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server_flask.py
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from collections import OrderedDict
import inspect as inspector
import flask
import json
from flask import Flask, render_template
from flask import request
from sklearn.feature_extraction.text import CountVectorizer
from sentence_transformers import SentenceTransformer, util
import pickle
import tensorflow as tf
from flask_cors import CORS, cross_origin
# if you are using python 3, you should
import urllib.request
app = Flask(__name__)
CORS(app,resources={r"/getOutput": {"origins": "*"}})
senTransformer = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1')
model = pickle.load(open('mnb_model_v1.pkl','rb'))
count_vect = pickle.load(open('countvect_v1.pkl','rb'))
url = f'http://34.85.227.59:8983/solr'
BATCH_SIZE=50
MODEL_NAME1="msmarco-distilbert-base-dot-prod-v3"
model1 = SentenceTransformer(MODEL_NAME1)
# if torch.cuda.is_available():
# model1 = model1.to(torch.device("cuda"))
queries = [{"topic":'education','values':[]},{"topic":'healthcare','values':[]},
{"topic":'politics','values':[]},{"topic":'technology','values':[]},{"topic":'nature','values':[]},
{"topic":'chitchat','values':[]}]
def processSentence(sen):
embeddings = model1.encode(sen)
return str(list(embeddings))
def sendRequestToChitchat(inputQuery):
reqUrl = url+'/chitchat_v2/query?q=question:'+inputQuery+'&fl=*,score'
reqUrl = reqUrl.replace(" ", "%20")
content = urllib.request.urlopen(reqUrl).read()
#content = urllib.request.urlopen( url+'/chitchat_v2/query?q=question:'+inputQuery+'&fl=*,score' ).read()
response = json.loads(content)
docs = response['response']['docs']
answers = []
queries[0]['values'].append(0)
queries[1]['values'].append(0)
queries[2]['values'].append(0)
queries[3]['values'].append(0)
queries[4]['values'].append(0)
queries[5]['values'].append(1)
if len(docs)==0:
return "Didn't get you."
for doc in docs:
answers.append(doc['answer'])
query_emb = senTransformer.encode(inputQuery)
doc_emb = senTransformer.encode(answers)
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
doc_score_pairs = list(zip(answers, scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
result = []
for doc, score in doc_score_pairs:
temp = {}
temp['score']=score
temp['doc']=doc
result.append(temp)
return result[0]['doc']
def sendRequestToReddit(inputQuery,topic):
queryVector = processSentence(inputQuery)
if len(topic) != 0:
reqUrl = url+'/IR_reseach/query?fq=topic:'+topic[0]+'&indent=true&q.op=AND&q={!knn f=question_vector}'+queryVector
else:
reqUrl = url+'/IR_reseach/query?indent=true&q={!knn f=question_vector}'+queryVector
reqUrl = reqUrl.replace(" ", "%20")
content = urllib.request.urlopen(reqUrl).read()
response = json.loads(content)
docs = response['response']['docs']
answers = []
if len(docs)==0:
return "Didn't get you."
edu,nature,tech,health,politics = 0,0,0,0,0
for doc in docs:
if doc['topic'] == 'nature':
nature+=1
elif doc['topic'] == 'education':
edu+=1
elif doc['topic'] == 'technology':
tech+=1
elif doc['topic'] == 'healthcare':
health+=1
elif doc['topic'] == 'politics':
politics+=1
answers.append(doc['answer'])
edu/=len(docs)
nature/=len(docs)
tech/=len(docs)
health/=len(docs)
politics/=len(docs)
queries[0]['values'].append(edu)
queries[1]['values'].append(health)
queries[2]['values'].append(politics)
queries[3]['values'].append(tech)
queries[4]['values'].append(nature)
queries[5]['values'].append(0)
query_emb = senTransformer.encode(inputQuery)
doc_emb = senTransformer.encode(answers)
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
doc_score_pairs = list(zip(answers, scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
result = []
for doc, score in doc_score_pairs:
temp = {}
temp['score']=score
temp['doc']=doc
result.append(temp)
return result[0]['doc']
@app.route("/getOutput", methods=['POST'])
@cross_origin(origin='*')
def execute_query():
data = request.get_json()
topic = data['topics']
if len(topic)==0:
te = count_vect.transform([data['input']])
else:
te = count_vect.transform([topic[0]+':'+data['input']])
inputQuery = data['input']
op = model.predict(te)
print(op)
if op == 'chitchat':
answer = sendRequestToChitchat(inputQuery)
else:
answer = sendRequestToReddit(inputQuery,topic)
response = {
"response":answer
}
return response
@app.route("/getStatistics", methods=['GET'])
@cross_origin(origin='*')
def execute_statistics():
return queries
@app.route("/app")
@cross_origin(origin='*')
def execute_html():
return render_template('index.html')
@app.route("/statistics")
@cross_origin(origin='*')
def execute_statistics_page():
return render_template('statistics.html')
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5050)