Update app.py

app.py

@@ -6,6 +6,7 @@ import requests
 import os
 from transformers import pipeline
 import datetime
+import io
 
 # Initialize Summarizer
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
@@ -13,6 +14,14 @@ 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 = {
+    "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},
+    "Financials": {"P/E Ratio": 15, "P/S Ratio": 2, "P/B Ratio": 1.5},
+    "Energy": {"P/E Ratio": 12, "P/S Ratio": 1.2, "P/B Ratio": 1.3},
+}
+
 # Helper Functions
 def get_company_info(symbol):
     api_key = os.getenv("POLYGON_API_KEY")
@@ -24,6 +33,7 @@ def get_company_info(symbol):
         return {
             'Name': data.get('name', 'N/A'),
             'Industry': data.get('sic_description', 'N/A'),
+            'Sector': data.get('market', 'N/A'),
             'Market Cap': data.get('market_cap', 0),
             'Total Revenue': data.get('total_employees', 0) * 100000
         }
@@ -85,6 +95,18 @@ def calculate_ratios(market_cap, total_revenue, price, dividend_amount, assumed_
         'Dividend Yield (%)': dividend_yield
     }
 
+def compare_to_sector(sector, ratios):
+    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 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}, "
@@ -108,10 +130,11 @@ def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=50000000
     dates, prices = get_historical_prices(symbol)
 
     if not info or not price:
-        return "Error fetching stock information.", None, None, None
+        return "Error fetching stock information.", None, None, None, None, None
 
     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)
 
     fig, ax = plt.subplots()
     ax.plot(dates, prices, label=f"{symbol} Price")
@@ -124,14 +147,26 @@ def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=50000000
     info_table = pd.DataFrame({"Metric": list(info.keys()), "Value": list(info.values())})
     ratios_table = pd.DataFrame({"Ratio": list(ratios.keys()), "Value": list(ratios.values())})
 
-    return summary, info_table, ratios_table, fig
+    return summary, info_table, ratios_table, sector_comp, fig
+
+def download_report(info_table, ratios_table, sector_comp, summary):
+    buffer = io.StringIO()
+    info_table.to_csv(buffer, index=False)
+    buffer.write("\n")
+    ratios_table.to_csv(buffer, index=False)
+    buffer.write("\n")
+    sector_comp.to_csv(buffer, index=False)
+    buffer.write("\nSummary\n")
+    buffer.write(summary)
+    buffer.seek(0)
+    return buffer
 
 with gr.Blocks() as iface:
     with gr.Row():
-        symbol = gr.Textbox(label="Stock Symbol (e.g., AAPL)", info="The ticker symbol of the company you want to analyze.")
-        eps = gr.Number(label="Assumed EPS", value=5.0, info="Earnings Per Share (EPS) is used to calculate the P/E Ratio.")
-        growth = gr.Number(label="Assumed Growth Rate", value=0.1, info="Expected annual growth rate for PEG Ratio.")
-        book = gr.Number(label="Assumed Book Value", value=500000000, info="Company's total net assets for P/B Ratio.")
+        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.")
 
     with gr.Tabs():
         with gr.Tab("AI Research Summary"):
@@ -140,16 +175,26 @@ with gr.Blocks() as iface:
             output_info = gr.Dataframe()
         with gr.Tab("Valuation Ratios"):
             output_ratios = gr.Dataframe()
+        with gr.Tab("Sector Comparison"):
+            output_sector = gr.Dataframe()
         with gr.Tab("Historical Price Chart"):
             output_chart = gr.Plot()
 
     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_chart]
+        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
     )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()