.dockerignore

@@ -1,2 +0,0 @@
-.git
-__pycache__

.gitignore

@@ -1,3 +0,0 @@
-janet.pdf
-__pycache__/
-ahmed.ibrahim39699_interests.json

Dockerfile

@@ -2,12 +2,12 @@ FROM python:3.8
 
 WORKDIR /backend_janet 
 
-COPY requirements_simple.txt .
+COPY requirements_main.txt .
 
-RUN pip install -r requirements_simple.txt
+RUN pip install -r requirements_main.txt
 
 RUN rm -fr /root/.cache/*
 
 COPY . .
 
-ENTRYPOINT ["python", "main_simple.py"]
+ENTRYPOINT ["python", "main.py"]

ahmed.ibrahim39699_interests.json

@@ -1 +0,0 @@
-{"interest":{"0":"chatbots?","1":"list commands","2":"chatbots"},"frequency":{"0":2,"1":1,"2":1}}

main.py

@@ -34,43 +34,19 @@ cors = CORS(app, resources={r"/api/predict": {"origins": url},
 users = {}
 alive = "alive"
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-device_flag = torch.cuda.current_device() if torch.cuda.is_available() else -1
-
-query_rewriter = pipeline("text2text-generation", model="castorini/t5-base-canard")
-intent_classifier = pipeline("sentiment-analysis", model='/models/intent_classifier', device=device_flag)
-entity_extractor = spacy.load("/models/entity_extractor")
-offensive_classifier = pipeline("sentiment-analysis", model='/models/offensive_classifier', device=device_flag)
-ambig_classifier = pipeline("sentiment-analysis", model='/models/ambig_classifier', device=device_flag)
-coref_resolver = spacy.load("en_coreference_web_trf")
-        
-nlu = NLU(query_rewriter, coref_resolver, intent_classifier, offensive_classifier, entity_extractor, ambig_classifier)
-
-#load retriever and generator
-retriever = SentenceTransformer('/models/retriever/').to(device)
-qa_generator = pipeline("text2text-generation", model="/models/train_qa", device=device_flag)
-summ_generator = pipeline("text2text-generation", model="/models/train_summ", device=device_flag)
-chat_generator = pipeline("text2text-generation", model="/models/train_chat", device=device_flag)
-amb_generator = pipeline("text2text-generation", model="/models/train_amb_gen", device=device_flag)
-generators = {'qa': qa_generator,
-              'chat': chat_generator,
-              'amb': amb_generator,
-              'summ': summ_generator}
-rec = Recommender(retriever)
-  
-def vre_fetch(token):
+def vre_fetch():
     while True:
         try:
             time.sleep(1000)
             print('getting new material')
-            users[token]['vre'].get_vre_update()
-            users[token]['vre'].index_periodic_update()
-            users[token]['rg'].update_index(vre.get_index())
-            users[token]['rg'].update_db(vre.get_db())
-            #vre.get_vre_update()
-            #vre.index_periodic_update()
-            #rg.update_index(vre.get_index())
-            #rg.update_db(vre.get_db())
+            #users[token]['args']['vre'].get_vre_update()
+            #users[token]['args']['vre'].index_periodic_update()
+            #users[token]['args']['rg'].update_index(vre.get_index())
+            #users[token]['args']['rg'].update_db(vre.get_db())
+            vre.get_vre_update()
+            vre.index_periodic_update()
+            rg.update_index(vre.get_index())
+            rg.update_db(vre.get_db())
         except Exception as e:
             alive = "dead_vre_fetch"
             
@@ -113,7 +89,7 @@ def init_dm():
         token = request.get_json().get("token")
         status = request.get_json().get("stat")
         if status == "start":
-            message = {"stat": "waiting", "err": ""}
+            message = {"stat": "waiting"}
         elif status == "set":
             headers = {"gcube-token": token, "Accept": "application/json"}
             if token not in users:
@@ -123,25 +99,18 @@ def init_dm():
                     username = response.json()['result']['username']
                     name = response.json()['result']['fullname']
                 
