app.py

import os
import re 

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import DirectoryLoader, PyPDFLoader

from langchain.vectorstores import Chroma
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.prompts import PromptTemplate
from langchain.llms import HuggingFaceHub
from langchain.chains import RetrievalQA

from transformers import pipeline

import gradio as gr


HUGGING_FACE_TOKEN = os.environ["HUGGING_FACE_TOKEN"] 

# Vous pouvez choisir parmi les nombreux midèles disponibles sur HugginFace (https://huggingface.co/models)
model_name = "llmware/industry-bert-insurance-v0.1"

def remove_special_characters(string):
        return re.sub(r"\n", " ", string)
    

def RAG_Langchain(query):
    embeddings = SentenceTransformerEmbeddings(model_name=model_name)
    repo_id = "llmware/bling-sheared-llama-1.3b-0.1"

    loader = DirectoryLoader('data/', glob="**/*.pdf", show_progress=True, loader_cls=PyPDFLoader)

    documents = loader.load()
    
    # La taille des chunks est un paramètre important pour la qualité de l'information retrouvée. Il existe plusieurs méthodes
    # pour en choisir la valeur. 
    # L'overlap correspond au nombre de caractères partagés entre un chunk et le chunk suivant
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)

    texts = text_splitter.split_documents(documents)

    chunk = texts[0]
    chunk.page_content = remove_special_characters(chunk.page_content)

    #Data Preparation
    for chunks in texts:
        chunks.page_content = remove_special_characters(chunks.page_content)

    # On charge tous les documents dans la base de données vectorielle, pour les utiliser ensuite
    vector_stores=Chroma.from_documents(texts, embeddings, collection_metadata = {"hnsw:space": "cosine"}, persist_directory="stores/insurance_cosine")

    #Retrieval
    load_vector_store=Chroma(persist_directory="stores/insurance_cosine", embedding_function=embeddings)

    #On prend pour l'instant k=1, on verra plus tard comment sélectionner les résultats de contexte
    docs = load_vector_store.similarity_search_with_score(query=query, k=1)
    results = {"Score":[],"Content":[],"Metadata":[]};

    for i in docs:
        doc, score = i
        #print({"Score":score, "Content":doc.page_content, "Metadata":doc.metadata})
        results['Score'].append(score)
        results['Content'].append(doc.page_content)
        results['Metadata'].append(doc.metadata)
        
    context = results['Content']
    
    return results
    

def generateResponseBasedOnContext(model_name, context_string, query):
    
    question_answerer = pipeline("question-answering", model=model_name)
    context_prompt = "You are a sports expert. Answer the user's question by using following context: "

    context = context_prompt + context_string
    print("context : ", context)

    result = question_answerer(question=query, context=context)
    return result['answer']


def gradio_adapted_RAG(model_name, query):
    context = str(RAG_Langchain(query)['Content'])
    generated_answer = generateResponseBasedOnContext(str(model_name), 
                               context, 
                               query)
    return generated_answer


dropdown = gr.Dropdown(choices=["distilbert-base-uncased-distilled-squad",
                                       "impira/layoutlm-document-qa", 
                                       "impira/layoutlm-invoices"], label="Choose a model")

iface = gr.Interface(fn=gradio_adapted_RAG, inputs=[dropdown, "text"], outputs="text")
iface.launch()