in-session.py

streamlit-chat-context-in-session.py

@@ -1,125 +0,0 @@
-# Manages user & assistant messages in the session state.
-
-### 1. Import the libraries
-import streamlit as st
-import time
-import os
-
-from dataclasses import dataclass
-from dotenv import load_dotenv
-# https://api.python.langchain.com/en/latest/llms/langchain_community.llms.cohere.Cohere.html#langchain_community.llms.cohere.Cohere
-from langchain_community.llms import Cohere
-
-### 2. Setup datastructure for holding the messages
-# Define a Message class for holding the query/response
-@dataclass
-class Message:
-    role: str       # identifies the actor (system, user or human, assistant or ai)
-    payload: str    # instructions, query, response
-
-# Streamlit knows about the common roles as a result, it is able to display the icons
-USER = "user"            # or human, 
-ASSISTANT = "assistant"  # or ai, 
-SYSTEM = "system"
-
-# This is to simplify local development
-# Without this you will need to copy/paste the API key with every change
-try:
-    # CHANGE the location of the file
-    load_dotenv('C:\\Users\\raj\\.jupyter\\.env')
-    # Add the API key to the session - use it for populating the interface
-    if os.getenv('COHERE_API_KEY'):
-        st.session_state['COHERE_API_KEY'] = os.getenv('COHERE_API_KEY')
-except:
-    print("Environment file not found !! Copy & paste your Cohere API key.")
-
-
-### 3. Initialize the datastructure to hold the context
-MESSAGES='messages'
-if  MESSAGES not in st.session_state:
-    system_message  = Message(role=SYSTEM, payload='you are a polite assistant named "Ruby".')
-    st.session_state[MESSAGES] = [system_message]
-
-### 4. Setup the title & input text element for the Cohere API key
-#    Set the title
-#    Populate API key from session if it is available
-st.title("Multi-Turn conversation interface  !!!")
-
-# If the key is already available, initialize its value on the UI
-if 'COHERE_API_KEY' in st.session_state:
-    cohere_api_key = st.sidebar.text_input('Cohere API key',value=st.session_state['COHERE_API_KEY'])
-else:
-    cohere_api_key = st.sidebar.text_input('Cohere API key',placeholder='copy & paste your API key')
-
-
-
-
-### 5. Define utility functions to invoke the LLM
-
-# Create an instance of the LLM
-@st.cache_resource
-def  get_llm():
-     return Cohere(model="command", cohere_api_key=cohere_api_key) 
-
-# Create the context by concatenating the messages
-def get_chat_context():
-    context = ''
-    for msg in st.session_state[MESSAGES]:
-        context = context + '\n\n' + msg.role + ':' + msg.payload
-    return context
-
-# Generate the response and return
-def  get_llm_response(prompt):
-    llm = get_llm()
-
-    # Show spinner, while we are waiting for the response
-    with st.spinner('Invoking LLM ... '):
-        # get the context
-        chat_context = get_chat_context()
-
-        # Prefix the query with context
-        query_payload = chat_context +'\n\n Question: ' + prompt
-
-        response = llm.invoke(query_payload)
-
-        return response
-
-### 6. Write the messages to chat_message container
-# Write messages to the chat_message element
-# This is needed as streamlit re-runs the entire script when user provides input in a widget
-# https://docs.streamlit.io/develop/api-reference/chat/st.chat_message
-for msg in st.session_state[MESSAGES]:
-    st.chat_message(msg.role).write(msg.payload)
-
-### 7. Create the *chat_input* element to get the user query
-# Interface for user input
-prompt = st.chat_input(placeholder='Your input here')
-
-### 8. Process the query received from user
-if prompt:
-    # create user message and add to end of messages in the session
-    user_message = Message(role=USER, payload=prompt)
-    st.session_state[MESSAGES].append(user_message)
-
-    # Write the user prompt as chat message
-    st.chat_message(USER).write(prompt)
-
-    # Invoke the LLM
-    response = get_llm_response(prompt)
-
-    # Create message object representing the response
-    assistant_message = Message(role=ASSISTANT, payload=response)
-
-    # Add the response message to the mesages array in the session
-    st.session_state[MESSAGES].append(assistant_message)
-
-    # Write the response as chat_message
-    st.chat_message(ASSISTANT).write(response)
-
-### 9. Write out the current content of the context
-st.divider()
-st.subheader('st.session_state[MESSAGES] dump:')
-
-# Print the state of the buffer
-for msg in st.session_state[MESSAGES]:
-    st.text(msg.role + ' : ' + msg.payload)