Upload 18 files

Csv/Brent_oil.csv

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Log_Connection/log_file.py

@@ -0,0 +1,14 @@
+import logging
+import os
+from datetime import datetime
+from Variable_artifects.artifact import LOG_ENTRY_DIR
+
+
+LOG_FILE = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
+logs_path = os.path.join(LOG_ENTRY_DIR, LOG_FILE)
+os.makedirs(logs_path, exist_ok=True)
+LOG_FILE_PATH = os.path.join(logs_path, LOG_FILE)
+
+logging.basicConfig(filename=LOG_FILE_PATH,
+                    format="[ %(asctime)s ] %(name)s - %(levelname)s - %(message)s",
+                    level=logging.INFO)

Support_module_dir/support_function_plot.py

@@ -0,0 +1,94 @@
+from datetime import datetime
+from Variable_artifects.artifact import PLOT_DIR, COMBINE_PLOT
+import os
+import matplotlib.pyplot as plt
+
+
+# function for plot prediction and rolling window(previous price)
+def combine_plot_function(dataframe_instance, predictions_instance):
+    try:
+        plt.figure(figsize=(13, 4))
+
+        # Scatter plot for the existing DataFrame
+        plt.plot(dataframe_instance['Date'],
+                 dataframe_instance['Price'],
+                 marker='o',
+                 label='Previous Rolling Window Prices')
+
+        # Adding a vertical dashed line at the end of the existing DataFrame
+        plt.axvline(x=dataframe_instance['Date'].max(),
+                    color='black',
+                    linestyle='--',
+                    label=None)
+
+        # Scatter plots for the prediction DataFrame after the dashed line
+        plt.plot(predictions_instance['Date'],
+                 predictions_instance['Lower_Bound'],
+                 marker='o',
+                 label='Predicted Lower Bound',
+                 color='red')
+
+        plt.plot(predictions_instance['Date'],
+                 predictions_instance['Upper_Bound'],
+                 marker='o',
+                 label='Predicted Upper Bound',
+                 color='green')
+
+        plt.plot(predictions_instance['Date'],
+                 predictions_instance['Mean_Price'],
+                 marker='o',
+                 label='Mean Price',
+                 color='blue')
+
+        # Shading the region between Lower Bound and Mean Price in red
+        plt.fill_between(predictions_instance['Date'],
+                         predictions_instance['Lower_Bound'],
+                         predictions_instance['Mean_Price'],
+                         color='red', alpha=0.3)
+
+        # Shading the region between Mean Price and Upper Bound in green
+        plt.fill_between(predictions_instance['Date'],
+                         predictions_instance['Mean_Price'],
+                         predictions_instance['Upper_Bound'],
+                         color='green', alpha=0.3)
+
+        # Connecting the last point of df_existing to the first point of df_new
+        plt.plot([dataframe_instance['Date'].max(), predictions_instance['Date'].min()],
+                 [dataframe_instance['Price'].iloc[-1], predictions_instance['Lower_Bound'].iloc[0]],
+                 linestyle='--', color='red')
+
+        plt.plot([dataframe_instance['Date'].max(), predictions_instance['Date'].min()],
+                 [dataframe_instance['Price'].iloc[-1], predictions_instance['Upper_Bound'].iloc[0]],
+                 linestyle='--', color='green')  # Separate line for Upper Bound
+
+        plt.plot([dataframe_instance['Date'].max(), predictions_instance['Date'].min()],
+                 [dataframe_instance['Price'].iloc[-1], predictions_instance['Mean_Price'].iloc[0]],
+                 linestyle='--', color='blue')  # Separate line for Mean Price
+
+        # Setting grid
+        plt.grid(True)
+
+        # Setting directory and path for plot Images
+        os.makedirs(PLOT_DIR, exist_ok=True)
+        plot_path = os.path.join(PLOT_DIR, COMBINE_PLOT)
+        plt.legend()
+        plt.xticks(rotation=45)
+        plt.savefig(plot_path)
+        plt.close()
+
+    except Exception as e:
+        print(f"Error in combine_plot_function: {e}")
+
+
+# class function 3: For date and time
+class DateTimeFunctionClass:
+    def __init__(self):
+        self.curr_date_time = datetime.now()
+        self.curr_date = self.curr_date_time.date()
+        self.curr_time = self.curr_date_time.strftime("%H_%M_%S")
+
+    def current_date(self):
+        return self.curr_date
+
+    def current_time(self):
+        return self.curr_time

