Aggiunta dashboard Admin

data/README.md

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+## Dati 
+In questa directory sarà presente il dataset di progetto e altri file utili per la preparazione del dataset.

data/data_preparation/README.md

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+## Preparazione dei dati
+In questa directory saranno presenti statistiche, immagini, text utili sui dati.

data/data_preparation/data_preparation.py

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+#######################################
+# 4.3 Preparazione dei dati
+#######################################
+
+# Librerie utili per l'analisi dei dati
+import pandas as pd
+import numpy as np
+import matplotlib
+import seaborn as sns
+from numpy.random.mtrand import seed
+
+# Configurazione dello stile dei grafici
+sns.set(context='notebook', style='darkgrid', palette='colorblind', font='sans-serif', font_scale=1, rc=None)
+matplotlib.rcParams['figure.figsize'] =[8,8]
+matplotlib.rcParams.update({'font.size': 15})
+matplotlib.rcParams['font.family'] = 'sans-serif'
+
+
+# Configurazione del dataset e suddivisione dei dati in training e test set
+def data_split(data, ratio):
+    np.random.seed(42)
+    shuffled = np.random.permutation(len(data))
+    test_set_size = int(len(data) * ratio)
+    test_indices = shuffled[:test_set_size]
+    train_indices = shuffled[test_set_size:]
+    return data.iloc[train_indices], data.iloc[test_indices]
+
+# main
+if __name__== "__main__":
+
+    # Lettura dei dati e suddivisione dei dati con ratio 0.2
+    covid = pd.read_csv('data/data.csv')
+    train, test = data_split(covid, 0.2)
+    X_train = train[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
+       'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
+       'Heart Disease', 'Diabetes', 'Hyper Tension', 'Fatigue ',
+       'Gastrointestinal ', 'Abroad travel', 'Contact with COVID Patient',
+       'Attended Large Gathering', 'Visited Public Exposed Places',
+       'Family working in Public Exposed Places', 'Wearing Masks',
+       'Sanitization from Market']].to_numpy()
+
+    X_test = test[['Breathing Problem', 'Fever', 'Dry Cough', 'Sore throat',
+       'Running Nose', 'Asthma', 'Chronic Lung Disease', 'Headache',
+       'Heart Disease', 'Diabetes', 'Hyper Tension', 'Fatigue ',
+       'Gastrointestinal ', 'Abroad travel', 'Contact with COVID Patient',
+       'Attended Large Gathering', 'Visited Public Exposed Places',
+       'Family working in Public Exposed Places', 'Wearing Masks',
+       'Sanitization from Market']].to_numpy()
+
+    Y_train = train[['COVID-19']].to_numpy().reshape(4348,)
+    Y_test = test[['COVID-19']].to_numpy().reshape(1086,)
+
+# Stampa delle informazioni del dataframe, inclusi l'indice dtype e le colonne, i valori non null e l'utilizzo della memoria.
+# Scrittura delle informazioni su file.txt
+with open('data/data_preparation/info.txt', 'w') as f:
+    covid.info(buf=f)
+
+# Genera statistiche descrittive
+covid.describe().to_csv("data/data_preparation/dataset_statics.csv") # salvataggio su un file.csv per renderlo leggibile
+
+
+## 4.3.1 Pulizia dei dati ##
+# Verifica dei dati mancanti
+missing_values=covid.isnull().sum() # valori mancanti
+percent_missing = covid.isnull().sum()/covid.shape[0]*100 # valori mancanti %
+value = {
+    'missing_values ':missing_values,
+    'percent_missing %':percent_missing  
+}
+frame=pd.DataFrame(value)
+frame.to_csv('data/data_preparation/missing_value.csv') # salvataggio su un file.csv per renderlo leggibile
+
+
+
+## 4.3.2 Visualizzazione dei dati ##
+# - Il codice è commentato per evitare la sovrascrittura dei file -
+
+# COVID-19
+# sns_plot = sns.countplot(x='COVID-19', data=covid)
+# figure = sns_plot.get_figure()
+# figure.savefig('data/data_preparation/data_viz/COVID-19.png', dpi = 400)
+
+# Breathing Problem 
+# sns_breathing = sns.countplot(x='Breathing Problem',hue='COVID-19',data=covid)
+# figure1 = sns_breathing.get_figure()
+# figure1.savefig('data/data_preparation/data_viz/BreathingProblem.png', dpi = 400)
+
+# Fever 
+# sns_fever = sns.countplot(x='Fever', hue='COVID-19', data=covid)
+# figure2 = sns_fever.get_figure()
+# figure2.savefig('data/data_preparation/data_viz/Fever.png', dpi = 400)
+
+# Dry Cough
+# sns_dry = sns.countplot(x='Dry Cough',hue='COVID-19',data=covid)
+# figure3 = sns_dry.get_figure()
+# figure3.savefig('data/data_preparation/data_viz/dry.png', dpi = 400)
+
+# Sore Throat
+# sns_sore = sns.countplot(x='Sore throat',hue='COVID-19',data=covid)
+# figure4 = sns_sore.get_figure()
+# figure4.savefig('data/data_preparation/data_viz/sore.png', dpi = 400)
+
+
+
+
+

