janet
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NLU.py
2
NLU.py
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@ -89,7 +89,7 @@ class NLU:
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Query -> coref resolution & intent extraction -> if intents are not confident or if query is ambig -> rewrite query and recheck -> if still ambig, ask a clarifying question
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"""
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if utterance.lower() in ["help", "list resources", "list papers", "list datasets", "list topics"]:
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return {"modified_query": utterance, "intent": "COMMAND", "entities": [], "is_offensive": False, "is_clear": True}
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return {"modified_query": utterance.lower(), "intent": "COMMAND", "entities": [], "is_offensive": False, "is_clear": True}
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self.to_process = utterance
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@ -247,7 +247,7 @@ class ResponseGenerator:
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elif action == "command":
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if utterance == "help":
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return self.gen_response(action="Help", name=name, vrename=vrename)
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return self.gen_response(action="getHelp", vrename=vrename)
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elif utterance == "list resources":
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return self.gen_response(action="listResources", vrename=vrename)
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elif utterance == "list papers":
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2
main.py
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main.py
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@ -130,7 +130,7 @@ def predict():
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rec.generate_recommendations(users[token]['username'], new_user_interests, new_vre_material)
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dm.update(state)
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action = dm.next_action()
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response = rg.gen_response(action=action, utterance=state['modified_query'], state=dm.get_recent_state(), consec_history=dm.get_consec_history(), chitchat_history=dm.get_chitchat_history(), vrename=vre.name)
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response = rg.gen_response(action=action, utterance=state['modified_query'], state=dm.get_recent_state(), consec_history=dm.get_consec_history(), chitchat_history=dm.get_chitchat_history(), vrename=vre.name, username=users[token]['username'], name=users[token]['name'].split()[0])
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message = {"answer": response, "query": text, "cand": "candidate", "history": dm.get_consec_history(), "modQuery": state['modified_query']}
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if state['intent'] == "QA":
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response = response.split("_______ \n The answer is: ")[1]
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