new graphs

app.py

@@ -1,31 +1,36 @@
 # import required packages
-from flask import Flask, request
-from flask_cors import CORS  # comment this on deployment
+from flask import Flask, render_template, request
+import requests
 import pickle
-import pandas as pd
+import numpy as np
+from flask_cors import CORS #comment this on deployment
+import sklearn
+from sklearn.preprocessing import StandardScaler
 
 # create a Flask object
 app = Flask(__name__, static_url_path='', static_folder='frontend/build')
-CORS(app)  # comment this on deployment
+CORS(app) #comment this on deployment
 
 # load the ml model which we have saved earlier in .pkl format - rn I'm using Maria's model
 model = pickle.load(open('diabetes_readmission_model.pkl', 'rb'))
 
+@app.route('/', methods=['GET'])
+# create a function Home that will return index.html(which contains html form)
+def Home():
+    return render_template('index.html')
+
+# creating object for StandardScaler
+standard_to = StandardScaler()
 
 # this gets called in readmissionform
-@app.route('/prediction/', methods=['POST'])
-def predict():
+@app.route('/predict_model', methods=['POST'])
+def add_movie():
     data = request.get_json()
 
-    race_map = {'Caucasian': 1, 'African American': 2, 'Asian': 3, 'Hispanic': 4, 'Other': 5}
-    gender_map = {'Female': 1, 'Male': 2, 'Other': 3}
-    age_map = {'0-10': 1, '10-20': 2, '30-40': 3, '40-50': 4, '60-70': 5, '70-80': 6, '80-90': 7, '90-100': 8, '100': 9}
-
     # all the values from the cleaned data set see: diabetes_readmission_model
-
-    race = race_map[data['race']]
-    gender = gender_map[data['gender']]
-    age = age_map[data['age']]
+    race = data['Race']
+    gender = data['Gender']
+    age = data['Age']
     admission_type_id = data['admission_type_id']
     discharge_disposition_id = data['discharge_disposition_id']
     admission_source_id = data['admission_source_id']
@@ -60,33 +65,83 @@ def predict():
     examide = data['examide']
     citoglipton = data['citoglipton']
     insulin = data['insulin']
-    glyburide_metformin = 1
-    glipizide_metformin = 1
-    glimepiride_pioglitazone = 1
-    metformin_rosiglitazone = 1
-    metformin_pioglitazone = 1
-    change = 1
-    diabetes_med = 1
+    glyburide_metformin = data['glyburide_metformin']
+    glipizide_metformin = data['glipizide_metformin']
+    glimepiride_pioglitazone = data['glimepiride_pioglitazone']
+    metformin_rosiglitazone = data['metformin_rosiglitazone']
+    metformin_pioglitazone = data['metformin_pioglitazone']
+    change = data['change']
+    diabetesMed = data['diabetesMed']
 
     prediction = model.predict([[race, gender, age, admission_type_id, discharge_disposition_id, admission_source_id,
-                                 time_in_hospital, num_lab_procedures, num_procedures, num_medications,
-                                 number_outpatient,
+                                 time_in_hospital, num_lab_procedures, num_procedures, num_medications, number_outpatient,
                                  number_emergency, number_inpatient, diag_1, diag_2, diag_3, number_diagnosis,
-                                 max_glu_serum, a1cresult, metformin, repaglinide, nateglinide, chlorpropamide,
-                                 glimepiride,
+                                 max_glu_serum, a1cresult, metformin, repaglinide, nateglinide, chlorpropamide, glimepiride,
                                  acetohexamide, glipizide, glyburide, tolbutamide, pioglitazone, rosiglitazone,
                                  acarbose, miglitol, troglitazone, tolazamide, examide, citoglipton, insulin,
-                                 glyburide_metformin, glipizide_metformin, glimepiride_pioglitazone,
-                                 metformin_rosiglitazone,
-                                 metformin_pioglitazone, change, diabetes_med]])
+                                 glyburide_metformin, glipizide_metformin, glimepiride_pioglitazone, metformin_rosiglitazone,
+                                 metformin_pioglitazone, change, diabetesMed]])
+
+    return 'Done', 201
+
+# Use this as an example of how to call the ML models ------------------------- delete after we are done with predict_model
+@app.route("/predict", methods=['POST'])
+# define the predict function which is going to predict the results from ml model based on the given values through html form
+def predict():
+    Fuel_Type_Diesel = 0
+    if request.method == 'POST':
+
+        # Use request.form to get the data from html form through post method.
+        # these all are nothing but features of our dataset(ml model)
+        Year = int(request.form['Year'])
+        Year = 2020 - Year
+        Present_Price = float(request.form['Present_Price'])
+        Kms_Driven = int(request.form['Kms_Driven'])
+        Kms_Driven2 = np.log(Kms_Driven)
+        Owner = int(request.form['Owner'])
+        Fuel_Type_Petrol = request.form['Fuel_Type_Petrol']
+
+        # Fuel_Type(feature) is categorised into petrol, diesel, cng, therefore we have done one-hot encoding on it while building model
+        if (Fuel_Type_Petrol == 'Petrol'):
+            Fuel_Type_Petrol = 1
+            Fuel_Type_Diesel = 0
+        elif (Fuel_Type_Petrol == 'Diesel'):
+            Fuel_Type_Petrol = 0
+            Fuel_Type_Diesel = 1
+        else:
+            Fuel_Type_Petrol = 0
+            Fuel_Type_Diesel = 0
+
+        # Seller_type(feature) is categorised into indivividual and dealer,therefore we have done one-hot encoding on it while building model
+        Seller_Type_Individual = request.form['Seller_Type_Individual']
+        if (Seller_Type_Individual == 'Individual'):
+            Seller_Type_Individual = 1
+        else:
+            Seller_Type_Individual = 0
+
+        # Transmission mannual(feature) is categorised into mannual and automatic,therefore we have done one-hot encoding on it while building model
+        Transmission_Mannual = request.form['Transmission_Mannual']
+        if (Transmission_Mannual == 'Mannual'):
+            Transmission_Mannual = 1
+        else:
+            Transmission_Mannual = 0
+        prediction = model.predict([[Present_Price, Kms_Driven2, Owner, Year, Fuel_Type_Diesel, Fuel_Type_Petrol,
+                                     Seller_Type_Individual, Transmission_Mannual]])
+        output = round(prediction[0], 2)
+
+        # condition for invalid values
+        if output < 0:
+            return render_template('index.html', prediction_text="Sorry you cannot sell this car")
 
-    readmission_chance = prediction[0]
-    response = {'result': readmission_chance}
-    print(response)
-    return response, 201
+        # condition for prediction when values are valid
+        else:
+            return render_template('index.html', prediction_text="You Can Sell the Car at {} lakhs".format(output))
 
+    # html form to be displayed on screen when no values are inserted; without any output or prediction
+    else:
+        return render_template('index.html')
 
 if __name__ == "__main__":
     # run method starts our web service
     # Debug : as soon as I save anything in my structure, server should start again
-    app.run(debug=True, port=9999)
+    app.run(debug=True)