-                    vre = VRE("assistedlab", token, retriever)
-                    vre.init()
-                    index = vre.get_index()
-                    db = vre.get_db()
-
-                    rg = ResponseGenerator(index,db, rec, generators, retriever)
-                
-                    users[token] = {'username': username, 'name': name, 'dm': DM(), 'activity': 0, 'user': User(username, token), 'vre': vre, 'rg': rg}
+                    users[token] = {'username': username, 'name': name, 'dm': DM(), 'activity': 0, 'user': User(username, token)}
 
                     threading.Thread(target=user_interest_decay, args=(token,), name='decayinterest_'+users[token]['username']).start()
-                    threading.Thread(target=vre_fetch, name='updatevre'+users[token]['username'], args=(token,)).start()
-                    message = {"stat": "done", "err": ""}
+
+                    message = {"stat": "done"}
                 else:
-                    message = {"stat": "rejected", "err": ""}
+                    message = {"stat": "rejected"}
             else:
-                message = {"stat": "done", "err": ""}
+                message = {"stat": "done"}
         return message
     except Exception as e:
-        message = {"stat": "init_dm_error", "err": str(e)}
+        message = {"stat": "init_dm_error"}
         return message
 
 
@@ -151,8 +120,8 @@ def predict():
     token = request.get_json().get("token")
     dm = users[token]['dm']
     user = users[token]['user']
-    rg = users[token]['rg']
-    vre = users[token]['vre']
+    #rg = users[token]['args']['rg']
+    #vre = users[token]['args']['vre']
     message = {}
     try:
         if text == "<HELP_ON_START>":
@@ -198,8 +167,8 @@ def predict():
         users[token]['dm'] = dm
         users[token]['user'] = user
         users[token]['activity'] = 0
-        users[token]['vre'] = vre
-        users[token]['rg'] = rg
+        #users[token]['args']['vre'] = vre
+        #users[token]['args']['rg'] = rg
         return reply
     except Exception as e:
         message = {"answer": str(e), "query": "", "cand": "candidate", "history": "", "modQuery": ""}
@@ -231,6 +200,47 @@ def feedback():
     
 if __name__ == "__main__":
     warnings.filterwarnings("ignore")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    device_flag = torch.cuda.current_device() if torch.cuda.is_available() else -1
+
+    query_rewriter = pipeline("text2text-generation", model="castorini/t5-base-canard")
+    intent_classifier = pipeline("sentiment-analysis", model='/models/intent_classifier', device=device_flag)
+    entity_extractor = spacy.load("/models/entity_extractor")
+    offensive_classifier = pipeline("sentiment-analysis", model='/models/offensive_classifier', device=device_flag)
+    ambig_classifier = pipeline("sentiment-analysis", model='/models/ambig_classifier', device=device_flag)
+    coref_resolver = spacy.load("en_coreference_web_trf")
+        
+    nlu = NLU(query_rewriter, coref_resolver, intent_classifier, offensive_classifier, entity_extractor, ambig_classifier)
+
+    #load retriever and generator
+    retriever = SentenceTransformer('/models/retriever/').to(device)
+    qa_generator = pipeline("text2text-generation", model="/models/train_qa", device=device_flag)
+    summ_generator = pipeline("text2text-generation", model="/models/train_summ", device=device_flag)
+    chat_generator = pipeline("text2text-generation", model="/models/train_chat", device=device_flag)
+    amb_generator = pipeline("text2text-generation", model="/models/train_amb_gen", device=device_flag)
+    generators = {'qa': qa_generator,
+                  'chat': chat_generator,
+                  'amb': amb_generator,
+		  'summ': summ_generator}
+    rec = Recommender(retriever)
+    vre = VRE("assistedlab", '2c1e8f88-461c-42c0-8cc1-b7660771c9a3-843339462', retriever)
+    vre.init()
+    index = vre.get_index()
+    db = vre.get_db()
+    rg = ResponseGenerator(index,db, rec, generators, retriever)
+    del retriever
+    del generators
+    del qa_generator
+    del chat_generator
+    del summ_generator
+    del amb_generator
+    del query_rewriter
+    del intent_classifier
+    del entity_extractor
+    del offensive_classifier
+    del ambig_classifier
+    del coref_resolver 
+    threading.Thread(target=vre_fetch, name='updatevre').start()
     threading.Thread(target=clear_inactive, name='clear').start()
     """
     conn = psycopg2.connect(host="janet-pg", database=os.getenv("POSTGRES_DB"), user=os.getenv("POSTGRES_USER"), password=os.getenv("POSTGRES_PASSWORD"))

main_simple.py

@@ -5,9 +5,6 @@ import shutil
 import re
 import requests
 import time
-from User import User
-from DM import DM
-import threading 
 app = Flask(__name__)
 url = os.getenv("FRONTEND_URL_WITH_PORT")
 cors = CORS(app, resources={r"/api/predict": {"origins": url},
@@ -16,54 +13,31 @@ cors = CORS(app, resources={r"/api/predict": {"origins": url},
                             r"/health": {"origins": "*"}
                            })
 users = {}
-alive = "alive"
-def user_interest_decay(token):
-    while True:
-        try:
-            if token in users:
-                print("decaying interests after 3 minutes for " + users[token]['username'])
-                time.sleep(180)
-                users[token]['user'].decay_interests()
-            else:
-                break
-        except Exception as e:
-            alive = "dead_interest_decay"
+
 @app.route("/health", methods=['GET'])
 def health():
-    if alive=="alive":
-        return "Success", 200
-    else:
-        return alive, 500
+    return "Success", 200
 
