def handle_data():
if request.method == 'POST':
return render_template("search.html")
if request.method == 'GET':
query = request.args.get('content')
model = NLPModel()
clf_path = 'lib/models/SentimentClassifier.pkl'
with open(clf_path, 'rb') as f:
model.clf = pickle.load(f)
vec_path = 'lib/models/TFIDFVectorizer.pkl'
with open(vec_path, 'rb') as f:
model.vectorizer = pickle.load(f)
user_query = query
uq_vectorized = model.vectorizer_transform(np.array([user_query]))
prediction = model.predict(uq_vectorized)
# print(prediction)
pred_proba = model.predict_proba(uq_vectorized)
confidence = round(pred_proba[0], 3)
print(prediction,confidence)
if prediction == 0:
filename = 'cry.jpg'
return send_file(filename, mimetype='image/jpg')
else:
filename = 'smile.jpg'
return send_file(filename, mimetype='image/jpg')
import pickle
import numpy as np
from model import NLPModel
app = Flask(__name__)
api = Api(app)
model = NLPModel()
clf_path = 'lib/models/SentimentClassifier.pkl'
with open(clf_path, 'rb') as f:
model.clf = pickle.load(f)
vec_path = 'lib/models/TFIDFVectorizer.pkl'
with open(vec_path, 'rb') as f:
model.vectorizer = pickle.load(f)
# argument parsing
parser = reqparse.RequestParser()
parser.add_argument('query')
@app.route('/')
def main():
return "Main Page\nIf you use curl, using 'curl -X GET http://127.0.0.1:5000/prediction -d query='that movie was boring''"
class PredictSentiment(Resource):
def get(self):
# use parser and find the user's query
args = parser.parse_args()
