wchynto commited on
Commit
2269928
1 Parent(s): bf503c1

feat : add flask

Browse files
Dockerfile ADDED
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+ FROM python:3.9
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+
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+ RUN useradd -m -u 1000 user
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+ USER user
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+ ENV PATH="/home/user/.local/bin:$PATH"
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+
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+ WORKDIR /app
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+
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+ COPY --chown=user ./requirements.txt requirements.txt
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+ RUN pip install --no-cache-dir --upgrade -r requirements.txt
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+
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+ COPY --chown=user . /app
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+
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+ CMD [ "python", "-m" , "flask", "run", "--host=0.0.0.0", "--port", "7860"]
app.py ADDED
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+ from flask import Flask, render_template
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+ import pandas as pd
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+ import matplotlib.pyplot as plt
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+ import datetime
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+ from sklearn.model_selection import train_test_split
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+ from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error, r2_score, mean_absolute_error
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+ from sklearn.linear_model import LinearRegression
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+ import os
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+
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+ app = Flask(__name__)
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+
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+ def create_lagged_features(data, lags):
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+ features = pd.DataFrame()
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+ for lag in range(1, lags + 1):
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+ features[f'Lag_{lag}'] = data.shift(lag)
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+ return features
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+
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+ df = pd.read_csv('./dataset/BBCA-History.csv')
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+ df = df = df.reindex(index=df.index[::-1])
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+
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+ df_display = df
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+
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+ df['Tanggal'] = pd.to_datetime(df['Tanggal'], dayfirst=True)
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+ df['Perubahan%'] = df['Perubahan%'].str.replace('%', '').str.replace(',', '.').astype(float)
25
+ df['Perubahan%'] = df['Perubahan%'].astype(float)
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+ df['Vol.'] = df['Vol.'].str.replace('M', 'e6').str.replace('B', 'e9').str.replace(',', '.').astype(float)
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+
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+ # Calculate 30-day moving average
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+ df['MA30'] = df['Terakhir'].rolling(window=30).mean()
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+
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+ # Generate the plot
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+ plt.figure(figsize=(10, 6))
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+ plt.plot(df['Tanggal'], df['Pembukaan'], label='Harga Pembukaan')
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+ plt.plot(df['Tanggal'], df['Terakhir'], label='Harga Terakhir')
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+ plt.plot(df['Tanggal'], df['MA30'], label='MA 30', linestyle='--', color='red')
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+ plt.xlabel('Tanggal')
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+ plt.ylabel('Harga')
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+ plt.title('Pergerakan Harga Pembukaan dan Terakhir dengan MA 30')
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+ plt.legend()
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+
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+ # Save the plot to a static file
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+ plot_path = os.path.join('static', 'plot.png')
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+ plt.savefig(plot_path)
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+ plt.close()
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+
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+ selected_features = ['Tanggal', 'Pembukaan']
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+ df = df[selected_features]
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+
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+ df.set_index('Tanggal', inplace=True)
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+
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+ lags = 90
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+ lagged_features = create_lagged_features(df['Pembukaan'], lags)
53
+ df_with_lags = pd.concat([df, lagged_features], axis=1).dropna()
54
+
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+ X = df_with_lags[[f'Lag_{i}' for i in range(1, lags + 1)]]
56
+ y = df_with_lags['Pembukaan']
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+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
58
+
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+ model = LinearRegression()
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+ model.fit(X_train, y_train)
61
+
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+ y_pred = model.predict(X_test)
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+
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+ plt.figure(figsize=(10, 6))
65
+ plt.plot(y_test.index, y_test, label='Actual Values')
66
+ plt.plot(y_test.index, y_pred, label='Predicted Values', linestyle='--')
67
+ plt.xlabel('Tanggal')
68
+ plt.ylabel('Harga Pembukaan')
69
+ plt.title('Actual vs Predicted Values')
70
+ plt.legend()
71
+
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+ # Save the plot to a static file
73
+ plot_training_path = os.path.join('static', 'plot_training.png')
74
+ plt.savefig(plot_training_path)
75
+ plt.close()
76
+
77
+ mse = mean_squared_error(y_test, y_pred)
78
+ mae = mean_absolute_error(y_test, y_pred)
79
+ mape = mean_absolute_percentage_error(y_test, y_pred)
80
+ r2 = r2_score(y_test, y_pred)
81
+
82
+ last_date = y_test.index[-1]
83
+ next_date = last_date + datetime.timedelta(days=1)
84
+
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+ latest_lags = X.iloc[-1].values.reshape(1, -1)
86
+ next_value = model.predict(latest_lags)
87
+
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+ @app.route('/')
89
+ def index():
90
+ table_html = df_display.to_html(classes='table table-striped', index=False)
91
+
92
+ return render_template('index.html', table=table_html, plot_url=plot_path)
93
+
94
+ @app.route('/predict')
95
+ def predict():
96
+
97
+ return render_template('predict.html', plot_training_url=plot_training_path, next_date=next_date.date(), next_value=next_value[0], mse=mse, mae=mae, mape=mape, r2=r2)
98
+
99
+ if __name__ == '__main__':
100
+ app.run(debug=True)
dataset/BBCA-History.csv ADDED
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+ "Tanggal","Terakhir","Pembukaan","Tertinggi","Terendah","Vol.","Perubahan%"
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1150
+ "03/03/2020","6.320","6.200","6.350","6.200","114,66M","3,95%"
1151
+ "02/03/2020","6.080","6.290","6.290","6.035","144,87M","-3,34%"
1152
+ "28/02/2020","6.290","6.200","6.290","6.035","169,95M","0,00%"
1153
+ "27/02/2020","6.290","6.470","6.475","6.200","129,25M","-2,02%"
1154
+ "26/02/2020","6.420","6.500","6.535","6.420","92,82M","-1,68%"
1155
+ "25/02/2020","6.530","6.550","6.560","6.510","86,45M","0,08%"
1156
+ "24/02/2020","6.525","6.565","6.600","6.510","84,91M","-1,36%"
1157
+ "21/02/2020","6.615","6.600","6.660","6.595","98,26M","0,30%"
1158
+ "20/02/2020","6.595","6.690","6.695","6.565","136,72M","-1,49%"
1159
+ "19/02/2020","6.695","6.665","6.760","6.665","74,59M","0,00%"
1160
+ "18/02/2020","6.695","6.685","6.720","6.670","98,44M","-0,37%"
1161
+ "17/02/2020","6.720","6.680","6.750","6.680","115,32M","0,60%"
1162
+ "14/02/2020","6.680","6.780","6.795","6.675","147,03M","-1,62%"
1163
+ "13/02/2020","6.790","6.820","6.820","6.740","49,74M","-0,15%"
1164
+ "12/02/2020","6.800","6.800","6.800","6.760","69,97M","0,29%"
1165
