app.py

import os
import json
import gradio as gr
from llama_index.core import (
    VectorStoreIndex,
    download_loader,
    StorageContext
)
from dotenv import load_dotenv, find_dotenv

import chromadb

from llama_index.llms.mistralai import MistralAI
from llama_index.embeddings.mistralai import MistralAIEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core.indices.service_context import ServiceContext

from pathlib import Path

TITLE = "RIZOA-AUCHAN Chatbot Demo"
DESCRIPTION = "Example of an assistant with Gradio, coupling with function calling 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-medium"

load_dotenv()
env_api_key = os.environ.get("MISTRAL_API_KEY")
query_engine = None

# Define LLMs
llm = MistralAI(api_key=env_api_key, model=llm_model)
embed_model = MistralAIEmbedding(model_name="mistral-embed", api_key=env_api_key)

# create client and a new collection
db = chromadb.PersistentClient(path="./chroma_db")
chroma_collection = db.get_or_create_collection("quickstart")

# set up ChromaVectorStore and load in data
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
service_context = ServiceContext.from_defaults(
    chunk_size=1024, llm=llm, embed_model=embed_model
)

#PDFReader = download_loader("PDFReader")
#loader = PDFReader()

index = VectorStoreIndex(
    [], service_context=service_context, storage_context=storage_context
)
query_engine = index.as_query_engine(similarity_top_k=5)

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

'''
image = os.path.join(BASE_PATH,"img","logo_rizoa_auchan.jpg")
print(f"Chemin de l'image : {image}")
image = os.path.join("img","logo_rizoa_auchan.jpg")
print(f"chemin 2 : {image}")
image = os.path.abspath(os.path.join("img", "logo_rizoa_auchan.jpg"))
print(f"Image 3 : {image}")
image = os.path.join("https://huggingface.co/spaces/rizoa-auchan-hack/hack/blob/main/img/logo_rizoa_auchan.jpg")
print(f"Image 4 : {image}")
'''
image = os.path.join("logo_rizoa_auchan.jpg")
print(f"Chemin:{image}")

if os.path.exists(image):
    print("Image existe")
else:
    print("Image n'existe pas")


PLACEHOLDER = (image)

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"), 
                    #value=os.path.join("img","logo_rizoa_auchan.jpg"),
                    value="logo_rizoa_auchan.jpg",
                    height=250,
                    width=250,
                    container=False,
                    show_download_button=False
                    )
            '''
            gr.HTML(
                value = '<img src="https://huggingface.co/spaces/rizoa-auchan-hack/hack/resolve/main/logo_rizoa_auchan.jpg">'
            )
        with gr.Column(scale=4):   
            gr.Markdown(
                """ 
                # Bienvenue au Chatbot FAIR-PLAI 
                
                Ce chatbot est un assistant numérique, médiateur des vendeurs-acheteurs
                """
            )

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

    # with gr.Row():
    #     with gr.Column():
    #         input_file = gr.File(
    #             label="Load a pdf",
    #             file_types=[".pdf"],
    #             file_count="single",
    #             type="filepath",
    #             interactive=True,
    #         )
    #         file_msg = gr.Textbox(
    #             label="Loaded documents:", container=False, visible=False
    #         )

    #         input_file.upload(
    #             fn=load_document,
    #             inputs=[
    #                 input_file,
    #             ],
    #             outputs=[file_msg],
    #             concurrency_limit=20,
    #         )

    #         file_btn = gr.Button(value="Encode file ✅", interactive=True)
    #         btn_msg = gr.Textbox(container=False, visible=False)

    #         with gr.Row():
    #             db_list = gr.Markdown(value=get_documents_in_db)
    #             delete_btn = gr.Button(value="Empty db 🗑️", interactive=True, scale=0)

    #         file_btn.click(
    #             load_file,
    #             inputs=[input_file],
    #             outputs=[file_msg, btn_msg, db_list],
    #             show_progress="full",
    #         )
    #         delete_btn.click(empty_db, outputs=[db_list], show_progress="minimal")

    gr.Markdown(""" ### Ask a question """)

    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

if __name__ == "__main__":
    demo.launch(allowed_paths=['/home/user/app/img/','./img/','.'])