Update main.py

main.py

@@ -1,13 +1,3 @@
-# -*- coding: utf-8 -*-
-"""flaskProto.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1VC7pgAb9uuRwD0Bm89x8sAvKi7uliBoF
-"""
-
-#cd drive/'My Drive'/datasetDevhack
 import os
 import flask
 import pandas as pd
@@ -25,75 +15,51 @@ from sklearn.pipeline import Pipeline
 # instantiate flask 
 app = flask.Flask(__name__)
 
-# load the model, and pass in the custom metric function
-global graph
-graph = tf.get_default_graph()
+# load the model
+model = load_model('final_model.h5')
 
 holidays_tt = ["2020-01-01",
-                "2020-01-15",
-                "2020-01-26",
-                "2020-02-21",
-                "2020-03-10",
-                "2020-03-25",
-                "2020-04-02",
-                "2020-04-06",
-                "2020-04-10",
-                "2020-05-01",
-                "2020-05-07",
-                "2020-05-25",
-                "2020-06-23",
-                "2020-08-01",
-                "2020-08-03",
-                "2020-08-12",
-                "2020-08-15",
-                "2020-08-22",
-                "2020-08-30",
-                "2020-08-31",
-                "2020-10-02",
-                "2020-10-25",
-                "2020-10-30",
-                "2020-11-14",
-                "2020-11-30",
-                "2020-12-25"
-                ]
+               "2020-01-15",
+               "2020-01-26",
+               "2020-02-21",
+               "2020-03-10",
+               "2020-03-25",
+               "2020-04-02",
+               "2020-04-06",
+               "2020-04-10",
+               "2020-05-01",
+               "2020-05-07",
+               "2020-05-25",
+               "2020-06-23",
+               "2020-08-01",
+               "2020-08-03",
+               "2020-08-12",
+               "2020-08-15",
+               "2020-08-22",
+               "2020-08-30",
+               "2020-08-31",
+               "2020-10-02",
+               "2020-10-25",
+               "2020-10-30",
+               "2020-11-14",
+               "2020-11-30",
+               "2020-12-25"
+              ]
 url = "https://api.openweathermap.org/data/2.5/weather?q=Bengaluru,in&APPID=b1a275b64af38a8f9823800a58345b93"
+
 # homepage
 @app.route("/", methods=["GET","POST"])
 def homepage():
     return flask.render_template("index.html")
 
 # define a predict function as an endpoint 
-# @app.route("/feedback", methods=["POST"])
-# def predict():  
-#     formFinal = []
-#     formData1 = int(flask.request.form['formData1'])
-#     print(formData1)
-#     formData2 = int(flask.request.form['formData2'])
-#     print(formData2)
-#     formFinal.append(formData1)
-#     formFinal.append(formData2)
-#     print(formFinal)
-
-
-mar = [0.14,0.53,0.24,0.13,0.67,0.87,0.22,0.23,0.12,0.56,0.23,0.25,0.78,0.12]
-model = load_model('final_model.h5')
-
-
 @app.route("/predict", methods=["POST"])
 def predict():
     dat = flask.request.form['date']
-    #print(dat)
     time = flask.request.form['time']
-   # print(time)
     
     #holiday
-    if str(dat) in holidays_tt:
-        holiday=1
-    else:
-        holiday=0
-    
-    #print("Holiday =", holiday)
-    
+    holiday = 1 if str(dat) in holidays_tt else 0
     
     response = requests.get(url).json()
     temp = float(response["main"]["temp"]) - 273.15
@@ -101,120 +67,64 @@ def predict():
     temp_max = float(response["main"]["temp_max"]) - 273.15
     pressure = response["main"]["pressure"]
     humidity = response["main"]["humidity"]
-    #print(temp, temp_min, temp_max, pressure, humidity)
-    
-    #dat = "2023-11-01"
-    #time = "01:01"
     
-    #week
+    # week
     date_time_obj = datetime.datetime.strptime(dat, '%Y-%m-%d')
-    week = datetime.date(date_time_obj.year,date_time_obj.month,date_time_obj.day).isocalendar()[1]
-    
-    if week<26:
-        week = week + 25
-        
+    week = datetime.date(date_time_obj.year, date_time_obj.month, date_time_obj.day).isocalendar()[1]
+    if week < 26:
+        week += 25
     
-    #hour
+    # hour
     hour = int(time[:-3])
     
