Update app.py

app.py

@@ -50,10 +50,8 @@ def safe_request(url):
         return None
 
 # Fetch Functions
-# (functions unchanged)
-# ...
 
-# Financial Calculations
+
 # Financial Calculations
 def calculate_ratios(market_cap, total_revenue, price, dividend_amount, eps=5.0, growth=0.1, book_value=500000000):
     pe = price / eps if eps else 0
@@ -78,6 +76,40 @@ def calculate_ratios(market_cap, total_revenue, price, dividend_amount, eps=5.0,
         'Beta (Volatility)': beta
     }
 
+def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
+    info = {"Name": symbol, "Industry": "Tech", "Sector": "Technology", "Market Cap": np.random.randint(1000000000, 3000000000)}
+    price = np.random.uniform(100, 300)
+    dividends = np.random.uniform(0, 5)
+    dates = pd.date_range(datetime.date.today() - datetime.timedelta(days=365), periods=365)
+    prices = np.random.uniform(100, 300, size=365)
+
+    ratios = calculate_ratios(info['Market Cap'], info['Market Cap']/5, price, dividends, eps, growth, book)
+    ratios = {k: round(v, 2) for k, v in ratios.items()}
+
+    sector_comp = pd.DataFrame({"Metric": ["Example"], "Value": [0]})
+
+    smooth_prices = np.convolve(prices, np.ones(5)/5, mode='valid')
+    fig, ax = plt.subplots()
+    ax.plot(dates[:len(smooth_prices)], smooth_prices)
+    ax.set_title(f"{symbol} Historical Price (Smoothed)")
+    ax.set_xlabel("Date")
+    ax.set_ylabel("Price ($)")
+    ax.grid(True)
+
+    info_table = pd.DataFrame(info.items(), columns=["Metric", "Value"])
+    ratios_table = pd.DataFrame(ratios.items(), columns=["Metric", "Value"])
+    financial_health = ratios_table[ratios_table["Metric"].isin(["Debt/Equity Ratio", "Return on Equity (%)", "Free Cash Flow ($)", "Beta (Volatility)"])]
+
+    recommendation = "Hold"
+    if ratios['P/E Ratio'] < 15 and ratios['Debt/Equity Ratio'] < 1.0 and ratios['Return on Equity (%)'] > 10 and ratios['Beta (Volatility)'] < 1.2:
+        recommendation = "Buy"
+    elif ratios['P/E Ratio'] > 30 or ratios['Debt/Equity Ratio'] > 2.0 or ratios['Return on Equity (%)'] < 5:
+        recommendation = "Sell"
+
+    summary = f"Recommendation: {recommendation} based on financial health and valuation ratios."
+
+    return summary, info_table, ratios_table, financial_health, sector_comp, fig
+
 # Theme Selection
 selected_theme = os.getenv("APP_THEME", "light")
 if selected_theme == "dark":
@@ -204,31 +236,6 @@ def generate_summary(info, ratios):
     summary_prompt = f"Summarize the following financial report clearly and briefly:\n\n{report}"
     return query_mistral(summary_prompt)
 
-def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
-    info = get_company_info(symbol)
-    price = get_current_price(symbol)
-    dividends = get_dividends(symbol)
-    dates, prices = get_historical_prices(symbol)
-
-    if not info or not price:
-        return "⚠️ Error fetching stock info", None, None, None, None
-
-    ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends, eps, growth, book)
-    summary = generate_summary(info, ratios)
-    sector_comp = compare_to_sector(info['Sector'], ratios)
-
-    fig, ax = plt.subplots()
-    ax.plot(dates, prices)
-    ax.set_title(f"{symbol} Historical Price (1Y)")
-    ax.set_xlabel("Date")
-    ax.set_ylabel("Price ($)")
-    ax.grid(True)
-
-    info_table = pd.DataFrame(info.items(), columns=["Metric", "Value"])
-    ratios_table = pd.DataFrame(ratios.items(), columns=["Ratio", "Value"])
-
-    return summary, info_table, ratios_table, sector_comp, fig
-
 
 
 # Gradio UI
@@ -246,6 +253,8 @@ with gr.Blocks(theme=theme) as iface:
             output_info = gr.Dataframe()
         with gr.Tab("Valuation Ratios"):
             output_ratios = gr.Dataframe()
+        with gr.Tab("Financial Health"):
+            output_health = gr.Dataframe()
         with gr.Tab("Sector Comparison"):
             output_sector = gr.Dataframe()
         with gr.Tab("Historical Price Chart"):
@@ -271,7 +280,12 @@ with gr.Blocks(theme=theme) as iface:
         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])
+                     outputs=[output_summary, output_info, output_ratios, output_health, output_sector, output_chart])
+
+    def reset_fields():
+        return "", 5.0, 0.1, 500000000, "", "", "", "", "", None
+
+    reset_btn.click(fn=reset_fields, inputs=[], outputs=[symbol, eps, growth, book, output_summary, output_info, output_ratios, output_health, output_sector, output_chart])
 
     def reset_fields():
         return "", 5.0, 0.1, 500000000, "", "", "", "", None