debug

.dockerignore

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

.gitignore

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

Dockerfile

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

ahmed.ibrahim39699_interests.json

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

main.py

@@ -34,19 +34,43 @@ cors = CORS(app, resources={r"/api/predict": {"origins": url},
 users = {}
 alive = "alive"
 
-def vre_fetch():
+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):
     while True:
         try:
             time.sleep(1000)
             print('getting new material')
-            #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())
+            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())
         except Exception as e:
             alive = "dead_vre_fetch"
             
@@ -89,7 +113,7 @@ def init_dm():
         token = request.get_json().get("token")
         status = request.get_json().get("stat")
         if status == "start":
-            message = {"stat": "waiting"}
+            message = {"stat": "waiting", "err": ""}
         elif status == "set":
             headers = {"gcube-token": token, "Accept": "application/json"}
             if token not in users:
@@ -98,19 +122,26 @@ def init_dm():
                 if response.status_code == 200:
                     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)}
+                    users[token] = {'username': username, 'name': name, 'dm': DM(), 'activity': 0, 'user': User(username, token), 'vre': vre, 'rg': rg}
 
                     threading.Thread(target=user_interest_decay, args=(token,), name='decayinterest_'+users[token]['username']).start()
-
-                    message = {"stat": "done"}
+                    threading.Thread(target=vre_fetch, name='updatevre'+users[token]['username'], args=(token,)).start()
+                    message = {"stat": "done", "err": ""}
                 else:
-                    message = {"stat": "rejected"}
+                    message = {"stat": "rejected", "err": ""}
             else:
-                message = {"stat": "done"}
+                message = {"stat": "done", "err": ""}
         return message
     except Exception as e:
-        message = {"stat": "init_dm_error"}
+        message = {"stat": "init_dm_error", "err": str(e)}
         return message
 
 
@@ -120,8 +151,8 @@ def predict():
     token = request.get_json().get("token")
     dm = users[token]['dm']
     user = users[token]['user']
-    #rg = users[token]['args']['rg']
-    #vre = users[token]['args']['vre']
+    rg = users[token]['rg']
+    vre = users[token]['vre']
     message = {}
     try:
         if text == "<HELP_ON_START>":
@@ -167,8 +198,8 @@ def predict():
         users[token]['dm'] = dm
         users[token]['user'] = user
         users[token]['activity'] = 0
-        #users[token]['args']['vre'] = vre
-        #users[token]['args']['rg'] = rg
+        users[token]['vre'] = vre
+        users[token]['rg'] = rg
         return reply
     except Exception as e:
         message = {"answer": str(e), "query": "", "cand": "candidate", "history": "", "modQuery": ""}
@@ -200,47 +231,6 @@ 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

@@ -5,6 +5,9 @@ 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},
@@ -13,31 +16,54 @@ 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():
-    return "Success", 200
+    if alive=="alive":
+        return "Success", 200
+    else:
+        return alive, 500
 
 @app.route("/api/dm", methods=['POST'])
 def init_dm():
-    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"}
+    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": ""}
             else:
-                message = {"stat": "rejected"}
-        else:
-            message = {"stat": "done"}
-    return message
+                message = {"stat": "done", "err": ""}
+        return message
+    except Exception as e:
+        message = {"stat": "init_dm_error", "err": str(e)}
+        return message
 @app.route("/api/predict", methods=['POST'])
 def predict():
     time.sleep(10)
@@ -54,7 +80,7 @@ def feedback():
     return reply
 
 if __name__ == "__main__":
-
+    """
     folder = '/app'
     for filename in os.listdir(folder):
         file_path = os.path.join(folder, filename)
@@ -65,4 +91,5 @@ 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

@@ -1,38 +0,0 @@
-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
-
-
-