data/data_preparation/dataset_statics.csv

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+,Breathing Problem,Fever,Dry Cough,Sore throat,Running Nose,Asthma,Chronic Lung Disease,Headache,Heart Disease,Diabetes,Hyper Tension,Fatigue ,Gastrointestinal ,Abroad travel,Contact with COVID Patient,Attended Large Gathering,Visited Public Exposed Places,Family working in Public Exposed Places,Wearing Masks,Sanitization from Market,COVID-19
+count,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0,5434.0
+mean,0.6661759293338241,0.7863452337136547,0.7926021347073978,0.7274567537725433,0.5432462274567538,0.4626426205373574,0.47202797202797203,0.5034965034965035,0.4642988590357011,0.4762605815237394,0.49006256900993744,0.5191387559808612,0.4694516010305484,0.45104895104895104,0.5016562384983437,0.4619065145380935,0.5189547294810453,0.41626794258373206,0.0,0.0,0.8065881486934119
+std,0.4716211327739574,0.4099235665965471,0.40548026751388566,0.44530878904756294,0.49817209342489355,0.4986483574853146,0.4992628934027898,0.5000337861645077,0.4987696953792399,0.4994820831364206,0.4999472415983911,0.49967955300634165,0.4991118498366407,0.49764381725600054,0.5000432695898289,0.4985926537423099,0.4996865689546946,0.49298444924353374,0.0,0.0,0.39500939378839356
+min,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+25%,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
+50%,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0
+75%,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0
+max,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0

data/data_preparation/info.txt

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+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 5434 entries, 0 to 5433
+Data columns (total 21 columns):
+ #   Column                                   Non-Null Count  Dtype
+---  ------                                   --------------  -----
+ 0   Breathing Problem                        5434 non-null   int64
+ 1   Fever                                    5434 non-null   int64
+ 2   Dry Cough                                5434 non-null   int64
+ 3   Sore throat                              5434 non-null   int64
+ 4   Running Nose                             5434 non-null   int64
+ 5   Asthma                                   5434 non-null   int64
+ 6   Chronic Lung Disease                     5434 non-null   int64
+ 7   Headache                                 5434 non-null   int64
+ 8   Heart Disease                            5434 non-null   int64
+ 9   Diabetes                                 5434 non-null   int64
+ 10  Hyper Tension                            5434 non-null   int64
+ 11  Fatigue                                  5434 non-null   int64
+ 12  Gastrointestinal                         5434 non-null   int64
+ 13  Abroad travel                            5434 non-null   int64
+ 14  Contact with COVID Patient               5434 non-null   int64
+ 15  Attended Large Gathering                 5434 non-null   int64
+ 16  Visited Public Exposed Places            5434 non-null   int64
+ 17  Family working in Public Exposed Places  5434 non-null   int64
+ 18  Wearing Masks                            5434 non-null   int64
+ 19  Sanitization from Market                 5434 non-null   int64
+ 20  COVID-19                                 5434 non-null   int64
+dtypes: int64(21)
+memory usage: 891.6 KB

data/data_preparation/missing_value.csv

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+,missing_values ,percent_missing %
+Breathing Problem,0,0.0
+Fever,0,0.0
+Dry Cough,0,0.0
+Sore throat,0,0.0
+Running Nose,0,0.0
+Asthma,0,0.0
+Chronic Lung Disease,0,0.0
+Headache,0,0.0
+Heart Disease,0,0.0
+Diabetes,0,0.0
+Hyper Tension,0,0.0
+Fatigue ,0,0.0
+Gastrointestinal ,0,0.0
+Abroad travel,0,0.0
+Contact with COVID Patient,0,0.0
+Attended Large Gathering,0,0.0
+Visited Public Exposed Places,0,0.0
+Family working in Public Exposed Places,0,0.0
+Wearing Masks,0,0.0
+Sanitization from Market,0,0.0
+COVID-19,0,0.0

init_db.py

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+import sqlite3
+
+connection = sqlite3.connect('selftestcovid19.db')
+with open('selftestcovid19.sql') as f:
+    connection.executescript(f.read())
+connection.commit()
+connection.close()

main.py

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+#################################################
+# 4.6 Distribuzione del modello
+#################################################
+from multiprocessing import connection
+import sqlite3
+from flask import Flask, render_template, redirect, request
+app = Flask(__name__)
+import pickle
+
+# Apertura del model.pkl per estrarre i dati passati da myTraining.py
+file = open('model.pkl', 'rb')
+clf = pickle.load(file)
+file.close()
+
+# Applicazione FLASK
+@app.route('/', methods=["GET", "POST"])
+def covid_checker():
+    if request.method == "POST":
+        myDict = request.form
+        breating = int(myDict['Breathing Problem'])
+        fever = int(myDict['Fever'])
+        dry = int(myDict['Dry Cough'])
+        sore = int(myDict['Sore throat'])
+        running = int(myDict['Running Nose'])
+        asthma = int(myDict['Asthma'])
+        chronic = int(myDict['Chronic Lung Disease'])
+        headache = int(myDict['Headache'])
+        heart = int(myDict['Heart Disease'])
+        diabetes = int(myDict['Diabetes'])
+        hyper = int(myDict['Hyper Tension'])
+        fatigue = int(myDict['Fatigue '])
+        gastrointestinal = int(myDict['Gastrointestinal '])
+        abroad = int(myDict['Abroad travel'])
+        contact = int(myDict['Contact with COVID Patient'])
+        attended = int(myDict['Attended Large Gathering'])
+        visited = int(myDict['Visited Public Exposed Places'])
+        family = int(myDict['Family working in Public Exposed Places'])
+        wearing = int(myDict['Wearing Masks'])
+        sanitization = int(myDict['Sanitization from Market'])
+        # Inferenza
+        inputFeatures = [breating, fever, dry, sore, running, asthma, chronic, headache,
+                heart, diabetes, hyper, fatigue, gastrointestinal, abroad, contact, 
+                attended, visited, family, wearing, sanitization]
+        infProb =clf.predict_proba([inputFeatures])[0][1]
+
+        # Verifica del risultato
+        if infProb >= 0 and infProb <= 0.50:
+            str1 = " Pertanto la tua situazione non desta preoccupazione verso l'infezione al SARS-CoV-2 (COVID-19). \
+            Se comunque la preoccupazione sussiste è bene chiamare il proprio medico di fiducia/famiglia per informazioni \
+                più dettagliate." 
+            return render_template('show.html', inf = round((infProb*100), 0), text = str1)
+        elif infProb > 0.50 and infProb <= 0.75:
+            str2 = " Pertanto la tua situazione è dubbia, riprova a fare il test oppure chiama il tuo medico di famiglia, \
+                il tuo pediatra o la guardia medica per avere informazioni più dettagliate. Prenota un tampone RAPIDO presso una farmacia \
+                    nei pressi della tua zona."
+            return render_template('showRapido.html', inf = round((infProb*100), 0), text = str2)
+        elif infProb > 0.75 and infProb <= 1:
+            str3 = " Pertanto la tua situazione suscita preoccupazione e per il test sei stato infettato dal SARS-CoV-2 (COVID-19). \
+                Tuttavia, recati SOLO in farmacia per la prenotazione di un tampone MOLECOLARE e/o chiami al telefono il tuo medico di famiglia, \
