Update app.py

app.py

@@ -15,13 +15,10 @@ POLYGON_API_KEY = os.getenv("POLYGON_API_KEY")
 
 # Helper Functions
 def get_company_info(symbol):
-    print(f"DEBUG: Using API Key: {POLYGON_API_KEY}")
-    url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={POLYGON_API_KEY}"
-    print(f"DEBUG: Fetching company info URL: {url}")
+    api_key = os.getenv("POLYGON_API_KEY")
+    url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={api_key}"
     try:
         response = requests.get(url)
-        print(f"DEBUG: Response Status: {response.status_code}")
-        print(f"DEBUG: Response JSON: {response.json()}")
         response.raise_for_status()
         data = response.json()['results']
         return {
@@ -36,11 +33,8 @@ def get_company_info(symbol):
 
 def get_current_price(symbol):
     url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/prev?adjusted=true&apiKey={POLYGON_API_KEY}"
-    print(f"DEBUG: Fetching current price URL: {url}")
     try:
         response = requests.get(url)
-        print(f"DEBUG: Response Status: {response.status_code}")
-        print(f"DEBUG: Response JSON: {response.json()}")
         response.raise_for_status()
         data = response.json()['results'][0]
         return float(data['c'])
@@ -50,11 +44,8 @@ def get_current_price(symbol):
 
 def get_dividends(symbol):
     url = f"https://api.polygon.io/v3/reference/dividends?ticker={symbol}&apiKey={POLYGON_API_KEY}"
-    print(f"DEBUG: Fetching dividends URL: {url}")
     try:
         response = requests.get(url)
-        print(f"DEBUG: Response Status: {response.status_code}")
-        print(f"DEBUG: Response JSON: {response.json()}")
         response.raise_for_status()
         data = response.json()['results'][0]
         return {
@@ -69,11 +60,8 @@ def get_historical_prices(symbol):
     end = datetime.date.today()
     start = end - datetime.timedelta(days=365)
     url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/range/1/day/{start}/{end}?adjusted=true&sort=asc&apiKey={POLYGON_API_KEY}"
-    print(f"DEBUG: Fetching historical prices URL: {url}")
     try:
         response = requests.get(url)
-        print(f"DEBUG: Response Status: {response.status_code}")
-        print(f"DEBUG: Response JSON: {response.json()}")
         response.raise_for_status()
         results = response.json()['results']
         dates = [datetime.datetime.fromtimestamp(r['t']/1000) for r in results]
@@ -83,28 +71,30 @@ def get_historical_prices(symbol):
         print(f"DEBUG: Error fetching historical prices: {e}")
         return [], []
 
-def calculate_ratios(market_cap, total_revenue, price, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
+def calculate_ratios(market_cap, total_revenue, price, dividend_amount, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
     pe_ratio = price / assumed_eps if assumed_eps else 0
     ps_ratio = market_cap / total_revenue if total_revenue else 0
     pb_ratio = market_cap / book_value if book_value else 0
     peg_ratio = pe_ratio / (growth_rate * 100) if growth_rate else 0
+    dividend_yield = (dividend_amount / price) * 100 if price else 0
     return {
         'P/E Ratio': pe_ratio,
         'P/S Ratio': ps_ratio,
         'P/B Ratio': pb_ratio,
-        'PEG Ratio': peg_ratio
+        'PEG Ratio': peg_ratio,
+        'Dividend Yield (%)': dividend_yield
     }
 
 def generate_summary(info, ratios):
     text = (f"{info['Name']} operates in the {info['Industry']} sector. It has a market capitalization of "
             f"${info['Market Cap']:,.2f}. The company exhibits a P/E ratio of {ratios['P/E Ratio']:.2f}, "
             f"P/S ratio of {ratios['P/S Ratio']:.2f}, and P/B ratio of {ratios['P/B Ratio']:.2f}. "
+            f"Its dividend yield is {ratios['Dividend Yield (%)']:.2f}%. "
             f"This suggests a {'potential undervaluation' if ratios['P/E Ratio'] < 20 else 'higher valuation'} relative to the market.")
     summary = summarizer(text, max_length=120, min_length=30, do_sample=False)[0]['summary_text']
     return summary
 
 def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
-    # Safeguard against None values
     if assumed_eps is None:
         assumed_eps = 5.0
     if growth_rate is None:
@@ -120,10 +110,9 @@ def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=50000000
     if not info or not price:
         return "Error fetching stock information.", None, None, None
 
-    ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, assumed_eps, growth_rate, book_value)
+    ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends['Dividend Amount'], assumed_eps, growth_rate, book_value)
     summary = generate_summary(info, ratios)
 
-    # Create historical price chart
     fig, ax = plt.subplots()
     ax.plot(dates, prices, label=f"{symbol} Price")
     ax.set_title(f"{symbol} Historical Price (1 Year)")
@@ -137,23 +126,38 @@ def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=50000000
 
     return summary, info_table, ratios_table, fig
 
-iface = gr.Interface(
-    fn=stock_research,
-    inputs=[
-        gr.Textbox(label="Stock Symbol (e.g., AAPL)"),
-        gr.Number(label="Assumed EPS", value=5.0),
-        gr.Number(label="Assumed Growth Rate", value=0.1),
-        gr.Number(label="Assumed Book Value", value=500000000)
-    ],
-    outputs=[
-        gr.Textbox(label="AI Research Summary"),
-        gr.Dataframe(label="Company Snapshot"),
-        gr.Dataframe(label="Valuation Ratios"),
-        gr.Plot(label="Historical Price Chart")
-    ],
-    title="AI-Powered Stock Researcher",
-    description="Enter a stock symbol to get company info, valuation ratios, a 1-year price chart, and an AI-generated research summary based on live Polygon.io data."
-)
+with gr.Blocks() as iface:
+    gr.Markdown("""
+    # 📚 Why These Inputs Matter:
+    - **Assumed EPS**: Needed to calculate the Price/Earnings (P/E) Ratio.
+    - **Assumed Growth Rate**: Needed to calculate the Price/Earnings/Growth (PEG) Ratio.
+    - **Assumed Book Value**: Needed to calculate the Price/Book (P/B) Ratio.
+    - **Dividend Info**: Automatically fetched.
+    """)
+    
+    with gr.Row():
+        symbol = gr.Textbox(label="Stock Symbol (e.g., AAPL)")
+        eps = gr.Number(label="Assumed EPS", value=5.0)
+        growth = gr.Number(label="Assumed Growth Rate", value=0.1)
+        book = gr.Number(label="Assumed Book Value", value=500000000)
+
+    with gr.Tabs():
+        with gr.Tab("AI Research Summary"):
+            output_summary = gr.Textbox()
+        with gr.Tab("Company Snapshot"):
+            output_info = gr.Dataframe()
+        with gr.Tab("Valuation Ratios"):
+            output_ratios = gr.Dataframe()
+        with gr.Tab("Historical Price Chart"):
+            output_chart = gr.Plot()
+
+    submit_btn = gr.Button("Run Analysis")
+
+    submit_btn.click(
+        fn=stock_research,
+        inputs=[symbol, eps, growth, book],
+        outputs=[output_summary, output_info, output_ratios, output_chart]
+    )
 
 if __name__ == "__main__":
     iface.launch()