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+# Use Python 3.10.12 as the base image
+FROM python:3.9.13
+
+# Set the working directory to /code
+WORKDIR /code
+
+# Copy the requirements file into the container at /code/requirements.txt
+COPY ./requirements.txt /code/requirements.txt
+
+# Upgrade pip and install the dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Create a non-root user with UID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the non-root user
+USER user
+
+# Set environment variables for the user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory for the application
+WORKDIR $HOME/app
+
+# Copy the local code into the container at /home/user/app
+COPY --chown=user . $HOME/app
+
+# Specify the command to run on container start
+CMD ["streamlit", "run", "app.py","--server.port","7860"]

app.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from pandas_datareader import data
+from keras.models import load_model
+import streamlit as st
+import plotly.graph_objects as go
+import datetime as dt
+import yfinance as yf
+import pandas_ta as ta
+from plotly.subplots import make_subplots
+from datetime import timedelta
+from sklearn.preprocessing import MinMaxScaler
+
+st.set_page_config(page_title='CryptoPredict 2.0', page_icon=':chart_with_upwards_trend:')
+st.title('CryptoCurrency Price Prediction')
+
+
+stocks = [ 'BTC-USD', 'ETH-USD', 'BNB-USD', 'SOL-USD', 'ADA-USD', 'XRP-USD', 'DOT-USD', 'DOGE-USD',
+          'AVAX-USD', 'LTC-USD', 'MATIC-USD', 'SHIB-USD']
+
+
+
+st.markdown('#')
+
+with st.expander(""):
+    col1, col2, col3 = st.columns([1, 1, 1])
+
+
+
+    col2.markdown("###### CRYPTO CURRENCIES")
+    col2.markdown("""
+    | Cryptocurrency | Ticker Symbol |
+    | --- | --- |
+    | Bitcoin | BTC-USD |
+    | Ethereum | ETH-USD |
+    | Binance Coin | BNB-USD |
+    | Solana | SOL-USD |
+    | Cardano | ADA-USD |
+    | XRP | XRP-USD |
+    | Polkadot | DOT-USD |
+    | Dogecoin | DOGE-USD |
+    | Avalanche | AVAX-USD |
+    | Litecoin | LTC-USD |
+    | Polygon | MATIC-USD |
+    | Shiba Inu | SHIB-USD |
+    """)
+
+
+
+
+user_input = st.selectbox('Enter Stock Ticker', stocks)
+
+st.markdown('# ')
+
+st.markdown('##### Select The Date Range For Technical Analysis')
+
+START = st.date_input('START:', value=pd.to_datetime("2017-01-01"))
+TODAY = st.date_input('END (Today):', value=pd.to_datetime("today"))
+
+stock_info = yf.Ticker(user_input).fast_info
+
+# stock_info.keys() for other properties you can explore
+
+
+st.subheader(user_input)
+
+
+def load_data(user_input):
+    yf.pdr_override()
+    daata = data.get_data_yahoo(user_input, start=START, end=TODAY)
+    daata.reset_index(inplace=True)
+    return daata
+
+
+df = load_data(user_input)
+
+# describing data
+
+st.subheader('Data Range 2017-Today')
+# df= df.reset_index()
+
+st.write(df.tail(10))
+st.write(df.describe())
+# Force lowercase (optional)
+df.columns = [x.lower() for x in df.columns]
+
+
+
+st.subheader("Prediction of Stock Price")
+
+# train test split
+data_training = pd.DataFrame(df['close'][0:int(len(df) * 0.70)])
+data_testing = pd.DataFrame(df['close'][int(len(df) * 0.70): int(len(df))])
+
+st.write("training data: ", data_training.shape)
+st.write("testing data: ", data_testing.shape)
+
+# scaling of data using min max scaler (0,1)
+
+
+scaler = MinMaxScaler(feature_range=(0, 1))
+
+data_training_array = scaler.fit_transform(data_training)
+
+# Load model
+model = load_model("lstm_model_2.h5")
+
+# testing part
+past_100_days = data_training.tail(30)
+
+final_df = past_100_days.append(data_testing, ignore_index=True)
+
+input_data = scaler.fit_transform(final_df)
+
+x_test = []
+y_test = []
+
+for i in range(100, input_data.shape[0]):
+    x_test.append(input_data[i - 100: i])
+    y_test.append(input_data[i, 0])
+x_test, y_test = np.array(x_test), np.array(y_test)
+
+y_predicted = model.predict(x_test)
+
+scaler = scaler.scale_
+
+scale_factor = 1 / scaler[0]
+
+y_predicted = y_predicted * scale_factor
+
+y_test = y_test * scale_factor
+
+
+
+st.subheader('Stock Price Prediction by Date')
+
+df1 = df.reset_index()['close']
