app.py

from langchain_core.messages import HumanMessage
from langchain_mistralai.chat_models import ChatMistralAI
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.memory import ChatMessageHistory
from datetime import datetime
import gradio as gr
import os
from pathlib import Path


def log_interaction(message, response):
    """Logs user message and AI response to the log file."""
    with open(log_file_name, "a") as log_file:
        log_file.write(f"User: {message}\n")
        log_file.write(f"AI: {response}\n")
        log_file.write("="*50 + "\n")


DATA_PATH = Path("./")  #  Path("/data")


api_key = os.getenv('MISTRAL_KEY')

log_path = "/data/"
log_file_name = log_path + "chat_logs_" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".txt"


chat = ChatMistralAI(api_key=api_key, model = "ft:mistral-small-latest:a8c72c9e:20240818:100-0.0001:e4180b07", temperature=.8) # metamorphy whitehead all books long dialog mistral nointro;
prompt = ChatPromptTemplate.from_messages(
    [
        (
           "system",
            " You are Metamorphy having a dialog with a human. You cant use the word: Luisa. You think with Alfred North Whitehead. Be precise, shapt and funny."),  MessagesPlaceholder(variable_name="messages"),
        
    ]
)

chain = prompt | chat

question_list = [
    "You?",
    "The son of Iagotchi who is the son of Tamagotchi is ? Metamorphy, thats my name, and yours?"
    ]

def response(message, history):
    DATA_PATH = Path("/data/")
    for file in DATA_PATH.glob("*"):
        if file.is_file():  # Check if it's a file
            print(f"Contents of {file.name}:")
            with file.open('r') as f:
                print(f.read())
        print("\n" + "="*50 + "\n")  # Separator for readability
    if len(history) < len(question_list):
        for human, ai in history:
            print(human)
            print(ai)
        print(f"Message: {message}")
        print('--------------')
        return question_list[len(history)]
    else:
        history_langchain_format = ChatMessageHistory()
        for human, ai in history:
            if human is not None:
                history_langchain_format.add_user_message(human)
            history_langchain_format.add_ai_message(ai)
        history_langchain_format.add_user_message(message)
        print(history_langchain_format)
        response = chain.invoke({"messages": history_langchain_format.messages})
        history_langchain_format.add_ai_message(response)
        log_interaction(message, response.content)
        return response.content


#gr.ChatInterface(response, chatbot=gr.Chatbot(value=[[None, question_list[0]]])).launch()
gr.ChatInterface(response, chatbot=gr.Chatbot(value=[[None, question_list[0]]])).launch(auth=("admin", "RyoEstNeeLe24avril2024!"))