app.py

import os
import gradio as gr
from llama_index import VectorStoreIndex, SimpleDirectoryReader, SummaryIndex
from llama_index.readers import SimpleWebPageReader
from llama_index.llms import MistralAI
from llama_index.embeddings import MistralAIEmbedding
from llama_index import ServiceContext
from llama_index.query_engine import RetrieverQueryEngine

title = "Gaia Mistral Chat RAG URL Demo"
description = "Example of an assistant with Gradio, RAG from url and Mistral AI via its API"
placeholder = "Vous pouvez me posez une question sur ce contexte, appuyer sur Entrée pour valider"
placeholder_url = "Extract text from this url"
llm_model = 'mistral-small'
# choose api_key from .env or from input field
# placeholder_api_key = "API key"
env_api_key = os.environ.get("MISTRAL_API_KEY")

query_engine = None

with gr.Blocks() as demo:

    gr.Markdown(""" ### Welcome to Gaia Level 2 Demo 
    Add an URL at the bottom of the interface before interacting with the Chat.
    This demo allows you to interact with a webpage and then ask questions to Mistral APIs.
    Mistral will answer with the context extracted from the webpage.
    """)

    # with gr.Row():
        # api_key_text_box = gr.Textbox(placeholder=placeholder_api_key, container=False, scale=7)

    def setup_with_url(url):
        global query_engine

        # Set-up clients
        llm = MistralAI(api_key=env_api_key,model=llm_model)
        embed_model = MistralAIEmbedding(model_name='mistral-embed', api_key=env_api_key)
        service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm, embed_model=embed_model)

        # Set-up db
        documents = SimpleWebPageReader(html_to_text=True).load_data([url])
        index = VectorStoreIndex.from_documents(documents, service_context=service_context)
        query_engine = index.as_query_engine(similarity_top_k=15)
        return placeholder

    gr.Markdown(""" ### 1 / Extract data from URL """)

    with gr.Row():
        url_msg = gr.Textbox(placeholder=placeholder_url, container=False, scale=7)
        url_btn = gr.Button(value="Process url ✅", interactive=True)
        url_btn.click(setup_with_url, [url_msg], url_msg, show_progress= "full")
    
    gr.Markdown(""" ### 2 / Ask a question about this context """)

    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder=placeholder)
    clear = gr.ClearButton([msg, chatbot])

    def respond(message, chat_history):
        response = query_engine.query(message)
        chat_history.append((message, str(response)))
        return chat_history

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

demo.title = title

demo.launch()