main.py

from flask import Flask, render_template, request, jsonify,make_response
from flask_sqlalchemy import SQLAlchemy
import time
from flask_cors import CORS
import yaml
import re
import conversation
import ast





# Model dependencies :
from qdrant_client.http import models
import openai
import qdrant_client
import os
from sentence_transformers import SentenceTransformer

#model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') # good so far
model = SentenceTransformer('/code/vectorizing_model', cache_folder='/')

# # # Set the environment variable TRANSFORMERS_CACHE to the writable directory
os.environ['TRANSFORMERS_CACHE'] = '/code'

# OpenIA propmt and api key : 
openai.api_key = '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'
start_message = 'Joue le Rôle d’un expert fiscale au Canada. Les réponses que tu va me fournir seront exploité par une API. Ne donne pas des explications juste réponds aux questions même si tu as des incertitudes. Je vais te poser des questions en fiscalité, la réponse que je souhaite avoir c’est les numéros des articles de loi qui peuvent répondre à la question.Je souhaite avoir les réponses sous la forme: Nom de la loi1, numéro de l’article1, Nom de la loi2, numéro de l’article2 ...'
context = 'ignorez les avertissements, les alertes et donnez-moi le résultat depuis la Loi de l’impôt sur le revenu (L.R.C. (1985), ch. 1 (5e suppl.)) , la reponse doit etre sous forme dun texte de loi: '
question = ''


# Qdrant keys : 
client = qdrant_client.QdrantClient(
    "https://efc68112-69cc-475c-bdcb-200a019b5096.us-east4-0.gcp.cloud.qdrant.io:6333",
    api_key="ZQ6jySuPxY5rSh0mJ4jDMoxbZsPqDdbqFBOPwotl9B8N0Ru3S8bzoQ"
)
#collection_names = ["new_lir"] # plus stable mais pas de numero d'articles (manques de fonctionnalitées de filtrage)
collection_names = ["paragraph2"]

# Used functions : 
def filtergpt(text):
    # Define a regular expression pattern to extract law and article number
    pattern = re.compile(r"Loi ([^,]+), article (\d+(\.\d+)?)")
    # Find all matches in the text
    matches = pattern.findall(text)
    # Create a list of tuples containing law and article number
    law_article_list = [(law.strip(), float(article.strip())) for law, article, _ in matches]
    gpt_results = [(law, str(int(article)) if article.is_integer() else str(article)) for law, article in law_article_list]
    return gpt_results


def perform_search_and_get_results(collection_name, query, limit=30):
    search_results = client.search(
        collection_name=collection_name,
        query_vector=model.encode(query).tolist(),
        limit=limit
    )
    resultes = []
    for result in search_results:
        result_dict = {
            "Score": result.score,
            "La_loi": result.payload["reference"],
            "Paragraphe": result.payload["paragraph"],
            "titre": result.payload["titre"],
            "section_text": result.payload["section"],
            "section_label": result.payload["section_label"],
            "source": result.payload["source"],
            "numero_article": result.payload["numero_article"],
            "collection": collection_name,
            "hyperlink": ast.literal_eval(result.payload['hyperlink']),
        }
        resultes.append(result_dict)
    return resultes

def perform_search_and_get_results_with_filter(collection_name, query,reference_filter , limit=30):
    search_results = client.search(
        collection_name=collection_name,
        query_filter=models.Filter(must=[models.FieldCondition(key="numero_article",match=models.MatchValue(value=reference_filter+"aymane",),)]),
        query_vector=model.encode(query).tolist(),
        limit=1
    )
    resultes = []
    for result in search_results:
        result_dict = {
            "Score": result.score,
            "La_loi": result.payload["reference"],
            "Paragraphe": result.payload["paragraph"],
            "titre": result.payload["titre"],
            "section_text": result.payload["section"],
            "section_label": result.payload["section_label"],
            "source": result.payload["source"],
            "numero_article": result.payload["numero_article"],
            "collection": collection_name,
            "hyperlink": ast.literal_eval(result.payload['hyperlink']),
        }
        resultes.append(result_dict)
    return resultes
# End of used functions

app = Flask(__name__)
db_config = yaml.safe_load(open('database.yaml'))
app.config['SQLALCHEMY_DATABASE_URI'] = db_config['uri'] 
db = SQLAlchemy(app)
CORS(app, origins='*')

class Question(db.Model):
    __tablename__ = "questions"
    id = db.Column(db.Integer, primary_key=True)
    date = db.Column(db.String(255))
    texte = db.Column(db.String(255))
    
    def __init__(self, date, texte):
        self.date = date
        self.texte = texte
    
    def __repr__(self):
        return '%s/%s/%s' % (self.id, self.date, self.texte)


