app.py

import gradio as gr
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
import warnings
warnings.filterwarnings('ignore')

# Function to download stock data
def download_stock_data(ticker):
    stock_data = yf.Ticker(ticker)
    df = stock_data.history(period="1y")[['Close']]
    return df
    
# Function to calculate Bollinger Bands
def calculate_bollinger_bands(df, window, no_of_stds):
    rolling_mean = df['Close'].rolling(window=window).mean()
    rolling_std = df['Close'].rolling(window=window).std()
    df['Bollinger High'] = rolling_mean + (rolling_std * no_of_stds)
    df['Bollinger Low'] = rolling_mean - (rolling_std * no_of_stds)
    return df

# Function for anomaly detection using Bollinger Bands
def detect_anomalies(df):
    anomalies = (df['Close'] > df['Bollinger High']) | (df['Close'] < df['Bollinger Low'])
    return anomalies

# Function to create the plot
def plot_stock(ticker, window, no_of_stds):
    df = download_stock_data(ticker)
    df_with_bands = calculate_bollinger_bands(df, window, no_of_stds)
    anomalies = detect_anomalies(df_with_bands)
    df_with_bands['Anomaly'] = anomalies

    fig = go.Figure()
    fig.add_trace(go.Scatter(x=df_with_bands.index, y=df_with_bands['Close'], name='Close Price', mode='lines'))
    fig.add_trace(go.Scatter(x=df_with_bands.index, y=df_with_bands['Bollinger High'], name='Bollinger High', line=dict(width=1)))
    fig.add_trace(go.Scatter(x=df_with_bands.index, y=df_with_bands['Bollinger Low'], name='Bollinger Low', line=dict(width=1)))
    fig.add_trace(go.Scatter(x=df_with_bands.index, y=df_with_bands['Close'].where(df_with_bands['Anomaly']), mode='markers', name='Anomaly', marker=dict(color='red', size=10)))
    fig.update_layout(title=f'Bollinger Bands and Anomalies for {ticker}', xaxis_title='Date', yaxis_title='Price', height=600)
    return fig

"""# Gradio interface for version 4.5.0
iface = gr.Interface(
    fn=plot_stock,
    inputs=[
        gr.Dropdown(['RELIANCE.NS', 'TCS.NS', 'HDFCBANK.NS', 'INFY.NS', 'MARUTI.NS'], label="Select Stock Ticker"),
        gr.Slider(minimum=5, maximum=100, value=20, label="Window Size for SMA"),
        gr.Slider(minimum=1, maximum=3, value=2, label="Number of Standard Deviations")
    ],
    outputs="plot",
    title="Stock Anomaly Detection with Bollinger Bands",
    description="Select a stock ticker and adjust the window size and standard deviations to view Bollinger Bands and anomalies."
)

if __name__ == "__main__":
    iface.launch()"""


# Correct the indentation of line 18
iface = gr.Interface(
    fn=plot_stock,
    inputs=[
        gr.Dropdown(['RELIANCE.NS', 'TCS.NS', 'HDFCBANK.NS', 'INFY.NS', 'MARUTI.NS'], label="Select Stock Ticker"),
        gr.Slider(minimum=5, maximum=100, value=20, label="Window Size for SMA"),
        gr.Slider(minimum=1, maximum=3, value=2, label="Number of Standard Deviations")
    ],
    outputs="plot",
    title="Stock Anomaly Detection with Bollinger Bands",
    description="Select a stock ticker and adjust the window size and standard deviations to view Bollinger Bands and anomalies.",
    theme=gr.themes.Soft()  # Apply the Soft theme
)
if __name__ == "__main__":
    iface.launch()