working_backend
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7e58c32311
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5
DM.py
5
DM.py
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@ -50,7 +50,4 @@ class DM:
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elif self.curr_state['intent'] == 'SUMMARIZEPAPER':
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return "sumPaper"
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else:
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if self.curr_state['intent'] in ['FINDPAPER', 'FINDDATASET', 'SUMMARIZEPAPER'] and len(self.curr_state['entities']) == 0:
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return "ClarifyResource"
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else:
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return "GenClarify"
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return "Clarify"
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@ -9,26 +9,38 @@ class Recommender:
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self.retriever = retriever
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self.rand_seed = 5
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def _new(self, material):
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for row in self.curr_recommendations:
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print(row)
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if row['id'] == material['id'] and row['type'] == material['type']:
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return False
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return True
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def _match_tags(self, material, interest):
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score = 0.7
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for tag in material['tags']:
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if cosine_similarity(np.array(self.retriever.encode([tag])),
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np.array(self.retriever.encode([interest]))) > score:
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if material not in self.curr_recommendations:
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if self._new(material):
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print('hi')
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self.curr_recommendations.append(material)
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self.recommended.append(False)
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def generate_recommendations(self, interests, new_material):
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for interest in interests:
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for material in new_material:
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for i, material in new_material.iterrows():
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self._match_tags(material, interest)
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def make_recommendation(self, user):
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if len(self.curr_recommendations) == 0:
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return ""
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index = random.choice(list(range(0, len(self.curr_recommendations))))
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while self.recommended[index] == True:
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index = random.choice(list(range(0, len(self.curr_recommendations))))
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to_consider = [idx for idx, value in enumerate(self.recommended) if value == False]
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if len(to_consider) == 0:
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return ""
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index = random.choice(list(range(0, len(to_consider))))
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index = self.recommended[index]
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#while self.recommended[index] == True:
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# index = random.choice(list(range(0, len(self.curr_recommendations))))
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recommendation = "Hey " + user + "! This " + self.curr_recommendations[index][
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'type'].lower() + " about " + ', '.join(
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self.curr_recommendations[index]['tags']).lower() + " was posted recently by " + \
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@ -168,13 +168,14 @@ class ResponseGenerator:
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gen_kwargs = {"length_penalty": 1.5, "num_beams":6, "max_length": 120}
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answer = self.generators['summ'](gen_seq, **gen_kwargs)[0]['generated_text'] + ' '
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return answer
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elif action == "ClarifyResource":
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if state['intent'] in ['FINDPAPER', 'SUMMARIZEPAPER']:
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return 'Please specify the title, the topic or the paper of interest.'
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elif action == "Clarify":
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if state['intent'] in ['FINDPAPER', 'SUMMARIZEPAPER'] and len(state['entities']) == 0:
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if len(self.paper) == 0:
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return 'Please specify the title, the topic of the paper of interest.'
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elif state['intent'] == 'FINDDATASET' and len(state['entities']) == 0:
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if len(self.dataset) == 0:
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return 'Please specify the title, the topic of the dataset of interest.'
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else:
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return 'Please specify the title, the topic or the dataset of interest.'
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elif action == "GenClarify":
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gen_kwargs = {"length_penalty": 2.5, "num_beams":8, "max_length": 120}
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question = self.generators['amb']('question: '+ utterance + ' context: ' + consec_history , **gen_kwargs)[0]['generated_text']
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return question
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gen_kwargs = {"length_penalty": 2.5, "num_beams":8, "max_length": 120}
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question = self.generators['amb']('question: '+ utterance + ' context: ' + consec_history , **gen_kwargs)[0]['generated_text']
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return question
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