Update app.py

app.py

@@ -1,25 +1,42 @@
-
 import gradio as gr
 import pandas as pd
 import numpy as np
 import plotly.express as px
 import scipy.optimize as sco
 from datetime import datetime, timedelta
+import requests
 import random
+from datetime import datetime, timedelta
 
-# Sample stock data generator
-def generate_sample_data(tickers, days=252):
-    np.random.seed(42)  # For reproducibility
-    dates = pd.date_range(end=datetime.now(), periods=days)
-    data = {}
+def fetch_stock_data(tickers):
+    """Fetch real stock data from Yahoo Finance API"""
+    all_data = {}
     
     for ticker in tickers:
-        # Generate random walk data
-        returns = np.random.normal(loc=0.0001, scale=0.02, size=days)
-        price = 100 * (1 + returns).cumprod()
-        data[ticker] = price
+        url = "https://yahoo-finance166.p.rapidapi.com/api/v1/finance/quote"
+        
+        querystring = {"symbol": ticker}
         
-    return pd.DataFrame(data, index=dates)
+        headers = {
+            "X-RapidAPI-Key": "e4d2d5bccdmsh3ad7175fdbb435bp13c65cjsn33c57",
+            "X-RapidAPI-Host": "yahoo-finance166.p.rapidapi.com"
+        }
+        
+        try:
+            response = requests.get(url, headers=headers, params=querystring)
+            response.raise_for_status()
+            data = response.json()
+            
+            # Extract historical prices
+            if "quoteResponse" in data and "result" in data["quoteResponse"]:
+                prices = data["quoteResponse"]["result"][0]
+                all_data[ticker] = prices["regularMarketPrice"]
+                
+        except Exception as e:
+            print(f"Error fetching data for {ticker}: {str(e)}")
+            continue
+    
+    return pd.DataFrame(all_data)
 
 # Predefined S&P 500 Stock List (Sample tickers)
 SP500_TICKERS = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA', 'BRK-B', 'NVDA', 'JPM', 'JNJ', 'V']
@@ -55,9 +72,12 @@ def simulate_investment(weights, mu, years, initial_investment=10000):
 
 def output_results(risk_level):
     try:
-        # Generate sample data instead of fetching from API
-        selected_tickers = random.sample(SP500_TICKERS, min(len(SP500_TICKERS), 10))
-        stocks_df = generate_sample_data(selected_tickers)
+        # Use real data instead of sample data
+        selected_tickers = random.sample(SP500_TICKERS, min(len(SP500_TICKERS), 5))  # Reduced to 5 tickers to avoid API limits
+        stocks_df = fetch_stock_data(selected_tickers)
+        
+        if stocks_df.empty:
+            raise ValueError("Failed to fetch stock data")
         
         returns = stocks_df.pct_change().dropna()
         
@@ -125,6 +145,7 @@ def output_results(risk_level):
         )
         
     except Exception as e:
+        print(f"Error in output_results: {str(e)}")
         return None, None, None, f"Error: {str(e)}", "Error", "Error", None
 
 # Create Gradio interface
@@ -169,4 +190,4 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
     )
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch()