Update app.py

app.py

@@ -14,7 +14,7 @@ summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 # Polygon API Key
 POLYGON_API_KEY = os.getenv("POLYGON_API_KEY")
 
-# Sector Averages (Hardcoded for now)
+# Sector Averages
 sector_averages = {
     "Technology": {"P/E Ratio": 25, "P/S Ratio": 5, "P/B Ratio": 6},
     "Healthcare": {"P/E Ratio": 20, "P/S Ratio": 4, "P/B Ratio": 3},
@@ -22,7 +22,8 @@ sector_averages = {
     "Energy": {"P/E Ratio": 12, "P/S Ratio": 1.2, "P/B Ratio": 1.3},
 }
 
-# Helper Functions
+# --- Helper Functions ---
+
 def safe_request(url):
     try:
         response = requests.get(url)
@@ -40,7 +41,6 @@ def get_company_info(symbol):
         print("DEBUG: API Key is missing!")
         return None
     url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={api_key}"
-    print(f"DEBUG: Fetching company info from URL: {url}")
     response = safe_request(url)
     if response:
         data = response.json().get('results', {})
@@ -56,7 +56,6 @@ def get_company_info(symbol):
 def get_current_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}"
-    print(f"DEBUG: Fetching current price from URL: {url}")
     response = safe_request(url)
     if response:
         data = response.json()['results'][0]
@@ -118,17 +117,34 @@ def answer_investing_question(question):
     response = summarizer(basic_prompt, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
     return response
 
-# (Rest of the app continues with stock_research, download_report, and Gradio UI, now including a new Tab for Q&A)
-
+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,
+        'Dividend Yield (%)': dividend_yield
+    }
+
+def compare_to_sector(sector, ratios):
+    if sector.lower() == 'stocks':
+        sector = 'Technology'  # Fallback
+    averages = sector_averages.get(sector, None)
+    if not averages:
+        return pd.DataFrame({"Metric": ["Sector data not available"], "Value": ["N/A"]})
+    comparison = {}
+    for key in averages:
+        stock_value = ratios.get(key, 0)
+        sector_value = averages[key]
+        comparison[key] = f"{stock_value:.2f} vs Sector Avg {sector_value:.2f}"
+    return pd.DataFrame({"Ratio": list(comparison.keys()), "Comparison": list(comparison.values())})
 
 def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
-    if assumed_eps is None:
-        assumed_eps = 5.0
-    if growth_rate is None:
-        growth_rate = 0.1
-    if book_value is None:
-        book_value = 500000000
-
     info = get_company_info(symbol)
     price = get_current_price(symbol)
     dividends = get_dividends(symbol)
@@ -139,7 +155,10 @@ def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=50000000
 
     ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends['Dividend Amount'], assumed_eps, growth_rate, book_value)
     summary = generate_summary(info, ratios)
-    sector_comp = compare_to_sector(info['Sector'], ratios)
+
+    # Apply fallback for sector
+    sector = info.get('Sector', 'Technology')
+    sector_comp = compare_to_sector(sector, ratios)
 
     fig, ax = plt.subplots()
     ax.plot(dates, prices, label=f"{symbol} Price")
@@ -149,13 +168,10 @@ def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=50000000
     ax.legend()
     ax.grid(True)
 
-    # ✅ Format info_table
     info_table = pd.DataFrame({
         "Metric": list(info.keys()),
         "Value": [f"${v:,.0f}" if isinstance(v, (int, float)) and abs(v) > 1000 else v for v in info.values()]
     })
-
-    # ✅ Format ratios_table
     ratios_table = pd.DataFrame({
         "Ratio": list(ratios.keys()),
         "Value": [f"{v:.3f}" if isinstance(v, float) else v for v in ratios.values()]
@@ -163,7 +179,6 @@ def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=50000000
 
     return summary, info_table, ratios_table, sector_comp, fig
 
-
 def download_report(info_table, ratios_table, sector_comp, summary):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w') as f:
         info_table.to_csv(f, index=False)
@@ -176,13 +191,14 @@ def download_report(info_table, ratios_table, sector_comp, summary):
         file_path = f.name
     return file_path
 
-# Gradio UI
+# --- Gradio UI ---
+
 with gr.Blocks() as iface:
     with gr.Row():
-        symbol = gr.Textbox(label="Stock Symbol (e.g., AAPL)", info="Ticker symbol of the company to analyze.")
-        eps = gr.Number(label="Assumed EPS", value=5.0, info="Earnings Per Share (EPS) for P/E calculation.")
-        growth = gr.Number(label="Assumed Growth Rate", value=0.1, info="Expected annual growth rate for PEG.")
-        book = gr.Number(label="Assumed Book Value", value=500000000, info="Total net assets for P/B calculation.")
+        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"):
@@ -195,22 +211,21 @@ with gr.Blocks() as iface:
             output_sector = gr.Dataframe()
         with gr.Tab("Historical Price Chart"):
             output_chart = gr.Plot()
+        with gr.Tab("Ask About Investing"):
+            user_question = gr.Textbox(label="Ask a question about investing...")
+            answer_box = gr.Textbox(label="Answer")
+            ask_button = gr.Button("Get Answer")
+            ask_button.click(fn=answer_investing_question, inputs=[user_question], outputs=[answer_box])
 
     submit_btn = gr.Button("Run Analysis")
     download_btn = gr.Button("Download Report")
     file_output = gr.File()
 
-    submit_btn.click(
-        fn=stock_research,
-        inputs=[symbol, eps, growth, book],
-        outputs=[output_summary, output_info, output_ratios, output_sector, output_chart]
-    )
+    submit_btn.click(fn=stock_research, inputs=[symbol, eps, growth, book],
+                     outputs=[output_summary, output_info, output_ratios, output_sector, output_chart])
 
-    download_btn.click(
-        fn=download_report,
-        inputs=[output_info, output_ratios, output_sector, output_summary],
-        outputs=file_output
-    )
+    download_btn.click(fn=download_report, inputs=[output_info, output_ratios, output_sector, output_summary],
+                       outputs=file_output)
 
 if __name__ == "__main__":
     iface.launch()