Support_module_dir/support_function_predict.py

@@ -0,0 +1,59 @@
+import time
+import warnings
+from Variable_artifects.artifact import FORECAST_DAY
+import pandas as pd
+
+
+def predict_function(trained_model, dataset):
+    """
+    Function created for prediction
+
+    Parameters
+    ---------
+    trained_model : SARIMAX model
+        A trained SARIMAX model.
+    dataset : pandas DataFrame
+        A DataFrame containing the historical time series data.
+
+    Returns
+    -------
+    predict_frame : pandas Dataframe         with predicted values and corresponding dates.
+    execution_time : float
+        Execution time for making predictions.
+    """
+    try:
+        # Specify the number of steps to forecast
+        FORECAST_STEPS = FORECAST_DAY
+        PERIODS = FORECAST_STEPS + 1
+
+        # Suppress ValueWarning and FutureWarning
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=UserWarning)
+            warnings.simplefilter("ignore", category=FutureWarning)
+
+            # Measure execution time
+            start_time = time.time()
+
+            # making prediction:
+            forecast = trained_model.get_forecast(FORECAST_STEPS, dynamic=True)
+
+            # Measure execution time
+            execution_time = time.time() - start_time
+
+            # Access the confidence interval directly using 'conf_int'
+            confidence_interval = forecast.conf_int()
+
+            # Get the last date in the DataFrame
+            last_date = dataset.index[-2]
+
+            # Generate a new DataFrame with the dates for the next x number of steps
+            new_dates = pd.date_range(start=last_date, periods=PERIODS, freq='B')[
+                        1:]  # 'B' stands for business day), [1:] Exclude the last_date
+
+            # Print or use the confidence interval
+            predict_frame = pd.DataFrame({"Date": new_dates, "Lower_Bound": confidence_interval.iloc[:, 0],
+                                          "Upper_Bound": confidence_interval.iloc[:, 1]})
+            return predict_frame, execution_time
+
+    except Exception as e:
+        raise e

Variable_artifects/artifact.py

@@ -0,0 +1,36 @@
+# artifact for log file
+LOG_ENTRY_DIR = "Log_entry"
+
+# artifact for connection
+BUNDLE_NAME = "secure-connect-oil.zip"
+ROOT_PATH = "D:/msn/pycharm_projects/Brentprice/secure-connect-oil.zip"
+
+# artifact for csv file:)
+CSV_FILE = "Brent_oil.csv"
+CSV_DIR = "Csv"
+
+# app file artifact
+PLOT_DIR = "static"
+COMBINE_PLOT = "combined_price_plot.png"
+
+# Trained model directory:
+MODEL_FILENAME = "SARIMAX_FORCAST_MODEL.joblib"
+MAIN_MODEL_DIR = "Saved_Model_dir"
+
+# SARIMAX Model initialization
+ORDER = (1, 1, 1)
+SEASONAL_ORDER = (1, 1, 1, 7)
+
+# support module artifact
+FORECAST_DAY = 7  # give 7 days prediction customize time frame
+
+"""
+    Note: ORDER = (p, d, q) SEASONAL_ORDER = (P, D, Q, s)
+    #  P = 1   P: Autoregressive order at the seasonal frequency.
+    #  D = 1   D: Order of seasonal differencing.
+    #  Q = 1   Q: Moving average order at the seasonal frequency.
+    #  s = 7   s: Window size. (keep it small)
+    #  p = 1   p:(AutoRegressive order) if p=2, the model uses the two most recent observations to predict the current one.
+    #  d = 1   d:(Integrated order)
+    #  q = 1   q: the model includes the most recent forecast error in the prediction.
+"""