-    #population
+    # population dictionary
     dic = {
-       "HSR Division" : 105265,
-       "Koramangala Division" : 63987,
-       "Indiranagar" : 58830,
-       "Shivajinagar" : 57437,
-       "Hebbal" : 54301,
-       "Whitefield" : 84428,
-       "Malleshwaram" : 57107,
-       "Rajaji Nagara Division" : 55250,
-       "Jayanagar" : 56658,
-       "Jalahalli" : 63391,
-       "Kengeri Division" : 68087,
-       "R R NAGAR" : 82848,
-       "Vidhanasoudha" : 69057,
-       "Peenya Division" : 96549
-       }
-    
+       "HSR Division": 105265,
+       "Koramangala Division": 63987,
+       "Indiranagar": 58830,
+       "Shivajinagar": 57437,
+       "Hebbal": 54301,
+       "Whitefield": 84428,
+       "Malleshwaram": 57107,
+       "Rajaji Nagara Division": 55250,
+       "Jayanagar": 56658,
+       "Jalahalli": 63391,
+       "Kengeri Division": 68087,
+       "R R NAGAR": 82848,
+       "Vidhanasoudha": 69057,
+       "Peenya Division": 96549
+    }
     
     lb = preprocessing.LabelBinarizer()
-    lb.fit(['HSR Division', 'Koramangala Division', 'Indiranagar',
-       'Shivajinagar', 'Hebbal', 'Whitefield', 'Malleshwaram',
-       'Rajaji Nagara Division', 'Jayanagar', 'Jalahalli',
-       'Kengeri Division', 'R R NAGAR', 'Vidhanasoudha',
-       'Peenya Division'])
-    
-    
+    lb.fit(list(dic.keys()))
     lt = list(dic.keys())
-    
     df = pd.DataFrame(lt)
-    
     divs = lb.transform(df)
-    
     divs = pd.DataFrame(divs)
     
-    #divs['Week'] = week
-    
-    #divs['Week'] = np.array(([week]*13).T
+    # Preparing data
+    week_col = [week] * 14
+    temp_max_col = [temp_max] * 14
+    temp_min_col = [temp_min] * 14
+    holiday_col = [holiday] * 14
+    pop_col = [dic[x] for x in lt]
+    hour_col = [hour] * 14
     
-    week = [week]*14
+    divs = pd.concat([pd.DataFrame(temp_max_col), divs], axis=1)
+    divs = pd.concat([pd.DataFrame(temp_min_col), divs], axis=1)
+    divs = pd.concat([pd.DataFrame(week_col), divs], axis=1)
+    divs = pd.concat([divs, pd.DataFrame(holiday_col)], axis=1)
+    divs = pd.concat([divs, pd.DataFrame(pop_col)], axis=1)
+    divs = pd.concat([divs, pd.DataFrame(hour_col)], axis=1)
     
-    temp_max = [temp_max]*14
-    temp_min = [temp_min]*14
-                        
-    holiday = [holiday]*14  
+    smol = pd.read_excel('smol.xlsx').iloc[:, 1:]
+    df = pd.DataFrame(np.concatenate((divs.values, smol.values), axis=0))
     
-    divs = pd.concat([pd.DataFrame(temp_max), divs], axis=1)
-    divs = pd.concat([pd.DataFrame(temp_min), divs], axis=1)
-    divs = pd.concat([pd.DataFrame(week), divs], axis=1)
-    divs = pd.concat([divs, pd.DataFrame(holiday)], axis=1)
-    
-        
-    pop = [dic[x] for x in lt]
-    
-    #population
-    divs = pd.concat([divs, pd.DataFrame(pop)], axis=1)
-    
-    
-    hour = [hour]*14
-    
-    divs = pd.concat([ divs, pd.DataFrame(hour)], axis=1)
-    
-    
-    smol = pd.read_excel('smol.xlsx')
-    smol = smol.iloc[:,1:]
-    #divs = pd.read_excel("pls.xlsx")
-    
-   # divs.to_csv("pls.csv")
-    
-    #print(smol.shape)
-    #print(divs.shape)
-
-    #fin = pd.concat([divs, smol])
-    
-    df = pd.DataFrame( np.concatenate( (divs.values, smol.values), axis=0 ) )
-    
-    from sklearn.preprocessing import StandardScaler
     sc_X = StandardScaler()
     df = sc_X.fit_transform(df)
-
-    
-    with graph.as_default():
-        prd = model.predict(df)
-        
-    
-    #print("divs is: ", divs)
-    prd = abs(prd[0:14])#-mar)
     
-    print("fin",prd)
+    prd = model.predict(df)
+    prd = abs(prd[0:14])
     newprd = prd.tolist()
-    #print(newprd)
-
-
-    return flask.render_template("index.html", data = newprd)
-    
-    
-    
     
-# start the flask app, allow remote connections 
-if __name__ == "__main__":
-    app.run(host='0.0.0.0', port=8000)
+    return flask.render_template("index.html", data=newprd)