Update main.py

main.py

@@ -1,64 +1,41 @@
-from flask import Flask, jsonify, request, make_response
-from qdrant_client.http import models
+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
+
+
+# Model dependencies :
+from qdrant_client.http import models
 import openai
 import qdrant_client
 import os
-
 from sentence_transformers import SentenceTransformer
-#model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2') # good so far
-#model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') # good so far
 
-model = SentenceTransformer('/code/vectorizing_model', cache_folder='/')
+
+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'
 
-
-# # Set the cache folder to the one you created
-# cache_folder = '/app/sentence_transformers_cache'
-# util.cache_folder = cache_folder
-
-
-# from langchain.embeddings import HuggingFaceEmbeddings
-
-# model = HuggingFaceEmbeddings(model_name='paraphrase-multilingual-MiniLM-L12-v2')
-
-# import os
-# os.environ['TRANSFORMERS_CACHE'] = '/blabla/cache/'
-# os.environ['HF_HOME'] = '/blabla/cache/'
-
-
+# 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 = ''
 
 
-app = Flask(__name__)
-CORS(app, origins='*')
-openai.api_key = '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'
-
-
-
-
+# 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 = ["lir"]
 
-
-#collection_names = ["new_lir"] # replace with the collection name used on Qdrant
-collection_names = ["lir"] # replace with the collection name used on Qdrant
-
-
-import re
-
-@app.route('/')
-def hello_world():
-    return 'Hello, World!'
-
-
+# Used functions : 
 def filtergpt(text):
     # Define a regular expression pattern to extract law and article number
     pattern = re.compile(r"Loi ([^,]+), article (\d+(\.\d+)?)")
@@ -69,6 +46,135 @@ def filtergpt(text):
     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=6):
+    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"],
+            "source": result.payload["source"],
+            "collection": collection_name
+        }
+        resultes.append(result_dict)
+    return resultes
+
+def perform_search_and_get_results_with_filter(collection_name, query,reference_filter , limit=6):
+    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"],
+            "source": result.payload["source"],
+            "titre": result.payload["titre"],
+            "collection": collection_name
+        }
+        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():
@@ -96,6 +202,12 @@ def chat():
                   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:
@@ -104,50 +216,12 @@ def chat():
             for collection_name in collection_names:
                 search_results = perform_search_and_get_results(collection_name, prompt)
                 results.extend(search_results)
-            return jsonify({'result_qdrant':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
 
-def perform_search_and_get_results(collection_name, query, limit=6):
-    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"],
-            "source": result.payload["source"],
-            "collection": collection_name
-        }
-        resultes.append(result_dict)
-    return resultes
-
-def perform_search_and_get_results_with_filter(collection_name, query,reference_filter , limit=6):
-    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"],
-            "source": result.payload["source"],
-            "titre": result.payload["titre"],
-            "collection": collection_name
-        }
-        resultes.append(result_dict)
-    return resultes
-
 if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860)
+    app.debug = True
+    app.run()