app.py

@@ -0,0 +1,59 @@
+# Import necessary libraries
+from flask import Flask, render_template
+from Variable_artifects.artifact import PLOT_DIR, COMBINE_PLOT
+from components.model_prediction import Prediction
+from Support_module_dir.support_function_plot import combine_plot_function
+import os
+
+
+# Function to load the model and make predictions
+def load_and_predict_model():
+    try:
+        saved_model_path = "Saved_Model_dir/2023-12-23_00_42_22/SARIMAX_FORCAST_MODEL.joblib"
+        prediction = Prediction(saved_model_path).model_prediction()  # called class Prediction
+        return prediction
+    except FileNotFoundError as file_error:
+        raise FileNotFoundError(f"Error loading model: {file_error}")
+    except Exception as e:
+        raise e
+
+
+# Call the function to load and predict
+predictions_instance, dataframe_instance = load_and_predict_model()
+
+# Reset the index and move it into a new column
+dataframe_instance = dataframe_instance[-7:].reset_index()
+
+# Assuming predictions_instance is your DataFrame
+predictions_instance['Mean_Price'] = (predictions_instance['Lower_Bound'] + predictions_instance['Upper_Bound']) / 2
+
+# calling plot function
+combine_plot_function(dataframe_instance, predictions_instance)
+
+# Initialize Flask app
+app = Flask(__name__)
+
+
+# Route for the home page
+@app.route('/', methods=['GET'])
+def home():
+    return render_template("about.html")
+
+
+# Route for the prediction page
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Setting directory and path for plot Images
+        plot_path = os.path.join(PLOT_DIR, COMBINE_PLOT)
+
+        return render_template('index.html', predictions=predictions_instance.to_dict(orient="records"),
+                               dataframe=dataframe_instance.to_dict(orient="records"),
+                               combine_plot=plot_path)
+    except Exception as e:
+        return render_template('error.html', error_message=str(e))
+
+
+# Run the app
+if __name__ == '__main__':
+    app.run()

requirements.txt

@@ -0,0 +1,10 @@
+cassandra-driver~=3.28.0
+python-dotenv~=0.21.0
+pandas~=2.0.3
+statsmodels~=0.14.0
+plotly~=5.9.0
+flask~=3.0.0
+requests
+jsonify
+joblib~=1.2.0
+matplotlib~=3.8.2

