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# from flask import Flask, request, render_template
# from predict import Predict
# app = Flask(__name__)
# @app.route('/')
# def home():
# return render_template('index.html')
# @app.route('/predict', methods=['POST'])
# def predict():
# feature_list = request.form.to_dict()
# result = Predict().predict(feature_list['code'])
# return render_template('index.html', prediction_text=result)
# if __name__ == "__main__":
# app.run(debug=True, use_reloader=False, port='8080')
import streamlit as st
from streamlit_ace import st_ace
from predict import Predict
# st.title("jRefactoring")
# codeSnippet = st.text_input('Enter Java Code here')
# st.text("")
# if st.button('Check'):
# if(codeSnippet!=""):
# st.text("")
# result = Predict().predict(codeSnippet)
# st.write(result)
st.title("jRefactoring - Automatic Extract Method Refactoring Detection Using Deep Learning")
st.subheader("Enter Java Code")
codeSnippet = st_ace(language = 'java', auto_update = True)
if st.button('Check'):
if(codeSnippet!=""):
st.text("")
result, t, l = Predict().predict(codeSnippet)
st.write("Threshold - "+ str(t))
st.write("Calculated Loss - "+ str(l))
st.write(result) |