+ "11/02/2020","6.780","6.785","6.785","6.745","77,53M","-0,07%"
1166
+ "10/02/2020","6.785","6.760","6.785","6.630","66,58M","0,37%"
1167
+ "07/02/2020","6.760","6.710","6.780","6.705","82,61M","0,30%"
1168
+ "06/02/2020","6.740","6.740","6.780","6.680","154,69M","0,15%"
1169
+ "05/02/2020","6.730","6.780","6.780","6.650","123,00M","1,97%"
1170
+ "04/02/2020","6.600","6.570","6.620","6.520","113,32M","2,48%"
1171
+ "03/02/2020","6.440","6.480","6.585","6.370","149,80M","-0,62%"
1172
+ "31/01/2020","6.480","6.800","6.805","6.480","163,14M","-3,86%"
1173
+ "30/01/2020","6.740","6.845","6.845","6.730","71,99M","-0,66%"
1174
+ "29/01/2020","6.785","6.800","6.845","6.770","43,82M","-0,07%"
1175
+ "28/01/2020","6.790","6.835","6.845","6.755","82,62M","-0,73%"
1176
+ "27/01/2020","6.840","6.865","6.940","6.815","39,68M","0,44%"
1177
+ "24/01/2020","6.810","6.820","6.880","6.810","69,65M","-0,44%"
1178
+ "23/01/2020","6.840","6.885","6.895","6.830","87,73M","0,00%"
1179
+ "22/01/2020","6.840","6.855","6.895","6.835","94,31M","0,15%"
1180
+ "21/01/2020","6.830","6.885","6.895","6.825","103,98M","-0,07%"
1181
+ "20/01/2020","6.835","6.950","6.975","6.830","93,55M","-0,58%"
1182
+ "17/01/2020","6.875","6.840","6.890","6.800","38,25M","0,36%"
1183
+ "16/01/2020","6.850","6.805","6.890","6.795","88,93M","0,22%"
1184
+ "15/01/2020","6.835","6.870","7.060","6.800","75,62M","-0,51%"
1185
+ "14/01/2020","6.870","6.745","6.885","6.740","93,58M","1,85%"
1186
+ "13/01/2020","6.745","6.740","6.760","6.735","67,86M","0,30%"
1187
+ "10/01/2020","6.725","6.765","6.825","6.710","73,36M","-0,22%"
1188
+ "09/01/2020","6.740","6.740","6.755","6.730","40,36M","0,90%"
1189
+ "08/01/2020","6.680","6.670","6.745","6.670","53,69M","-0,89%"
1190
+ "07/01/2020","6.740","6.735","6.770","6.730","45,02M","0,07%"
1191
+ "06/01/2020","6.735","6.720","6.750","6.690","27,30M","-0,96%"
1192
+ "03/01/2020","6.800","6.750","6.800","6.725","47,76M","1,64%"
1193
+ "02/01/2020","6.690","6.695","6.780","6.680","49,45M","0,07%"
requirements.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ Flask
2
+ pandas
3
+ matplotlib
4
+ scikit-learn
static/plot.png ADDED
static/plot_training.png ADDED
templates/index.html ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <meta charset="UTF-8">
5
+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
6
+ <title>IMPHNEN</title>
7
+ <style>
8
+ table {
9
+ border-collapse: collapse;
10
+ width: 100%;
11
+ border: 1px solid black; /* Add this line */
12
+ }
13
+
14
+ th, td {
15
+ border: 1px solid black;
16
+ padding: 8px;
17
+ text-align: left;
18
+ }
19
+
20
+ th {
21
+ background-color: #f2f2f2;
22
+ }
23
+
24
+ tr:nth-child(even) {
25
+ background-color: #f2f2f2;
26
+ }
27
+
28
+ .table-container {
29
+ max-height: 400px;
30
+ overflow-y: auto;
31
+ }
32
+ </style>
33
+ </head>
34
+ <body>
35
+ <h1>Forecasting Harga Pembukaan Saham BCA (BBCA) dengan Linear Regression Univariate</h1>
36
+ <p>Data terakhir diambil tanggal : 2024-11-29</p>
37
+ <a href="/predict">Lihat prediksi tanggal berikutnya</a>
38
+ <div class="container my-5">
39
+ <h2>DataFrame Display</h2>
40
+ <div class="table-container">
41
+ {{ table|safe }}
42
+ </div>
43
+
44
+ <div class="container my-5">
45
+ <h2>Pergerakan Harga MA 30</h2>
46
+ <img src="{{ plot_url }}" alt="Plot">
47
+ </div>
48
+ </div>
49
+ </body>
50
+ </html>
templates/predict.html ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <!DOCTYPE html>
2
+ <html lang="en">
3
+ <head>
4
+ <meta charset="UTF-8">
5
+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
6
+ <title>IMPHNEN</title>
7
+ </head>
8
+ <body>
9
+ <h1>Hasil Forecasting Harga Pembukaan Saham BCA (BBCA) dengan Linear Regression Univariate<</h1>
10
+ <a href="/">Kembali halaman utama</a>
11
+ <div class="container my-5">
12
+ <h2>Hasil Prediksi</h2>
13
+ <p>Harga Pembukaan Saham BCA (BBCA) pada tanggal {{ next_date }} adalah sebesar Rp {{ next_value }}</p>
14
+ </div>
15
+ <div class="container my-5">
16
+ <h2>Grafik Training Data</h2>
17
+ <img src="{{ plot_training_url }}" alt="">
18
+ </div>
19
+ <div class="container my-5">
20
+ <h2>Score Evaluasi</h2>
21
+ <ul>
22
+ <li>
23
+ <strong>Mean Absolute Error (MAE):</strong> {{ mae }}
24
+ </li>
25
+ <li>
26
+ <strong>Mean Squared Error (MSE):</strong> {{ mse }}
27
+ </li>
28
+ <li>
29
+ <strong>Root Mean Squared Error (RMSE):</strong> {{ rmse }}
30
+ </li>
31
+ <li>
32
+ <strong>Coefficient of Determination (R^2):</strong> {{ r2 }}
33
+ </li>
34
+ <li>
35
+ <strong>Mean Absolute Percentage Error (MAPE):</strong> {{ mape }}
36
+ </li>
37
+ </ul>
38
+ </div>
39
+ </body>
40
+ </html>