app.py

import os
import json
import pandas as pd
import gradio as gr
from llama_index.core import (
    VectorStoreIndex,
    download_loader,
    StorageContext
)

import logging
from dotenv import load_dotenv, find_dotenv
from pathlib import Path

# from llama_index.llms.mistralai import MistralAI
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
# from llama_index.embeddings.mistralai import MistralAIEmbedding
from src.utils_fct import *

TITLE = "RIZOA-AUCHAN Chatbot Demo"
DESCRIPTION = "Example of an assistant with Gradio, coupling with function callings and Mistral AI via its API"
PLACEHOLDER = (
    "Vous pouvez me posez une question, appuyer sur Entrée pour valider"
)
EXAMPLES = ["Comment fait on pour produire du maïs ?", "Rédige moi une lettre pour faire un stage dans une exploitation agricole", "Comment reprendre une exploitation agricole ?"]
MODEL = "mistral-large-latest"

# FILE = Path(__file__).resolve()
# BASE_PATH = FILE.parents[0]

load_dotenv()
ENV_API_KEY = os.environ.get("MISTRAL_API_KEY")
# HISTORY = pd.read_csv(os.path.join(BASE_PATH, "data/cereal_price.csv"), encoding="latin-1")
# HISTORY = HISTORY[[HISTORY["memberStateName"]=="France"]]
# HISTORY['price'] = HISTORY['price'].str.replace(",", ".").astype('float64')

# Define LLMs
CLIENT = MistralClient(api_key=ENV_API_KEY)
# EMBED_MODEL = MistralAIEmbedding(model_name="mistral-embed", api_key=ENV_API_KEY)

with gr.Blocks() as demo:
    with gr.Row(): 
        with gr.Column(scale=1): 
            gr.Image(value= os.path.join(BASE_PATH, "img/logo_rizoa_auchan.jpg"),#".\img\logo_rizoa_auchan.jpg", 
                    height=250,
                    width=250,
                    container=False, 
                    show_download_button=False
                    )
        with gr.Column(scale=4):   
            gr.Markdown(
                """ 
                # Bienvenue au Chatbot FAIR-PLAI 
                
                Ce chatbot est un assistant numérique, médiateur des vendeurs-acheteurs
                """
            )

    gr.Markdown(f""" ### {DESCRIPTION} """)

    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder=PLACEHOLDER)
    clear = gr.ClearButton([msg, chatbot])
    
    def respond(message, chat_history):
        messages = [ChatMessage(role="user", content=message)]
        # response = client.chat(
        #     model=MODEL, 
        #     messages=messages)

        response = forecast(messages)
        
        # prompt = f"Reformule le résultat suivant {response}"
        # prompt = [ChatMessage(role="user", content=prompt)]
        # chat_history.append((message, str(response)))
        final_response = CLIENT.chat(
            model=MODEL, 
            messages=response
        ).choices[0].message.content
        return "", [[None, None], 
                [None, str(final_response)]
                ]

    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    

# demo.title = TITLE

if __name__ == "__main__":
    demo.launch()