Update app.py

app.py

@@ -37,17 +37,32 @@ def get_company_fcf(symbol):
         response = requests.get(url)
         response.raise_for_status()
         data = response.json()
-
-        # Navigate the JSON to find Free Cash Flow
         fcf = data['results'][0]['financials']['cash_flow_statement']['free_cash_flow']['value']
         return float(fcf)
-
     except Exception as e:
         print(f"DEBUG: Error fetching FCF for {symbol}: {e}")
         return None
 
-def generate_summary(symbol, intrinsic_value):
-    text = f"Based on a DCF analysis, the intrinsic value of {symbol} is estimated to be ${intrinsic_value:,.2f}."
+def get_current_stock_price(symbol):
+    api_key = os.getenv("POLYGON_API_KEY")
+    url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/prev?adjusted=true&apiKey={api_key}"
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+        price = data['results'][0]['c']  # 'c' = close price
+        return float(price)
+    except Exception as e:
+        print(f"DEBUG: Error fetching current price for {symbol}: {e}")
+        return None
+
+def generate_summary(symbol, intrinsic_value, market_price):
+    verdict = "undervalued ✅" if intrinsic_value > market_price else "overvalued ⚠️"
+    text = (
+        f"Based on a DCF analysis, the intrinsic value of {symbol} is estimated to be "
+        f"${intrinsic_value:,.2f} compared to its current market price of ${market_price:,.2f}. "
+        f"The stock appears {verdict}."
+    )
     summary = summarizer(text, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
     return summary
 
@@ -66,6 +81,7 @@ def dcf_interface(symbol, growth_rate, discount_rate, terminal_growth_rate, fore
     fcf_forecast, present_values, terminal_value, terminal_value_pv, total_intrinsic_value = discounted_cash_flow(
         fcf, growth_rate, discount_rate, terminal_growth_rate, forecast_years
     )
+
     df = pd.DataFrame({
         'Year': list(range(1, forecast_years + 1)),
         'Forecasted FCF ($)': fcf_forecast,
@@ -79,7 +95,11 @@ def dcf_interface(symbol, growth_rate, discount_rate, terminal_growth_rate, fore
     ax.set_ylabel('Free Cash Flow ($)')
     ax.grid(True)
 
-    summary = generate_summary(symbol, total_intrinsic_value)
+    market_price = get_current_stock_price(symbol)
+    if market_price is None:
+        market_price = 0  # fallback
+
+    summary = generate_summary(symbol, total_intrinsic_value, market_price)
     return df, fig, summary
 
 iface = gr.Interface(
@@ -100,7 +120,7 @@ iface = gr.Interface(
         ["AAPL", 0.06, 0.08, 0.025, 5]
     ],
     title="AI-Powered DCF Valuation Calculator",
-    description="Enter a stock symbol and your assumptions. The app pulls live data from Polygon.io, runs a DCF, and generates a research summary."
+    description="Enter a stock symbol and your assumptions. The app pulls live data from Polygon.io, runs a DCF, compares to the market price, and generates a research summary."
 )
 
 if __name__ == "__main__":