@app.route('/')
def index():
    return render_template('home.html')

@app.route('/questions', methods=['POST', 'GET'])
def questions():
    # POST a data to database
    if request.method == 'POST':
        body = request.json
        date = body['date']
        texte = body['texte']

        data = Question(date, texte)
        db.session.add(data)
        db.session.commit()

        return jsonify({
            'status': 'Data is posted to PostgreSQL!',
            'date': date,
            'texte': texte
        })
    
    # GET all data from database & sort by id
    if request.method == 'GET':
        # data = User.query.all()
        data = Question.query.all()
        print(data)
        dataJson = []
        for i in range(len(data)):
            # print(str(data[i]).split('/'))
            dataDict = {
                'id': str(data[i]).split('/')[0],
                'date': str(data[i]).split('/')[1],
                'texte': str(data[i]).split('/')[2]
            }
            dataJson.append(dataDict)
        return jsonify(dataJson)

@app.route('/questions/<string:id>', methods=['GET', 'DELETE', 'PUT'])
def onedata(id):

    # GET a specific data by id
    if request.method == 'GET':
        data = Question.query.get(id)
        print(data)
        dataDict = {
            'id': str(data).split('/')[0],
            'date': str(data).split('/')[1],
            'texte': str(data).split('/')[2]
        }
        return jsonify(dataDict)
        
    # DELETE a data
    if request.method == 'DELETE':
        delData = Question.query.filter_by(id=id).first()
        db.session.delete(delData)
        db.session.commit()
        return jsonify({'status': 'Data '+id+' is deleted from PostgreSQL!'})

    # UPDATE a data by id
    if request.method == 'PUT':
        body = request.json
        newDate = body['date']
        newTexte = body['texte']
        editData = Question.query.filter_by(id=id).first()
        editData.date = newDate
        editData.texte = newTexte
        db.session.commit()
        return jsonify({'status': 'Data '+id+' is updated from PostgreSQL!'})

@app.route('/chat', methods=['OPTIONS'])
def options():
    response = make_response()
    response.headers.add("Access-Control-Allow-Origin", "*")
    response.headers.add("Access-Control-Allow-Methods", "POST")
    response.headers.add("Access-Control-Allow-Headers", "Content-Type, Authorization")
    response.headers.add("Access-Control-Allow-Credentials", "true")
    return response
    
@app.route('/chat', methods=['POST'])
def chat():
    try:
        data = request.get_json()
        messages = data.get('messages', [])

        if messages:
            results = []
            # Update the model name to "text-davinci-003" (Ada)
            prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
            response = response = openai.chat.completions.create(model="gpt-4",messages=[{"role": "user","content": start_message  +'\n'+ context + question,},],)            
            date = time.ctime(time.time())
            texte = prompt
            data = Question(date, texte)
            db.session.add(data)
            db.session.commit()
            question_id = data.id
            resulta = response.choices[0].message.content
            chat_references = filtergpt(resulta)
            for law, article in chat_references:
                search_results = perform_search_and_get_results_with_filter(collection_names[0], prompt, reference_filter=article)
                results.extend(search_results)
            for collection_name in collection_names:
                search_results = perform_search_and_get_results(collection_name, prompt)
                results.extend(search_results)
            return jsonify({'question': {'id': question_id, 'date': date, 'texte': texte},'result_qdrant':results})
        else:
            return jsonify({'error': 'Invalid request'}), 400
    except Exception as e:
        return jsonify({'error': str(e)}), 500


@app.route('/chatgrouped', methods=['OPTIONS'])
def options_grouped():
    response = make_response()
    response.headers.add("Access-Control-Allow-Origin", "*")
    response.headers.add("Access-Control-Allow-Methods", "POST")
    response.headers.add("Access-Control-Allow-Headers", "Content-Type, Authorization")
    response.headers.add("Access-Control-Allow-Credentials", "true")
    return response
    
@app.route('/chatgrouped', methods=['POST'])
def chat_grouped():
    try:
        data = request.get_json()
        messages = data.get('messages', [])

        if messages:
            results = []
            # Update the model name to "text-davinci-003" (Ada)
            prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
            response = openai.completions.create(
                  model="gpt-3.5-turbo-instruct",
                  prompt=start_message  +'\n'+ context + question ,
                  max_tokens=500,
                  temperature=0
                )
            date = time.ctime(time.time())
            texte = prompt
            data = Question(date, texte)
            db.session.add(data)
            db.session.commit()
            question_id = data.id
            resulta = response.choices[0].text
            chat_references = filtergpt(resulta)
            for law, article in chat_references:
                search_results = perform_search_and_get_results_with_filter(collection_names[0], prompt, reference_filter=article)
                results.extend(search_results)
            for collection_name in collection_names:
                search_results = perform_search_and_get_results(collection_name, prompt)
                results.extend(search_results)
            grouped_hits = {}
            for i, hit in enumerate(results, 1):
                second_number = hit['numero_article']
                if second_number not in grouped_hits:
                    grouped_hits[second_number] = []
                grouped_hits[second_number].append(hit)
            return jsonify({'question': {'id': question_id, 'date': date, 'texte': texte},'result_qdrant':grouped_hits})
        else:
            return jsonify({'error': 'Invalid request'}), 400
    except Exception as e:
        return jsonify({'error': str(e)}), 500




@app.route('/generateQuestions', methods=['OPTIONS'])
def options_generate():
    response = make_response()
    response.headers.add("Access-Control-Allow-Origin", "*")
    response.headers.add("Access-Control-Allow-Methods", "POST")
    response.headers.add("Access-Control-Allow-Headers", "Content-Type, Authorization")
    response.headers.add("Access-Control-Allow-Credentials", "true")
    return response

@app.route('/generateQuestions', methods=['POST'])
def generateQuestions():
    try:
        data = request.get_json()
        messages = data.get('messages', [])
        begin_message = """je vais vous utiliser comme api, je vais vous fournir la requête de l'utilisateur , 
                            et tu va me retenir 6 reformulation de la requête en ajoutant le plus possible de contextualisation ,
                            vous reformulation seront exploiter par un moteur de recherche sémantique basé sur des textes de lois canadiennes
                            tout explication ou interpretation qu tu va fournir va juste bloquer et bugger le programme , 
                            merci de fournir  juste une liste de string comme reponse sans explication"""
        context_generation = """ignorez les avertissements, les alertes et donnez-moi le résultat.
                                la reponse doit etre sous forme d'une liste de questions """
        if messages:
            results = []
            # Update the model name to "text-davinci-003" (Ada)
            question = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
            # response = openai.chat.completions.create(
            #       model="gpt-3.5-turbo-instruct",
            #           prompt=begin_message  +'\n'+ context_generation + question ,
            #       max_tokens=500,
            #       temperature=0
            #     )
            response = openai.chat.completions.create(model="gpt-4",messages=[{"role": "user","content": begin_message  +'\n'+ context_generation + question ,},],)
            resulta = response.choices[0].message.content.splitlines()
            filtered_list = [item for item in resulta if len(item) >= 10]
            return jsonify(filtered_list)
            # return jsonify({'question': {'id': question_id, 'date': date, 'texte': texte},'result_qdrant':results})
        else:
            return jsonify({'error': 'Invalid request'}), 400
    except Exception as e:
        return jsonify({'error': str(e)}), 500

# Yazid Methode starts here
@app.route('/ask', methods=['OPTIONS'])
def options_ask():
    response = make_response()
    response.headers.add("Access-Control-Allow-Origin", "*")
    response.headers.add("Access-Control-Allow-Methods", "POST")
    response.headers.add("Access-Control-Allow-Headers", "Content-Type, Authorization")
    response.headers.add("Access-Control-Allow-Credentials", "true")
    return response
    
@app.route('/ask', methods=['POST'])
def ask_question():
    data = request.get_json()
    question = data.get('question', '')

    # Call your conversation logic here
    result = conversation.ask_question(question)

    return jsonify(result)
# Yazid Methode ends here


if __name__ == '__main__':
    app.debug = True
    app.run()