app.py

import os
import time
import csv
import shutil
from datetime import datetime
import openai
import gradio as gr
import pandas as pd

# Embeddings
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma

# Chat Q&A
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage, SystemMessage

# This sets up OpenAI embeddings model
embeddings = OpenAIEmbeddings()

# Loads database from persisted directory
db_directory = "./docs/2023_12_21_chroma_db"
db = Chroma(persist_directory=db_directory, embedding_function=embeddings)

# This is code that retrieves relevant documents based on a similarity search (in this case, it grabs the top 2 relevant documents or chunks)
retriever = db.as_retriever(search_type='similarity', search_kwargs={"k":2})

with open('system_prompt.txt', 'r') as file:
    ORIG_SYSTEM_MESSAGE_PROMPT = file.read()


with open('user_info_simulated.txt', 'r') as file:
    user_info_simulated = file.read()

openai.api_key = os.getenv("OPENAI_API_KEY")

#chat = ChatOpenAI(model_name="gpt-3.5-turbo",temperature=0) # Faster for experiments
chat = ChatOpenAI(model_name="gpt-4",temperature=0)

# Make sure we don't exceed estimation of token limit:
TOKEN_LIMIT = 4096  # GPT-3.5 Turbo token limit
BUFFER = 100  # Extra tokens to consider for incoming messages
PERSISTENT_LOG_PATH = "/data/downvoted_responses.csv" # File in which to log downvoted responses
LOCAL_LOG_PATH = "./data/downvoted_responses.csv"

def estimate_tokens(texts):
    return sum([len(t.split()) for t in texts])

def truncate_history(history):
    tokens = estimate_tokens([msg.content for msg in history])
    while tokens + BUFFER > TOKEN_LIMIT and len(history) > 3:
        history = history[0:1] + history[3:]
        tokens = estimate_tokens([msg.content for msg in history])
    return history
    
def get_full_context(input):
    retrieved_documents = retriever.get_relevant_documents(input)
    context = ""  

    file_path = "./docs/Troubleshooting_Table.csv"
    data = pd.read_csv(file_path)

    for doc in retrieved_documents:
        index = doc.metadata['index']
        row_string = data.iloc[[index]].to_string(index=False)
        context += row_string + "\n\n" 

    return context


is_first_run = True  # Flag to check if it's the first run

# Here is the langchain
def predict(history, input):

    global is_first_run  # Use the global flag
    if is_first_run:
        context = get_full_context(input)
        print(context)  # For debugging
        is_first_run = False  # Set the flag to False after the first run
    else:
        context = ""

    history_langchain_format = []
    history_langchain_format.append(SystemMessage(content=f"{ORIG_SYSTEM_MESSAGE_PROMPT}, here is the user information: {user_info_simulated}"))
    for human, ai in history:
        history_langchain_format.append(HumanMessage(content=human))
        history_langchain_format.append(AIMessage(content=ai))
    history_langchain_format.append(HumanMessage(content=input))
    history_langchain_format.append(SystemMessage(content=f"Here is a table with some potentially useful information for troubleshooting: {context}"))

    # Truncate if history is too long
    history_langchain_format = truncate_history(history_langchain_format)

    gpt_response = chat(history_langchain_format)

    # Extract pairs of HumanMessage and AIMessage
    pairs = []
    for i in range(len(history_langchain_format)):
        if isinstance(history_langchain_format[i], HumanMessage) and (i+1 < len(history_langchain_format)) and isinstance(history_langchain_format[i+1], AIMessage):
            pairs.append((history_langchain_format[i].content, history_langchain_format[i+1].content))

    # Add the new AI response to the pairs for subsequent interactions
    pairs.append((input, gpt_response.content))

    return pairs

# Function to handle user message (this clears the interface)
def user(user_message, chatbot_history):
    return "", chatbot_history + [[user_message, ""]]

# Function to handle AI's response
def bot(chatbot_history):
    user_message = chatbot_history[-1][0] #This line is because we cleared the user_message previously in the user function above
    # Call the predict function to get the AI's response
    pairs = predict(chatbot_history, user_message)
    _, ai_response = pairs[-1]  # Get the latest response

    response_in_progress = ""
    for character in ai_response:
        response_in_progress += character
        chatbot_history[-1][1] = response_in_progress
        time.sleep(0.05)
        yield chatbot_history

# This is a function to do something with the voted information (TODO: Save this info somewhere?)
def vote(data: gr.LikeData):
    if data.liked:
        print("You upvoted this response: " + data.value)
    else:
        print("You downvoted this response: " + data.value)
        with open("output.txt", "a") as text_file:
          print(f"Disliked content: {data.value}", file=text_file)

def reset_flag():
    global is_first_run
    is_first_run = True

# The Gradio App interface
with gr.Blocks() as demo:
    gr.Markdown("""<h1><center>TROUBLESHOOTING Bot by CIONIC</center></h1>""")
    gr.Markdown("""<p><center>To open a new case, press the clear button.</center></p>""")
    chatbot = gr.Chatbot()
    textbox = gr.Textbox()
    clear_button = gr.ClearButton(components=[chatbot])
    clear_button.click(reset_flag, None, None)

    # Chain user and bot functions with `.then()`
    textbox.submit(user, [textbox, chatbot], [textbox, chatbot], queue=False).then(
        bot, chatbot, chatbot,
    )
    chatbot.like(vote, None, None)

# Enable queuing
demo.queue()
demo.launch(debug=True)