+scaler = MinMaxScaler(feature_range=(0, 1))
+df1 = scaler.fit_transform(np.array(df1).reshape(-1, 1))
+
+# datemax="24/06/2022"
+datemax = dt.datetime.strftime(dt.datetime.now() - timedelta(1), "%d/%m/%Y")
+datemax = dt.datetime.strptime(datemax, "%d/%m/%Y")
+x_input = df1[:].reshape(1, -1)
+temp_input = list(x_input)
+temp_input = temp_input[0].tolist()
+
+date1 = st.date_input("Enter Date in this format yyyy-mm-dd")
+
+result = st.button("Predict")
+# st.write(result)
+if result:
+    from datetime import datetime
+
+    my_time = datetime.min.time()
+    date1 = datetime.combine(date1, my_time)
+    # date1=str(date1)
+    # date1=dt.datetime.pastime(time_str,"%Y-%m-%d")
+
+    nDay = date1 - datemax
+    nDay = nDay.days
+
+    date_rng = pd.date_range(start=datemax, end=date1, freq='D')
+    date_rng = date_rng[1:date_rng.size]
+    lst_output = []
+    n_steps = x_input.shape[1]
+    i = 0
+
+    while i <= nDay:
+
+        if len(temp_input) > n_steps:
+            # print(temp_input)
+            x_input = np.array(temp_input[1:])
+            print("{} day input {}".format(i, x_input))
+            x_input = x_input.reshape(1, -1)
+            x_input = x_input.reshape((1, n_steps, 1))
+            # print(x_input)
+            yhat = model.predict(x_input, verbose=0)
+            print("{} day output {}".format(i, yhat))
+            temp_input.extend(yhat[0].tolist())
+            temp_input = temp_input[1:]
+            # print(temp_input)
+            lst_output.extend(yhat.tolist())
+            i = i + 1
+        else:
+            x_input = x_input.reshape((1, n_steps, 1))
+            yhat = model.predict(x_input, verbose=0)
+            print(yhat[0])
+            temp_input.extend(yhat[0].tolist())
+            print(len(temp_input))
+            lst_output.extend(yhat.tolist())
+            i = i + 1
+    res = scaler.inverse_transform(lst_output)
+    # output = res[nDay-1]
+
+    output = res[nDay]
+
+    st.write("*Predicted Price for Date :*", date1, "*is*", np.round(output[0], 2))
+    st.success('The Price is {}'.format(np.round(output[0], 2)))
+
+    # st.write("predicted price : ",output)
+
+    predictions = res[res.size - nDay:res.size]
+    print(predictions.shape)
+    predictions = predictions.ravel()
+    print(type(predictions))
+    print(date_rng)
+    print(predictions)
+    print(date_rng.shape)
+
+
+    @st.cache_data
+    def convert_df(df):
+        return df.to_csv().encode('utf-8')
+
+
+    df = pd.DataFrame(data=date_rng)
+    df['Predictions'] = predictions.tolist()
+    df.columns = ['Date', 'Price']
+    st.write(df)
+    csv = convert_df(df)
+    st.download_button(
+        "Press to Download",
+        csv,
+        "file.csv",
+        "text/csv",
+        key='download-csv'
+    )
+    # visualization
+
+    fig = plt.figure(figsize=(10, 6))
+    xpoints = date_rng
+    ypoints = predictions
+
+    plt.plot(xpoints, ypoints, color='blue', marker='o', linestyle='-', linewidth=2,
+             markersize=5)  # Customize line style and marker
+    plt.xticks(rotation=45, fontsize=10)  # Rotate x-axis labels and adjust fontsize
+    plt.yticks(fontsize=10)  # Adjust fontsize of y-axis labels
+    plt.xlabel('Date', fontsize=12)  # Set x-axis label and adjust fontsize
+    plt.ylabel('Price', fontsize=12)  # Set y-axis label and adjust fontsize
+    plt.title('Cryptocurrency Price Prediction', fontsize=14)  # Set plot title and adjust fontsize
+    plt.grid(True, linestyle='--', alpha=0.5)  # Add grid lines with linestyle and transparency
+    plt.tight_layout()  # Adjust layout to prevent clipping of labels
+
+    # Display the plot in Streamlit
+    st.pyplot(fig)

lstm_model_2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fe3c121f531790a2a3b73a761a1a82413d16955157e700b00401251cda544592
+size 2217560

requirements.txt

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+numpy~=1.23.5
+pandas-ta~=0.3.14b
+pandas_datareader~=0.10.0
+pandas~=1.5.1
+tensorflow~=2.10.0
+matplotlib~=3.6.2
+streamlit~=1.22.0
+plotly~=5.11.0
+yfinance~=0.2.12
+keras~=2.10.0
+scikit-learn~=1.2.0