Develop

.gitignore

@@ -101,4 +101,4 @@ venv.bak/
 /site
 
 # mypy
-.mypy_cache/
+.mypy_cache/

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.autopep8"
+    },
+    "python.formatting.provider": "none"
+}

LICENSE

@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

README.md

@@ -1,9 +1,5 @@
 # README
 
-This is the [Flask](http://flask.pocoo.org/) [quick start](http://flask.pocoo.org/docs/1.0/quickstart/#a-minimal-application) example for [Render](https://render.com).
+This is the [Flask](http://flask.pocoo.org/) API for IAGORA.
 
-The app in this repo is deployed at [https://flask.onrender.com](https://flask.onrender.com).
-
-## Deployment
-
-Follow the guide at https://render.com/docs/deploy-flask.
+The app in this repo is deployed at [https://iagora.onrender.com/](https://iagora.onrender.com/).

app.py

@@ -1,6 +1,80 @@
-from flask import Flask
+from flask import Flask, request, jsonify
 app = Flask(__name__)
 
+from flask_cors import CORS
+
+import json
+import gensim
+from flask import Response
+from models import llm
+from models import txtmodel
+from models import jsonloader
+from models import expfind
+from models import findstage
+
+
+CORS(app)
 @app.route('/')
 def hello_world():
     return 'Hello, World!'
+
+# Charger le modèle pré-entraîné de Word2Vec en français
+model = gensim.models.KeyedVectors.load_word2vec_format("models/frWac_non_lem_no_postag_no_phrase_200_skip_cut100.bin", binary=True)
+
+# Fonction pour obtenir des synonymes d'un mot avec le modèle Word2Vec
+def get_synonyms(word):
+    synonyms = []
+    # Vérifier si le mot est dans le vocabulaire du modèle
+    if word in model.key_to_index:
+        # Obtenir les 10 mots les plus similaires au mot donné
+        similar_words = model.most_similar(word, topn=1000)
+        # Extraire les mots des tuples (mot, similarité)
+        for word, similarity in similar_words:
+            synonyms.append(word)
+    return synonyms
+
+@app.route("/ask", methods=["POST"])
+def ask():
+    question = request.get_json()
+    response = {
+        "text": txtmodel.txtmodel.get_response(question['query']),
+        "wantToLearn": False
+    }
+
+    # Vérifier si un synonyme de "apprendre" est présent dans la requête
+    synonyms_learn = get_synonyms("apprendre") # Utiliser le mot "apprendre" en français pour utiliser le modèle Word2Vec
+    query_lower = question['query'].lower()
+
+    synonym_found = any(synonym in query_lower for synonym in synonyms_learn)
+
+    # Imprimer "Tuteur Virtuel" et "Tuteur Réel" si un synonyme de "apprendre" est trouvé
+    if synonym_found:
+        response = {
+            "text": "Veuillez Choisir",
+            "wantToLearn": True
+        }
+
+    return jsonify(response)
+
+@app.route("/llm", methods=["POST"])
+def llm_route():
+    requete = request.get_json()
+    print(requete['q1'])
+    print(requete['url'])
+    #text = request.json["text"]
+    result = llm.Llm.callLlm(requete['q1'],requete['url'])
+    resp = Response(result)
+    resp.charset = "utf-8"
+    return resp
+
+@app.route("/expert",  methods=["POST"])
+def loadjson():
+    requete = request.get_json()
+    results = expfind.ExpFind.findExp(requete['message'])
+    return results
+
+@app.route("/stage",  methods=["POST"])
+def stage():
+    requete = request.get_json()
+    results = findstage.Findstage.finds(requete['message'])
+    return results

data/donnerExperts.json

@@ -0,0 +1,92 @@
+[{
+    "expert_id": 1,
+    "first_name": "Devlen",
+    "last_name": "Proughten",
+    "email": "dproughten0@house.gov",
+    "country": "China",
+    "years_of_experience": 4,
+    "specialization": "angular",
+    "certifications": "CPA"
+  }, {
+    "expert_id": 2,
+    "first_name": "Fawn",
+    "last_name": "Adriani",
+    "email": "fadriani1@thetimes.co.uk",
+    "country": "Philippines",
+    "years_of_experience": 4,
+    "specialization": "angular",
+    "certifications": "CFA"
+  }, {
+    "expert_id": 3,
+    "first_name": "Donielle",
+    "last_name": "Olner",
+    "email": "dolner2@msn.com",
+    "country": "Serbia",
+    "years_of_experience": 18,
+    "specialization": "angular",
+    "certifications": "CPA"
+  }, {
+    "expert_id": 4,
+    "first_name": "Theodor",
+    "last_name": "Custy",
+    "email": "tcusty3@examiner.com",
+    "country": "Russia",
+    "years_of_experience": 17,
+    "specialization": "Java",
+    "certifications": "PMP"
+  }, {
+    "expert_id": 5,
+    "first_name": "Barth",
+    "last_name": "Amort",
+    "email": "bamort4@sogou.com",
+    "country": "Thailand",
+    "years_of_experience": 19,
+    "specialization": "react",
+    "certifications": "PMP"
+  }, {
+    "expert_id": 6,
+    "first_name": "Lorinda",
+    "last_name": "Tertre",
+    "email": "ltertre5@newsvine.com",
+    "country": "Tanzania",
+    "years_of_experience": 10,
+    "specialization": "react",
+    "certifications": "PHR"
+  }, {
+    "expert_id": 7,
+    "first_name": "Benedetta",
+    "last_name": "Prestner",
+    "email": "bprestner6@usnews.com",
+    "country": "Portugal",
+    "years_of_experience": 8,
+    "specialization": "angular",
+    "certifications": "PHR"
+  }, {
+    "expert_id": 8,
+    "first_name": "Claudine",
+    "last_name": "Slinn",
+    "email": "cslinn7@canalblog.com",
+    "country": "China",
+    "years_of_experience": 7,
+    "specialization": "angular",
+    "certifications": "PMP"
+  }, {
+    "expert_id": 9,
+    "first_name": "Reuben",
+    "last_name": "Sedgefield",
+    "email": "rsedgefield8@de.vu",
+    "country": "France",
+    "years_of_experience": 13,
+    "specialization": "angular",
+    "certifications": "CFA"
+  }, {
+    "expert_id": 10,
+    "first_name": "Zebadiah",
+    "last_name": "Hodinton",
+    "email": "zhodinton9@csmonitor.com",
+    "country": "Indonesia",
+    "years_of_experience": 4,
+    "specialization": "angular",
+    "certifications": "PHR"
+  }
+]

