def predict():
form = PredictForm()
value = randint(2000, 5000)
if form.validate_on_submit():
flash(f"Prediction: {value}", "success")
return redirect(url_for("home"))
return render_template("predict.html", title="Predict", form=form)
def bangalore():
form = PredictForm()
name = getareanames()
if form.validate_on_submit():
return redirect(url_for('test1'))
return render_template('bangalore.html',
form=form,
data=name,
title='Home')
def predictOutput():
form = PredictForm()
if form.validate_on_submit():
habitants = form.habitants.data
csvText = form.csvText.data
ht = lstmPredict(csvText, habitants)
return render_template("pages/predictOutput.html",
out=habitants,
csvText=csvText,
ht=ht)
return render_template("pages/predictOutput.html")
def predict():
form = PredictForm()
if form.validate_on_submit():
sex,exang,ca,cp,restecg,slope,thal = form.data['sex'],\
form.data['exang'],\
form.data['ca'],\
form.data['cp'],\
form.data['restecg'],\
form.data['slope'],\
form.data['thal']
flash("Have Heart Disease" if prediction(sex,exang,ca,cp,restecg,slope,thal)\
else "No Heart Disease" ,'success')
return render_template('predict.html',
title='predict the heart disease',
form=form)
def linear():
form = PredictForm()
if form.validate_on_submit():
stock = request.form["stockTicker"]
days = int(request.form["daysToPredict"])
stock_csv(stock)
prediction = predictPrice(stock, days)
if (days == 1):
flash("Linear Regression | " + str(stock) + "'s High in " +
str(days) + " day will be: $" +
str(round(prediction[0], 2)) + ".")
else:
flash("Linear Regression | " + str(stock) + "'s High in " +
str(days) + " days will be: $" +
str(round(prediction[0], 2)) + ".")
return render_template('linear.html', title='Linear Regression', form=form)
def home():
form = PredictForm()
if form.validate_on_submit():
cleaned = pattern.sub(' ', form.example.data.lower())
new_examples = [cleaned]
predictions, probs = classifier.predict(new_examples)
return render_template('result.html',
max_coef=classifier.get_max_coefficient(),
words=classifier.get_coefficients_for(
new_examples[0]),
example=new_examples[0],
pos_prob=probs[1],
neg_prob=probs[0],
form=TrainForm())
return render_template('index.html', form=form)
def home():
form = PredictForm()
if form.validate_on_submit():
cleaned = pattern.sub(' ', form.example.data.lower())
new_examples = [cleaned]
predictions, probs = classifier.predict(new_examples)
return render_template('result.html',
max_coef = classifier.get_max_coefficient(),
words = classifier.get_coefficients_for(new_examples[0]),
example = new_examples[0],
pos_prob = probs[1],
neg_prob = probs[0],
form = TrainForm())
return render_template('index.html',
form = form)
def indexfunc():
form=PredictForm()
model = keras.models.load_model("hospital_model.h5")
transformer = joblib.load("data_transformer.joblib")
prediction_text='Result will appear here...'
if form.validate_on_submit():
newdict={
'age': [str(form.age.data)],
'time_in_hospital': [int(form.time_in_hospital.data)],
'num_medications': [int(form.num_medications.data)],
'number_diagnoses': [int(form.number_diagnoses.data)],
'metformin':[str(form.metformin.data)],
'chlorpropamide':[str(form.chlorpropamide.data)],
'glimepiride':[str(form.glimepiride.data)],
'tolazamide':[str(form.tolazamide.data)],
'insulin':[str(form.insulin.data)],
'race':[str(form.race.data)],
'admission_type_id':[int(form.admission_type_id.data)],
'admission_source_id':[int(form.admission_source_id.data)],
'max_glu_serum':[str(form.max_glu_serum.data)],
'A1Cresult':[str(form.A1Cresult.data)]
}
newds=pd.DataFrame(newdict)
prediction = model.predict(transformer.transform(newds))
max_index_col = np.argmax(prediction, axis=1)
if max_index_col==0:
prediction_text=' The Patient will be readmitted more than 30 Times '
if max_index_col==1:
prediction_text=' The Patient will be readmitted less than 30 Times '
if max_index_col==2:
prediction_text=' The Patient will be not be readmitted '
return render_template('regression.html', form=form,prediction_text=prediction_text)