templates/about.html

@@ -0,0 +1,176 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <style>
+        body {
+            background-color: #313037;
+            color: white;
+            display: flex;
+            flex-direction: column;
+            align-items: stretch;
+            height: 100vh;
+            margin: 0;
+            overflow-x: hidden; /* Hide horizontal scrollbar */
+        }
+
+        .topnav {
+            background-color: black;
+            overflow: hidden;
+            position: fixed;
+            width: 100%;
+            height: 20%
+            z-index: 1000;
+        }
+
+        .topnav a {
+            float: left;
+            display: block;
+            color: white;
+            text-align: center;
+            padding: 14px 16px;
+            text-decoration: none;
+            font-size: 17px;
+        }
+
+        .topnav a:hover {
+            background-color: #808081;
+            color: white;
+        }
+
+        .main-content {
+            flex: 1;
+            padding: 20px;
+            margin-top: 70px; /* Adjust margin based on topnav height */
+        }
+
+        h2 {
+            margin-bottom: 20px;
+        }
+
+        table {
+            width: 100%;
+            height: 30%;
+        }
+
+        th, td {
+            border: 1px solid #ddd;
+            padding: 1px;
+            text-align: left;
+        }
+
+        .date-cell, .price-cell {
+            font-size: 24px; /* Set the font size for the cells */
+        }
+
+        form {
+            display: flex;
+            flex-direction: column; /* Display form items vertically */
+            margin-top: 20px; /* Add margin at the top */
+            flex: 1; /* Take remaining width */
+        }
+
+        fieldset {
+            font-size: 12px;
+            line-height: 2.5;
+            margin-bottom: 1px; /* Add margin at the bottom */
+            border-top: 5px solid black; /* Add a black strip at the top */
+            padding-top: 0.1px; /* Add padding to separate content from the strip */
+        }
+
+        input[type="submit"] {
+            padding: 15px 25px;
+            font-size: 20px;
+            border-radius: 6px;
+            border: none;
+            background-color: #86D7C4;
+            color: #FFF;
+            cursor: pointer;
+            transition: background-color 0.3s;
+        }
+
+        input[type="submit"]:hover {
+            background-color: black;
+        }
+
+        h1 {
+            font-size: 28px; /* Increase font size for heading elements */
+            color: white; /* custom color */
+            font-family: Arial
+        }
+
+        p {
+            font-size: 18px; /* Increase font size for paragraph elements */
+            font-family: Arial
+        }
+
+        li {
+            font-size: 18px; /* Increase font size for list elements */
+           font-family: Arial
+        }
+    </style>
+</head>
+<body>
+
+<div class="topnav">
+    <a href="D:/msn/pycharm_projects/price_forecast/templates/about.html" class="about-link">About</a>
+    <a href="https://public.tableau.com/app/profile/manoj.negi/viz/BRENT_PRICE_MOVEMENT/Dashboard1" class="dash-link">Dashboard</a>
+</div>
+
+<div class="main-content">
+    <form action="{{url_for('predict')}}" method="post">
+        <fieldset>
+           <legend>FORECAST BRENT PRICE</legend>
+            <br>
+            <input type="submit" value="Forecast">
+            <h2 style="font-family: Arial;">Click To Forecast 7 Days Price Movement</h2>
+
+
+
+            <h1> About The Project </h1>
+
+            <p><b>Purpose is to calculate the week-long prediction from the last date as updated in the database.
+            Forecast Prediction Window can be customized accordingly 7 days will give optimised result</b></p>
+
+            <p>
+
+                The Brent oil  Price Forecasting system is designed to address the increasing need for accurate predictions of  brent prices,
+                allowing stakeholders in the energy sector to make informed decisions. By leveraging historical data, market trends, and relevant factors.
+                The system aims to provide reliable forecasts, contributing to improved strategic planning and cost management.
+
+            </p>
+            <p>
+
+                Brent crude is known for its relatively low sulfur content and high API gravity, which makes it a light and sweet crude oil.
+                Light crude oils have a lower density and flow more easily, while sweet crude oils contain less sulfur,
+                making them less corrosive and easier to refine into high-quality products like gasoline.
+                Brent crude is a crucial benchmark for global oil prices and serves as a reference point for pricing two-thirds of the world's internationally traded crude oil supplies.
+                It is used in setting the price of many of the world's crude oil exports and helps determine the cost of gasoline and other refined products in various regions.
+                The Brent crude oil price is often quoted alongside the WTI crude oil price as a key indicator of the global oil market.
+
+            </p>
+            <h1> Dataset Description</h1>
+            <p> The Project is trained on the Dataset of previous 10 Years of price history of Brent oil.
+                <li>Dataset columns contain: Date, Price </li>
+                <li>Total Number of datapoints :2555</li>
+            </p>
+
+            <h1> Model Description </h1>
+            <p>
+                <li>The model taken for forecasting : Sarimax
+                    <li>Data classification: non-seasonal data.</li>
+                <li>MSE: 1.4 </li>
+                <li>Execution time :0.0047 approx</li>
+                <li>Model datapoints updated from Database : Cassandra</li>
+            </p>
+
+            <p>
+
+
+
+            </p>
+
+        </fieldset>
+    </form>
+</div>
+</body>
+</html>

