Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import pandas as pd
 import numpy as np
-from sklearn.linear_model import LinearRegression
 import yfinance as yf
 import datetime
 import matplotlib.pyplot as plt
@@ -12,21 +11,17 @@ def get_stock_data(ticker, start_date, end_date):
     stock_data.reset_index(inplace=True)
     return stock_data
 
-# Function to prepare data for Linear Regression model
-def prepare_data(stock_data):
-    stock_data['Date'] = pd.to_datetime(stock_data['Date'])
-    stock_data['Days'] = (stock_data['Date'] - stock_data['Date'].min()).dt.days
-    X = stock_data['Days'].values.reshape(-1, 1)
-    y = stock_data['Close'].values
-    return X, y
+# Function to calculate Simple Moving Average (SMA)
+def calculate_sma(stock_data, window=30):
+    stock_data['SMA'] = stock_data['Close'].rolling(window=window).mean()
+    return stock_data
 
-# Function to make predictions using Linear Regression
-def make_predictions(X, y, periods=90):
-    model = LinearRegression()
-    model.fit(X, y)
-    future_days = np.array(range(X[-1][0] + 1, X[-1][0] + 1 + periods)).reshape(-1, 1)
-    predicted_values = model.predict(future_days)
-    return future_days, predicted_values
+# Function to predict future stock price using SMA
+def predict_future_sma(stock_data, periods=30):
+    last_sma = stock_data['SMA'].iloc[-1]
+    future_dates = pd.date_range(start=stock_data['Date'].iloc[-1], periods=periods + 1, closed='right')
+    future_sma = np.full(periods, last_sma)
+    return future_dates, future_sma
 
 # Function to calculate percentage change
 def calculate_percentage_change(stock_data):
@@ -42,10 +37,11 @@ def get_stock_stats(stock_data):
     return high_value, low_value
 
 # Function to plot stock performance
-def plot_stock_performance(stock_data, predicted_dates, predicted_values):
+def plot_stock_performance(stock_data, predicted_dates, predicted_sma):
     plt.figure(figsize=(10, 5))
     plt.plot(stock_data['Date'], stock_data['Close'], label="Historical Data", color="blue")
-    plt.plot(predicted_dates, predicted_values, label="Predicted Data", color="orange")
+    plt.plot(stock_data['Date'], stock_data['SMA'], label="SMA (30 days)", color="green")
+    plt.plot(predicted_dates, predicted_sma, label="Predicted SMA", color="orange", linestyle='--')
     plt.xlabel("Date")
     plt.ylabel("Stock Price")
     plt.title("Stock Performance (Historical vs Predicted)")
@@ -63,24 +59,23 @@ def stock_prediction(ticker, start_date, end_date):
     percentage_change = calculate_percentage_change(stock_data)
     high_value, low_value = get_stock_stats(stock_data)
     
-    # Prepare data for prediction
-    X, y = prepare_data(stock_data)
-    future_days, predicted_values = make_predictions(X, y)
+    # Calculate Simple Moving Average
+    stock_data = calculate_sma(stock_data)
+    
+    # Predict future stock performance using SMA
+    predicted_dates, predicted_sma = predict_future_sma(stock_data)
     
     # Generate buy/sell suggestion (simple rule-based)
     suggestion = "Buy" if percentage_change < 0 else "Sell"
     
-    # Create prediction dates
-    predicted_dates = pd.date_range(start=stock_data['Date'].max(), periods=len(predicted_values), freq='D')
-    
     # Plot stock performance
-    plot_file = plot_stock_performance(stock_data, predicted_dates, predicted_values)
+    plot_file = plot_stock_performance(stock_data, predicted_dates, predicted_sma)
     
     return plot_file, percentage_change, high_value, low_value, suggestion
 
 # UI setup
 with gr.Blocks() as app:
-    gr.Markdown("# Stock Prediction App using Linear Regression")
+    gr.Markdown("# Stock Prediction App using Simple Moving Average (SMA)")
     
     stock_ticker = gr.Dropdown(
         label="Select Stock Ticker",