Update app.py

app.py

@@ -7,6 +7,9 @@ from sklearn.model_selection import train_test_split
 import gradio as gr
 import matplotlib.pyplot as plt
 from datetime import datetime, timedelta
+import joblib
+import os
+import re
 
 # Define stock tickers
 STOCK_TICKERS = [
@@ -130,14 +133,34 @@ def buy_or_sell(current_price: float, predicted_price: float) -> str:
     else:
         return "Sell"
 
+def validate_date_format(date_text: str) -> bool:
+    """
+    Validates that the input string is a date in 'YYYY-MM-DD' format.
+
+    Parameters:
+    - date_text (str): Date string to validate.
+
+    Returns:
+    - bool: True if valid, False otherwise.
+    """
+    # Regular expression for YYYY-MM-DD format
+    regex = r'^\d{4}-\d{2}-\d{2}$'
+    if re.match(regex, date_text):
+        try:
+            datetime.strptime(date_text, '%Y-%m-%d')
+            return True
+        except ValueError:
+            return False
+    return False
+
 def stock_prediction_app(ticker: str, start_date: str, end_date: str):
     """
     Main function to handle stock prediction and return outputs.
 
     Parameters:
     - ticker (str): Selected stock ticker.
-    - start_date (str): Training start date.
-    - end_date (str): Training end date.
+    - start_date (str): Training start date in 'YYYY-MM-DD' format.
+    - end_date (str): Training end date in 'YYYY-MM-DD' format.
 
     Returns:
     - percentage_change (str): Percentage change from start to end date.
@@ -146,9 +169,23 @@ def stock_prediction_app(ticker: str, start_date: str, end_date: str):
     - decision (str): Buy or Sell decision.
     - plot (matplotlib.figure.Figure): Plot of historical prices with tomorrow's prediction.
     """
+    # Validate date formats
+    if not (validate_date_format(start_date) and validate_date_format(end_date)):
+        return "Invalid date format. Please use YYYY-MM-DD.", "N/A", "N/A", "Error", None
+
+    # Convert strings to datetime objects
+    try:
+        start_dt = datetime.strptime(start_date, '%Y-%m-%d')
+        end_dt = datetime.strptime(end_date, '%Y-%m-%d')
+    except ValueError:
+        return "Invalid date values. Please ensure dates are correct.", "N/A", "N/A", "Error", None
+
+    if start_dt >= end_dt:
+        return "Start date must be before end date.", "N/A", "N/A", "Error", None
+
     # Fetch data
     data = fetch_stock_data(ticker, start_date, end_date)
-    
+
     if data.empty:
         return "N/A", "N/A", "N/A", "No Data Available", None
 
@@ -158,7 +195,7 @@ def stock_prediction_app(ticker: str, start_date: str, end_date: str):
     percentage_change = ((end_price - start_price) / start_price) * 100
     highest_price = data['Close'].max()
     lowest_price = data['Close'].min()
-    
+
     # Preprocess data
     try:
         X, y = preprocess_data(data)
@@ -167,33 +204,33 @@ def stock_prediction_app(ticker: str, start_date: str, end_date: str):
 
     if len(X) == 0:
         return f"{percentage_change:.2f}%", highest_price, lowest_price, "No Prediction", None
-    
+
     # Train the model
     try:
         model = train_model(X, y)
     except Exception as e:
         return f"Error in training model: {e}", highest_price, lowest_price, "Error", None
-    
+
     # Make prediction
     try:
         predicted_price = make_prediction(model, data)
     except Exception as e:
         return f"Error in making prediction: {e}", highest_price, lowest_price, "Error", None
-    
+
     # Current price is the last closing price
     current_price = data['Close'].iloc[-1]
     decision = buy_or_sell(current_price, predicted_price)
-    
+
     # Plotting historical prices and predicted tomorrow's price
     plt.figure(figsize=(10,5))
     plt.plot(data['Close'], label='Historical Close Price')
-    
+
     # Add predicted price for tomorrow
     tomorrow_date = data.index[-1] + timedelta(days=1)
     # Ensure tomorrow is a business day
     while tomorrow_date.weekday() >= 5:  # Saturday=5, Sunday=6
         tomorrow_date += timedelta(days=1)
-    
+
     plt.scatter(tomorrow_date, predicted_price, color='red', label='Predicted Close Price (Tomorrow)')
     plt.title(f'{ticker} Price Prediction for Tomorrow')
     plt.xlabel('Date')
@@ -202,8 +239,30 @@ def stock_prediction_app(ticker: str, start_date: str, end_date: str):
     plt.tight_layout()
     fig = plt.gcf()
     plt.close()
-    
+
     # Formatting outputs
     percentage_change_str = f"{percentage_change:.2f}%"
-    
+
     return percentage_change_str, highest_price, lowest_price, decision, fig
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=stock_prediction_app,
+    inputs=[
+        gr.Dropdown(choices=STOCK_TICKERS, label="Select Stock Ticker"),
+        gr.Textbox(label="Enter Start Date (YYYY-MM-DD)", placeholder="e.g., 2020-01-01"),
+        gr.Textbox(label="Enter End Date (YYYY-MM-DD)", placeholder="e.g., 2023-12-31")
+    ],
+    outputs=[
+        gr.Textbox(label="Percentage Change"),
+        gr.Number(label="Highest Closing Price"),
+        gr.Number(label="Lowest Closing Price"),
+        gr.Textbox(label="Decision (Buy/Sell)"),
+        gr.Plot(label="Stock Performance")
+    ],
+    title="Stock Prediction App",
+    description="Predict whether to buy or sell a stock based on historical data. Please enter dates in YYYY-MM-DD format."
+)
+
+# Launch the interface
+iface.launch()