 @app.route("/api/dm", methods=['POST'])
 def init_dm():
-    try:
-        token = request.get_json().get("token")
-        status = request.get_json().get("stat")
-        if status == "start":
-            message = {"stat": "waiting", "err": ""}
-        elif status == "set":
-            headers = {"gcube-token": token, "Accept": "application/json"}
-            if token not in users:
-                url = 'https://api.d4science.org/rest/2/people/profile'
-                response = requests.get(url, headers=headers)
-                if response.status_code == 200:
-                    username = response.json()['result']['username']
-                    name = response.json()['result']['fullname']
-                
-                    users[token] = {'username': username, 'name': name, 'dm': DM(), 'activity': 0, 'user': User(username, token)}
-
-                    threading.Thread(target=user_interest_decay, args=(token,), name='decayinterest_'+users[token]['username']).start()
-
-                    message = {"stat": "done", "err": ""}
-                else:
-                    message = {"stat": "rejected", "err": ""}
+    token = request.get_json().get("token")
+    status = request.get_json().get("stat")
+    if status == "start":
+        message = {"stat": "waiting"}
+    elif status == "set":
+        headers = {"gcube-token": token, "Accept": "application/json"}
+        if token not in users:
+            url = 'https://api.d4science.org/rest/2/people/profile'
+            response = requests.get(url, headers=headers)
+            if response.status_code == 200:
+                username = response.json()['result']['username']
+                name = response.json()['result']['fullname']
+                message = {"stat": "done"}
             else:
-                message = {"stat": "done", "err": ""}
-        return message
-    except Exception as e:
-        message = {"stat": "init_dm_error", "err": str(e)}
-        return message
+                message = {"stat": "rejected"}
+        else:
+            message = {"stat": "done"}
+    return message
 @app.route("/api/predict", methods=['POST'])
 def predict():
     time.sleep(10)
@@ -80,7 +54,7 @@ def feedback():
     return reply
 
 if __name__ == "__main__":
-    """
+
     folder = '/app'
     for filename in os.listdir(folder):
         file_path = os.path.join(folder, filename)
@@ -91,5 +65,4 @@ if __name__ == "__main__":
                 shutil.rmtree(file_path)
         except Exception as e:
             print('Failed to delete %s. Reason: %s' % (file_path, e))
-    """
     app.run(host='0.0.0.0')

requirements.txt

@@ -0,0 +1,38 @@
+faiss-gpu==1.7.2
+Flask==1.1.4
+flask-cors==3.0.10
+protobuf==3.20.0
+matplotlib==3.5.3
+nltk==3.7
+numpy==1.22.4
+pandas==1.3.5
+PyPDF2==3.0.1
+pdfquery
+html2text
+regex==2022.6.2
+requests==2.25.1
+scikit-learn==1.0.2
+scipy==1.7.3
+sentencepiece==0.1.97
+sklearn-pandas==1.8.0
+spacy==3.4.4
+spacy-alignments==0.9.0
+spacy-legacy==3.0.12
+spacy-loggers==1.0.4
+spacy-transformers==1.1.9
+spacy-experimental==0.6.2
+torch @ https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp38-cp38-linux_x86_64.whl
+torchaudio @ https://download.pytorch.org/whl/cu116/torchaudio-0.13.1%2Bcu116-cp38-cp38-linux_x86_64.whl
+torchsummary==1.5.1
+torchtext==0.14.1
+sentence-transformers
+torchvision @ https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp38-cp38-linux_x86_64.whl
+tqdm==4.64.1
+transformers
+markupsafe==2.0.1
+psycopg2==2.9.5
+en-coreference-web-trf @ https://github.com/explosion/spacy-experimental/releases/download/v0.6.1/en_coreference_web_trf-3.4.0a2-py3-none-any.whl
+Werkzeug==1.0.1
+
+
+