templates/index.html

@@ -0,0 +1,170 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <style>
+        body {
+            background-color: #313037;
+            color: white;
+            display: flex;
+            flex-direction: column;
+            align-items: stretch;
+            height: 100vh;
+            margin: 0;
+            overflow-x: hidden; /* Hide horizontal scrollbar */
+        }
+
+        .topnav {
+            background-color: black;
+            overflow: hidden;
+            position: fixed;
+            width: 100%;
+            height: 20%
+            z-index: 1000;
+        }
+
+        .topnav a {
+            float: left;
+            display: block;
+            color: white;
+            text-align: center;
+            padding: 14px 16px;
+            text-decoration: none;
+            font-size: 17px;
+        }
+
+        .topnav a:hover {
+            background-color: #808081;
+            color: white;
+        }
+
+        .main-content {
+            flex: 1;
+            padding: 20px;
+            margin-top: 70px; /* Adjust margin based on topnav height */
+        }
+
+        h2 {
+            margin-bottom: 20px;
+        }
+
+        table {
+            width: 100%;
+            height: 30%;
+        }
+
+        th, td {
+            border: 1px solid #ddd;
+            padding: 0px;
+            text-align: center;
+        }
+
+        .date-cell, .price-cell {
+            font-size: 14px; /* Set the font size for the cells */
+        }
+
+        form {
+            display: flex;
+            flex-direction: column; /* Display form items vertically */
+            margin-top: 20px; /* Add margin at the top */
+            flex: 1; /* Take remaining width */
+        }
+
+        fieldset {
+            font-size: 12px;
+            line-height: 2.5;
+            margin-bottom: 1px; /* Add margin at the bottom */
+            border-top: 5px solid black; /* Add a black strip at the top */
+            padding-top: 0.1px; /* Add padding to separate content from the strip */
+        }
+
+        input[type="submit"] {
+            padding: 10px 20px;
+            font-size: 16px;
+            border-radius: 5px;
+            border: none;
+            background-color: #86D7C4;
+            color: #FFF;
+            cursor: pointer;
+            transition: background-color 0.3s;
+        }
+
+        input[type="submit"]:hover {
+            background-color: #808081;
+        }
+
+        #plot-image-historical, #plot-image-preds {
+            width: 100%;
+            height: 30%;
+        }
+    </style>
+</head>
+<body>
+
+<div class="topnav">
+    <a href="templates/about.html" class="about-link">About</a>
+    <a href="https://public.tableau.com/app/profile/manoj.negi/viz/BRENT_PRICE_MOVEMENT/Dashboard1" class="dash-link">Dashboard</a>
+</div>
+
+<div class="main-content">
+    <form action="{{url_for('predict')}}" method="post">
+        <fieldset>
+           <legend>FORECAST BRENT PRICE</legend>
+
+                    <table>
+                        <tr>
+                            <td>
+                                <table>
+
+                                    <tr>
+                                        <h2> PREVIOUS ROLLING WINDOW </h2>
+                                        <th>Date</th>
+                                        <th>Previous Price</th>
+                                    </tr>
+                                            {% for dataframe in dataframe %}
+                                    <tr>
+                                        <td class="date-cell">{{dataframe.Date}}</td>
+                                        <td class="price-cell">{{dataframe.Price}}</td>
+                                    </tr>
+                                            {% endfor %}
+                                 </table>
+                            </td>
+                            <td>
+                                <table>
+                                    <tr>
+                                        <h2> FORECAST : PRICE MOVEMENT </h2>
+                                    </tr>
+                                    <tr>
+                                        <td>
+                                            <img id="plot-image-preds" src="{{combine_plot}}" alt="Pred_Price Plot">
+                                        </td>
+
+                                    </tr>
+                                </table>
+
+                            </td>
+                        </tr>
+                     </table>
+                    <table>
+                        <tr>
+                            <h2> WEEK FORECAST</h2>
+                            <th>Date</th>
+                            <th>Predicted Lower Bound</th>
+                            <th>Predicted Upper Bound</th>
+                            <th> Mean Prediction</th>
+                        </tr>
+                        {% for prediction in predictions %}
+                        <tr>
+                            <td class="date-cell">{{ prediction.Date }}</td>
+                            <td class="price-cell" style="color: red;">{{'%.3f'|format(prediction.Lower_Bound)}}</td>
+                            <td class="price-cell" style="color: green;">{{'%.3f'|format(prediction.Upper_Bound)}}</td>
+                            <td class="price-cell">{{'%.3f'|format(prediction.Mean_Price)}}</td>
+                        </tr>
+                             {% endfor %}
+                        </table>
+             <br><br>
+            <input type="submit" value="Forecast"> <br>
+        </fieldset>
+    </form>
+</div>
+</body>
+</html>