models/daollm.py

@@ -0,0 +1,35 @@
+from langchain import OpenAI, Cohere, sql_database
+from langchain_experimental.sql import SQLDatabaseChain
+import cx_Oracle
+import os
+from langchain.chains import load_chain
+import os
+
+COHERE_API_KEY="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"
+os.environ["COHERE_API_KEY"] = COHERE_API_KEY
+
+lib_dir = os.path.join(os.environ.get("HOME"), "Development", "instantclient_19_8")
+cx_Oracle.init_oracle_client(lib_dir=lib_dir)
+
+hostname='localhost'
+port='1521'
+service_name='ORCLCDB'
+username='c##iagora'
+password='iagora'
+
+cx_Oracle.init_oracle_client(lib_dir=lib_dir)
+oracle_connection_string_fmt = (
+  'oracle+cx_oracle://{username}:{password}@' +
+  cx_Oracle.makedsn('{hostname}', '{port}', service_name='{service_name}')
+)
+url = oracle_connection_string_fmt.format(
+  username=username, password=password, 
+  hostname=hostname, port=port, 
+  service_name=service_name,
+)
+from sqlalchemy import create_engine
+engine=create_engine(url, echo=True)
+db = SQLDatabase(engine)
+llm = Cohere(temperature=1, verbose=True)
+db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
+db_chain.run("Is Casey Brown in the database?")

models/embedding.py

@@ -0,0 +1,9 @@
+import openai
+
+def get_embedding(text, model="text-embedding-ada-002"):
+
+  # Get the embedding from the text embedding model.
+  embedding = openai.Embedding.create(input=[text], model=model)['data'][0]['embedding']
+
+  # Return the embedding.
+  return embedding

models/expfind.py

@@ -0,0 +1,24 @@
+import openai
+import os
+import json
+
+data_dir = os.path.join(os.path.dirname(__file__), "..", "data") 
+class ExpFind:
+    def findExp(message):
+        # Chargez le fichier JSON local
+        with open(os.path.join(data_dir, "donnerExperts.json"), "r") as file:
+            developers = json.load(file)
+            json_data = json.dumps(developers)
+        # Appeler l'API GPT
+        response = openai.Completion.create(
+            engine='text-davinci-003',
+            prompt = f"""{json_data}\n\n{message} :""",
+            max_tokens= 1000
+        )
+
+        # Traiter la réponse
+        answer = response.choices[0].text.strip()
+
+        # Afficher la réponse
+        print(answer)
+        return answer

models/findstage.py

@@ -0,0 +1,24 @@
+import openai
+import os
+import json
+
+data_dir = os.path.join(os.path.dirname(__file__), "..", "data") 
+class Findstage:
+    def finds(message):
+        # Chargez le fichier JSON local
+        with open(os.path.join(data_dir, "MOC.json"), "r") as file:
+            developers = json.load(file)
+            json_data = json.dumps(developers)
+        # Appeler l'API GPT
+        response = openai.Completion.create(
+            engine='text-davinci-003',
+            prompt = f"""{json_data}\n\n{message} :""",
+            max_tokens= 1000
+        )
+
+        # Traiter la réponse
+        answer = response.choices[0].text.strip()
+
+        # Afficher la réponse
+        print(answer)
+        return answer

models/jsonloader.py

@@ -0,0 +1,29 @@
+import json
+from pathlib import Path
+
+class JsonLoader:
+    def loadJson(path):
+        data = json.loads(Path(path).read_text())
+        return data
+
+    def search_in_json(data, search_string):
+      results = []
+      for item in data:
+        if search_string in item:
+          results.append(item)
+      return results
+
+
+    import json
+
+    def chercher_dans_json(cle, chaine_json):
+        try:
+            # Charger le JSON depuis la chaîne
+            data = json.loads(chaine_json)
+
+            # Rechercher la clé dans le JSON
+            valeur = data[cle]
+
+            return valeur
+        except (json.JSONDecodeError, KeyError):
+            return None

models/keymodel.py

@@ -0,0 +1,10 @@
+import os
+
+class KeyModel:
+    def load_openai_api_key():
+        # Charge le fichier .env
+        with open(".env", "r") as f:
+            env = f.read()
+
+        # Récupère la clé API OpenAI
+        return os.environ.get("OPENAI_API_KEY", "")