Update app.py

app.py

@@ -8,18 +8,28 @@ from tensorflow.keras.layers import LSTM, Dense, Dropout
 import matplotlib.pyplot as plt
 import gradio as gr
 from datetime import datetime
+import requests  # To get the exchange rate
 
 # Disable GPU usage and oneDNN optimizations
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
 
+# Helper function to get the current USD to INR exchange rate
+def get_usd_to_inr_rate():
+    try:
+        response = requests.get('https://api.exchangerate-api.com/v4/latest/USD')
+        data = response.json()
+        return data['rates']['INR']
+    except Exception as e:
+        print(f"Error fetching exchange rate: {e}")
+        return 82.0  # Use a fallback conversion rate (adjust if necessary)
+
 # Helper function to handle date adjustments and retries if data not found
 def adjust_date_range_if_needed(stock_data, ticker, start_date, end_date):
     retries = 3  # Number of retries for fetching data
     while stock_data.empty and retries > 0:
         start_date = (datetime.strptime(start_date, '%Y-%m-%d') - timedelta(days=1)).strftime('%Y-%m-%d')
         end_date = (datetime.strptime(end_date, '%Y-%m-%d') - timedelta(days=1)).strftime('%Y-%m-%d')
-        print(f"Retrying with adjusted dates: {start_date} to {end_date}")  # Debugging output
         stock_data = yf.download(ticker, start=start_date, end=end_date)
         retries -= 1
     return stock_data, start_date, end_date
@@ -28,14 +38,12 @@ def adjust_date_range_if_needed(stock_data, ticker, start_date, end_date):
 def get_stock_data(ticker, start_date, end_date):
     try:
         stock_data = yf.download(ticker, start=start_date, end=end_date)
-        print(f"Stock data downloaded: {stock_data.shape}")  # Debugging output to check data download
     except Exception as e:
-        print(f"Error fetching data: {e}")  # Debugging output for data download error
+        print(f"Error fetching data: {e}")
         return None, None, None
     
     # If stock data is empty, attempt to adjust the date range
     if stock_data.empty:
-        print("No data found for the original date range.")  # Debugging output
         stock_data, adjusted_start, adjusted_end = adjust_date_range_if_needed(stock_data, ticker, start_date, end_date)
         if stock_data.empty:
             return None, None, None  # If still empty after retries, return None
@@ -44,7 +52,6 @@ def get_stock_data(ticker, start_date, end_date):
 
 # Preprocess the data for the LSTM model
 def preprocess_data(stock_data):
-    # Closing prices
     close_prices = stock_data['Close'].values
     close_prices = close_prices.reshape(-1, 1)
     
@@ -78,7 +85,6 @@ def load_trained_model(file_name):
 
 # Train and make predictions
 def predict_stock(stock_data, scaler, model):
-    # Get the last 60 days of stock data for prediction
     last_60_days = stock_data[-60:]
     last_60_days_scaled = scaler.transform(last_60_days)
     
@@ -92,14 +98,11 @@ def predict_stock(stock_data, scaler, model):
     predicted_price = model.predict(X_test)
     predicted_price = scaler.inverse_transform(predicted_price)
     
-    print(f"Predicted price: {predicted_price}")  # Debugging output to verify predictions
     return predicted_price
 
 # Main app function
 def stock_predictor(ticker, start_date, end_date):
-    # Validate if the ticker is available on Yahoo Finance
-    if not ticker:
-        return f"Invalid stock ticker: {ticker}"
+    usd_to_inr = get_usd_to_inr_rate()  # Get the USD to INR conversion rate
     
     # Get stock data
     stock_data, adjusted_start, adjusted_end = get_stock_data(ticker, start_date, end_date)
@@ -116,7 +119,6 @@ def stock_predictor(ticker, start_date, end_date):
     
     if model is None:
         # Train the model if pre-trained model is not found
-        print("Training the model...")  # Debugging output for training
         model = build_model()
         X_train, y_train = [], []
         for i in range(60, len(scaled_data)):
@@ -134,17 +136,21 @@ def stock_predictor(ticker, start_date, end_date):
     # Predict stock price for tomorrow
     predicted_price = predict_stock(scaled_data, scaler, model)
     
-    # Historical vs Predicted
+    # Convert predicted price to INR
+    predicted_price_inr = predicted_price[0][0] * usd_to_inr
+    
+    # Historical vs Predicted Graph
     plt.figure(figsize=(14, 7))
-    plt.plot(stock_data['Close'], color="blue", label="Historical Prices")
-    plt.scatter(len(stock_data), predicted_price, color="red", label="Predicted Price for Tomorrow")
+    plt.plot(stock_data['Close'], color="blue", label="Historical Prices (USD)")
+    plt.scatter(len(stock_data), predicted_price[0], color="red", label="Predicted Price (USD)")
     plt.title(f"{ticker} Stock Price Prediction")
     plt.xlabel('Date')
-    plt.ylabel('Price')
+    plt.ylabel('Price (USD)')
     plt.legend()
     plt.show()
 
-    return f"Predicted Stock Price for {ticker} tomorrow: ${predicted_price[0][0]:.2f}"
+    # Return the predicted price in INR
+    return f"Predicted Stock Price for {ticker} tomorrow: ₹{predicted_price_inr:.2f} (INR)"
 
 # Gradio UI
 def build_ui():