+                il tuo pediatra o la guardia medica. Oppure chiami il Numero Verde regionale oppure ancora al Numero di Pubblica utilità: 1500."
+            return render_template('showMolecolare.html', inf = round((infProb*100), 0), text = str3) 
+    return render_template('index.html')
+
+@app.route('/About', methods=["GET", "POST"])
+def About():
+    return render_template('About.html')
+
+@app.route('/loginAdmin', methods=["GET", "POST"])
+def loginAdmin():
+    return render_template('loginAdmin.html')
+
+@app.route('/dashboardAdmin', methods=["GET", "POST"])
+def dashAdmin():
+    connection = sqlite3.connect('selftestcovid19.db')
+    connection.row_factory = sqlite3.Row
+    farmacie = connection.execute('SELECT * FROM Farmacie').fetchall()
+    connection.close()
+    return render_template('dashboardAdmin.html', farmacie=farmacie)
+
+@app.route('/creaFarmacia', methods=["GET", "POST"])
+def creaFarmacia():
+    if request.method == "POST":
+        NomeFarmacia = request.form['NomeFarmacia']
+        Citta = request.form['Citta']
+        CAP = request.form['CAP']
+        Email = request.form['Email']
+        PWD = request.form['PWD']
+        connection = sqlite3.connect('selftestcovid19.db')
+        connection.row_factory = sqlite3.Row
+        connection.execute('INSERT INTO Farmacie (NomeFarmacia, Citta, CAP, Email, PWD) VALUES (?,?,?,?,?)', (NomeFarmacia,Citta,CAP,Email,PWD))
+        connection.commit()
+        connection.close()
+        return redirect('/dashboardAdmin')
+    return render_template('creaFarmacia.html')
+
+@app.route('/ricercaFarmacia', methods=["GET", "POST"])
+def ricercaFarmacia():
+    if request.method == "POST":
+        Citta = request.form['Citta']
+        CAP = request.form['CAP']
+        connection = sqlite3.connect('selftestcovid19.db')
+        connection.row_factory = sqlite3.Row
+        farmacie = connection.execute('SELECT * FROM Farmacie WHERE (Citta = ? OR CAP = ?)', (Citta,CAP)).fetchall()
+        connection.close()
+        return render_template('/listaFarmacie.html', farmacie=farmacie)
+    return render_template('ricercaFarmacia.html')
+
+@app.route('/modificaFarmacia', methods=["GET", "POST"])
+def modificaFarmacia():
+    connection = sqlite3.connect('selftestcovid19.db')
+    connection.row_factory = sqlite3.Row
+    farmacie = connection.execute('SELECT * FROM Farmacie').fetchall()
+    connection.close()
+    return render_template('modificaFarmacia.html',farmacie=farmacie)
+
+@app.route('/rimozioneFarmacia', methods=["GET", "POST"])
+def rimozioneFarmacia():
+    connection = sqlite3.connect('selftestcovid19.db')
+    connection.row_factory = sqlite3.Row
+    farmacie = connection.execute('SELECT * FROM Farmacie').fetchall()
+    connection.close()
+    return render_template('rimozioneFarmacia.html',farmacie=farmacie)
+
+@app.route('/<int:ID>/rimuoviFarmacia', methods=["POST"])
+def rimuoviFarmacia(ID):
+    connection = sqlite3.connect('selftestcovid19.db')
+    connection.row_factory = sqlite3.Row
+    farmacie = connection.execute('DELETE FROM Farmacie WHERE ID = ?', (ID,))
+    connection.commit()
+    connection.close()
+    return redirect('/dashboardAdmin')
+
+@app.route('/<int:ID>/aggiornaFarmacia', methods=["POST"])
+def aggiornaFarmacia(ID):
+    if request.method == "POST":
+        NomeFarmacia = request.form['NomeFarmacia']
+        Citta = request.form['Citta']
+        CAP = request.form['CAP']
+        Email = request.form['Email']
+        PWD = request.form['PWD']
+        connection = sqlite3.connect('selftestcovid19.db')
+        connection.row_factory = sqlite3.Row
+        farmacie = connection.execute('UPDATE Farmacie SET NomeFarmacia = ?, Citta = ?, CAP = ?, Email = ?, PWD = ? WHERE ID = ?', (NomeFarmacia,Citta,CAP,Email,PWD,ID))
+        connection.commit()
+        connection.close()
+        return redirect('/dashboardAdmin')
+    return redirect('/dashboardAdmin')
+
+# main
+if __name__=="__main__":
+    app.run(debug=True)