def script():
client1 = request.form['client1'];
client2 = request.form['client2'];
legal1 = request.form['legal1'];
legal2 = request.form['legal2'];
from_d = request.form['from'];
start = from_d
df1 = model_call(str(client1),str(legal1))
model = pickle.load(open('model.pkl', 'rb'))
lastdate_1=give_last_date(client1, legal1)
pred1=model.forecast(6)
pred1_mean=round(pred1.mean(), 3)
show_predict1=np.array(df1[str(datetime.datetime.strptime(str(lastdate_1),'%Y%m%d').date())])
show_predict1=np.append(show_predict1, pred1)
df2 = model_call(str(client2),str(legal2))
model = pickle.load(open('model.pkl', 'rb'))
lastdate_2=give_last_date(client2, legal2)
pred2=model.forecast(6)
pred2_mean=round(pred2.mean(), 3)
print(lastdate_2)
print('.............................................................')
show_predict2=np.array(df2[str(datetime.datetime.strptime(str(lastdate_2),'%Y%m%d').date())])
show_predict2=np.append(show_predict2, pred2)
print(type(show_predict2))
#fig = make_subplots(rows=1, cols=2)
table_col=['Clients', 'Legal Entity', 'Mean of Predicted Paid Amount (USD)']
table_title=""
if pred1_mean > pred2_mean :
table_row=[[client1, client2], [legal1, legal2], [pred1_mean, pred2_mean]]
table_title="Client 1 and Legal Entity 1 are expected to do better business based on predicted mean amount"
else :
table_title="Client 2 and Legal Entity 2 are expected to do better business based on predicted mean amount "
table_row=[[client2, client1], [legal2, legal1], [pred2_mean, pred1_mean]]
fig = make_subplots(rows=2, cols=1, vertical_spacing=0.03,specs=[ [{"type": "table"}],[{"type": "scatter"}] ] )
fig.add_trace(go.Table(header=dict(values=table_col,font=dict(size=10),align="left"), cells=dict(values=table_row, height=40,align="left")), row=1, col=1)
fig.add_trace(go.Scatter(x =df1[start:].index,y=df1[start:],mode='lines',name='Recorded trend 1'), row=2, col=1)
fig.add_trace(go.Scatter(x=give_dates(lastdate_1),y=show_predict1,mode='lines',name='Predicted trend 1',line=dict(width=4, dash='dot')), row=2, col=1)
fig.add_trace(go.Scatter(x = df2[start:].index, y=df2[start:], mode='lines', name='Recorded Trend 2'), row=2, col=1)
fig.add_trace(go.Scatter(x=give_dates(lastdate_2), y=show_predict2, mode='lines', name='Predicted trend 2', line=dict(width=4, dash='dot')), row=2, col=1)
fig.update_yaxes(title_text="Paid Amount", row=2, col=1)
fig.update_xaxes(title_text='Dates', row=2, col=1)
fig.update_layout(title_text=table_title)
pio.write_html(fig, file='templates/output.html', auto_open=False)
return render_template('output.html')
def plot_com(client1, client2, legal1, legal2, from_d):
start = from_d
df1 = model_call(str(client1), str(legal1))
model = pickle.load(open('model.pkl', 'rb'))
lastdate_1 = give_last_date(client1, legal1)
pred1 = model.forecast(6)
show_predict1 = np.array(df1[str(
datetime.datetime.strptime(str(lastdate_1), '%Y%m%d').date())])
show_predict1 = np.append(show_predict1, pred1)
df2 = model_call(str(client2), str(legal2))
model = pickle.load(open('model.pkl', 'rb'))
lastdate_2 = give_last_date(client2, legal2)
pred2 = model.forecast(6)
print(lastdate_2)
print('.............................................................')
show_predict2 = np.array(df2[str(
datetime.datetime.strptime(str(lastdate_2), '%Y%m%d').date())])
show_predict2 = np.append(show_predict2, pred2)
print(type(show_predict2))
#fig = make_subplots(rows=1, cols=2)
fig = go.Figure()
fig.add_trace(
go.Scatter(x=df1[start:].index,
y=df1[start:],
mode='lines',
name='Recorded trend 1'))
fig.add_trace(
go.Scatter(x=give_dates(lastdate_1),
y=show_predict1,
mode='lines',
name='Predicted trend 1',
line=dict(width=4, dash='dot')))
fig.add_trace(
go.Scatter(x=df2[start:].index,
y=df2[start:],
mode='lines',
name='Recorded Trend 2'))
fig.add_trace(
go.Scatter(x=give_dates(lastdate_2),
y=show_predict2,
mode='lines',
name='Predicted trend 2',
line=dict(width=4, dash='dot')))
fig.update_yaxes(title_text="Paid Amount")
fig.update_xaxes(title_text='Dates')
pio.write_html(fig, file='templates/output.html', auto_open=False)
def plot_pred(cname, lename, from_d):
df = model_call(str(cname), str(lename))
model = pickle.load(open('model.pkl', 'rb'))
lastdate_ = give_last_date(cname, lename)
pred = model.forecast(6)
pred_mean = round(pred.mean(), 3)
start = from_d
show_predict = np.array(df[str(
datetime.datetime.strptime(str(lastdate_), '%Y%m%d').date())])
show_predict = np.append(show_predict, pred)
fig = go.Figure()
fig.add_trace(
go.Scatter(x=df[start:].index,
y=df[start:],
mode='lines',
name='Recorded'))
fig.add_trace(
go.Scatter(x=give_dates(lastdate_),
y=show_predict,
mode='lines',
name='Predicted',
line=dict(width=4, dash='dot')))
final_verdict = ''
if pred_mean > 0:
final_verdict = "Mean paid amount of next 6 months is {m}.<br>So, considering this it is beneficial to work with this client.".format(
m=pred_mean)
else:
final_verdict = "Mean paid amount of next 6 months is {m}.<br>So, considering this it is not beneficial to work with this client.".format(
m=pred_mean)
fig.update_layout(title_text=final_verdict)
fig.update_yaxes(title_text="Paid Amount")
fig.update_xaxes(title_text='Dates')
pio.write_html(fig, file='templates/predict.html', auto_open=False)
def plot_pred(cname, lename, from_d):
df = model_call(str(cname), str(lename))
model = pickle.load(open('model.pkl', 'rb'))
lastdate_ = give_last_date(cname, lename)
pred = model.forecast(6)
start = from_d
show_predict = np.array(df[str(
datetime.datetime.strptime(str(lastdate_), '%Y%m%d').date())])
show_predict = np.append(show_predict, pred)
fig = go.Figure()
fig.add_trace(
go.Scatter(x=df[start:].index,
y=df[start:],
mode='lines',
name='Recorded'))
fig.add_trace(
go.Scatter(x=give_dates(lastdate_),
y=show_predict,
mode='lines',
name='Predicted',
line=dict(width=4, dash='dot')))
slope = pred[-1] - df.iloc[-1]
final_verdict = ''
if slope > 0:
final_verdict = "Beneficial to work with this client"
else:
final_verdict = "Not beneficial to work with this client"
fig.update_layout(title_text=final_verdict)
fig.update_yaxes(title_text="Paid Amount")
fig.update_xaxes(title_text='Dates')
pio.write_html(fig, file='templates/predict.html', auto_open=False)