"Added model files"

Dockerfile

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+# Use a Python base image
+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /code
+
+# Copy the required files to the working directory
+COPY app.py .
+COPY ./templates/index.html /code/templates/index.html 
+COPY ./requirements.txt /code/requirements.txt
+COPY model_AAPL.h5 .
+COPY model_AMZN.h5 .
+COPY model_TSLA.h5 .
+
+# Install the required packages
+RUN pip install --no-cache-dir -r /code/requirements.txt
+
+# Expose the port that the Flask app will run on
+EXPOSE 5000
+
+# Start the Flask app
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, render_template, request
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+import yfinance as yf
+import pickle
+
+app = Flask(__name__)
+
+# Define tickers and load models
+tickers = ['AMZN', 'TSLA', 'AAPL']
+models = {}
+for ticker in tickers:
+    model = tf.keras.models.load_model(f'model_{ticker}.h5')
+    models[ticker] = model
+
+with open('min_max.pickle', 'rb') as handle:
+    min_max_scaling = pickle.load(handle)
+
+# Function to prepare the data for model input
+def prepare_data(data):
+    # Assuming the data is a 1D array of closing prices
+    # Reshape the data to have the shape (batch_size, timesteps, features)
+    data = np.array(data)
+    data = data.reshape(1, data.shape[0], 1)
+    return data
+
+# Function to get the last 60 days data for a ticker
+def get_last_60_days_data(ticker):
+    # Define the end date as yesterday
+    end_date = pd.Timestamp.today() - pd.Timedelta(days=1)
+    # Define the start date as 120 days before the end date
+    start_date = end_date - pd.Timedelta(days=120)
+
+    # Fetch the stock data using yfinance
+    stock_data = yf.download(ticker, start=start_date, end=end_date, progress=False)
+
+    # Ensure we have enough data (at least 60 days)
+    if len(stock_data) < 60:
+        return None
+
+    # Extract the last 60 days 'Close' prices from the stock data
+    last_60_days_data = stock_data['Close'].tolist()[-60:]
+
+    last_60_days_data = (last_60_days_data - min_max_scaling[ticker][0])/(min_max_scaling[ticker][1] - min_max_scaling[ticker][0])
+
+    return last_60_days_data.tolist()
+
+# Function to predict the next day closing value using the model
+def predict_next_day(ticker, data):
+    model = models[ticker]
+    data = prepare_data(data)
+    prediction = model.predict(data)
+    return prediction[0]
+
+def scale_back_data(data,ticker):
+    data = np.array(data)
+    data = data * (min_max_scaling[ticker][1] - min_max_scaling[ticker][0]) + min_max_scaling[ticker][0]
+    return data.tolist()
+
+# @app.route('/')
+# def hello_world():
+#    return "Hello World"
+
+# Flask route to handle the main page
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        selected_ticker = request.form['ticker']
+        last_60_days_data = get_last_60_days_data(selected_ticker)
+        last_60_days_data_original = get_last_60_days_data(selected_ticker)
+        predictions = predict_next_day(selected_ticker, last_60_days_data)
+        # for _ in range(10):
+        #     next_day_prediction = predict_next_day(selected_ticker, last_60_days_data)
+        #     predictions.append(next_day_prediction)
+        #     last_60_days_data.append(next_day_prediction)
+        #     last_60_days_data.pop(0)
+        predictions = scale_back_data(predictions,selected_ticker)
+        last_60_days_data_original = scale_back_data(last_60_days_data_original,selected_ticker)
+        return render_template('index.html', tickers=tickers, selected_ticker=selected_ticker, predictions=predictions, last_60_days_data=last_60_days_data_original)
+    else:
+        return render_template('index.html', tickers=tickers, selected_ticker=None, predictions=None, last_60_days_data=None)
+
+if __name__ == '__main__':
+    app.run()

model_AAPL.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a68a119cd48925080649e2f2aba403abeacdc232680b988ca635d21d551e43ed
+size 746232

model_AMZN.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4d1ea08fbc4894f499cfab2ced61c5f7c2975ba719a21cf153bd536a180d2c98
+size 746208

model_TSLA.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a22b259dfbf9d6b60967758b1bcda39ede97538aace25b75b744a489f76cb7c4
+size 746208

requirements.txt

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+flask
+plotly
+yfinance
+pandas
+tensorflow
+numpy

templates/index.html

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+<!DOCTYPE html>
+<html>
+<head>
+    <title>Stock Price Prediction</title>
+    <!-- Add Plotly.js library -->
+    <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
+</head>
+<body>
+    <h1>Stock Price Prediction</h1>
+    <h3>A demonstration of docker and flask to predict a particular stock by selecting appropriate model.</h3>
+    <h4>Please select a ticker and click predict.</h4>
+    <form method="POST">
+        <select name="ticker">
+            {% for ticker in tickers %}
+            <option value="{{ ticker }}" {% if selected_ticker == ticker %}selected{% endif %}>{{ ticker }}</option>
+            {% endfor %}
+        </select>
+        <input type="submit" value="Predict">
+    </form>
+    {% if predictions and last_60_days_data %}
+    <div style="width: 80%; margin: auto;">
+        <div id="stockChart"></div>
+    </div>
+    <script>
+        // Flask variables
+        var predictions = {{predictions}};
+        var last_60_days_data = {{last_60_days_data}};
+        
+        
+        // Create the Plotly chart
+        var trace1 = {
+            x: Array.from({ length: 60 }, (_, i) => i + 1),
+            y: last_60_days_data,
+            type: 'scatter',
+            mode: 'lines',
+            name: 'Historical Data',
+            line: { color: 'rgba(54, 162, 235, 1)' }
+        };
+        var trace2 = {
+            x: Array.from({ length: 10 }, (_, i) => i + 61),
+            y: predictions,
+            type: 'scatter',
+            mode: 'lines',
+            name: 'Predictions',
+            line: { color: 'rgba(255, 99, 132, 1)', dash: 'dash' }
+        };
+        var data = [trace1, trace2];
+        var layout = {
+            xaxis: {
+                title: 'Days'
+            },
+            yaxis: {
+                title: 'Stock Price',
+                zeroline: true
+            },
+            legend: {
+                x: 0,
+                y: 1.0,
+                bgcolor: 'rgba(255, 255, 255, 0)',
+                bordercolor: 'rgba(255, 255, 255, 0)'
+            }
+        };
+        Plotly.newPlot('stockChart', data, layout);
+    </script>
+    {% endif %}
+</body>
+</html>