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import torch
import pandas as pd
import torch.nn as nn
from flask import Flask, render_template, request
from model.bilstm import BiLSTM
app = Flask(__name__)
model = BiLSTM.load_from_checkpoint('checkpoints/bilstm_result/epoch=23-step=3456.ckpt', lr=1e-3, num_classes=1, input_size=12)
model.eval()
model.freeze()
@app.route('/', methods=['POST', 'GET'])
def index():
if request.method == 'POST':
data = {
'tau1': [request.form.get('tau1')],
'tau2': [request.form.get('tau2')],
'tau3': [request.form.get('tau3')],
'tau4': [request.form.get('tau4')],
'p1': [request.form.get('p1')],
'p2': [request.form.get('p2')],
'p3': [request.form.get('p3')],
'p4': [request.form.get('p4')],
'g1': [request.form.get('g1')],
'g2': [request.form.get('g2')],
'g3': [request.form.get('g3')],
'g4': [request.form.get('g4')]
}
df = pd.DataFrame(data).astype('float')
X = torch.tensor(df.values.tolist())
with torch.no_grad():
preds = model(X)
preds = nn.Sigmoid()(preds.squeeze(1))
preds = preds.numpy()
if preds > 0.5:
result = 'Stable'
else:
result = 'Unstable'
return render_template('index.html', result=result)
return render_template('index.html')
if __name__ == '__main__':
app.run(debug=True) |