Update app.py

app.py

@@ -39,6 +39,7 @@ sector_averages = {
     "Energy": {"P/E Ratio": 12, "P/S Ratio": 1.2, "P/B Ratio": 1.3},
 }
 
+
 # Safe Request Function
 def safe_request(url):
     try:
@@ -50,7 +51,9 @@ def safe_request(url):
 
 # 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
@@ -82,6 +85,152 @@ if selected_theme == "dark":
 else:
     theme = gr.themes.Soft(primary_hue="blue")
 
+
+# Fetch Functions
+def get_company_info(symbol):
+    url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={POLYGON_API_KEY}"
+    response = safe_request(url)
+    if response:
+        data = response.json().get('results', {})
+        sector = data.get('market', 'Technology')
+        if sector.lower() == 'stocks':
+            sector = 'Technology'
+        return {
+            'Name': data.get('name', 'N/A'),
+            'Industry': data.get('sic_description', 'N/A'),
+            'Sector': sector,
+            'Market Cap': data.get('market_cap', 0),
+            'Total Revenue': data.get('total_employees', 0) * 100000
+        }
+    return None
+
+def get_current_price(symbol):
+    url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/prev?adjusted=true&apiKey={POLYGON_API_KEY}"
+    response = safe_request(url)
+    if response:
+        return response.json()['results'][0]['c']
+    return None
+
+def get_dividends(symbol):
+    url = f"https://api.polygon.io/v3/reference/dividends?ticker={symbol}&apiKey={POLYGON_API_KEY}"
+    response = safe_request(url)
+    if response:
+        return response.json()['results'][0].get('cash_amount', 0)
+    return 0
+
+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}"
+    response = safe_request(url)
+    if response:
+        results = response.json()['results']
+        dates = [datetime.datetime.fromtimestamp(r['t']/1000) for r in results]
+        prices = [r['c'] for r in results]
+        return dates, prices
+    return [], []
+
+# 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
+    ps = market_cap / total_revenue if total_revenue else 0
+    pb = market_cap / book_value if book_value else 0
+    peg = pe / (growth * 100) if growth else 0
+    div_yield = (dividend_amount / price) * 100 if price else 0
+    return {
+        'P/E Ratio': pe,
+        'P/S Ratio': ps,
+        'P/B Ratio': pb,
+        'PEG Ratio': peg,
+        'Dividend Yield': div_yield
+    }
+
+def compare_to_sector(sector, ratios):
+    if sector.lower() == 'stocks':
+        sector = 'Technology'
+    averages = sector_averages.get(sector, {})
+    if not averages:
+        return pd.DataFrame({"Metric": ["Sector data not available"], "Value": ["N/A"]})
+
+    data = {
+        "Ratio": [],
+        "Stock Value": [],
+        "Sector Average": [],
+        "Difference": []
+    }
+    for key in averages:
+        stock_value = ratios.get(key, 0)
+        sector_value = averages.get(key, 0)
+        diff = stock_value - sector_value
+
+        # Add emoji based on difference
+        if diff < 0:
+            diff_display = f"{diff:.2f} 🟢"
+        elif diff > 0:
+            diff_display = f"{diff:.2f} 🔴"
+        else:
+            diff_display = f"{diff:.2f} ⚪"
+
+        data["Ratio"].append(key)
+        data["Stock Value"].append(round(stock_value, 2))
+        data["Sector Average"].append(round(sector_value, 2))
+        data["Difference"].append(diff_display)
+
+    return pd.DataFrame(data)
+
+def generate_summary(info, ratios):
+    recommendation = "Hold"
+    if ratios['P/E Ratio'] < 15 and ratios['P/B Ratio'] < 2 and ratios['PEG Ratio'] < 1.0 and ratios['Dividend Yield'] > 2:
+        recommendation = "Buy"
+    elif ratios['P/E Ratio'] > 30 and ratios['P/B Ratio'] > 5 and ratios['PEG Ratio'] > 2.0:
+        recommendation = "Sell"
+    
+    report = (
+        f"Company Overview:\n"
+        f"Name: {info['Name']}\n"
+        f"Industry: {info['Industry']}\n"
+        f"Sector: {info['Sector']}\n"
+        f"Market Cap: ${info['Market Cap']:,.2f}\n\n"
+        f"Financial Metrics:\n"
+        f"P/E Ratio: {ratios['P/E Ratio']:.2f}\n"
+        f"P/S Ratio: {ratios['P/S Ratio']:.2f}\n"
+        f"P/B Ratio: {ratios['P/B Ratio']:.2f}\n"
+        f"PEG Ratio: {ratios['PEG Ratio']:.2f}\n"
+        f"Dividend Yield: {ratios['Dividend Yield']:.2f}%\n\n"
+        f"Recommended Investment Action: {recommendation}.\n"
+    )
+
+    # Use Mistral to generate the summary
+    summary_prompt = f"Summarize the following financial report clearly and briefly:\n\n{report}"
+    return query_mistral(summary_prompt)
+
+def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
+    info = get_company_info(symbol)
+    price = get_current_price(symbol)
+    dividends = get_dividends(symbol)
+    dates, prices = get_historical_prices(symbol)
+
+    if not info or not price:
+        return "⚠️ Error fetching stock info", None, None, None, None
+
+    ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends, eps, growth, book)
+    summary = generate_summary(info, ratios)
+    sector_comp = compare_to_sector(info['Sector'], ratios)
+
+    fig, ax = plt.subplots()
+    ax.plot(dates, prices)
+    ax.set_title(f"{symbol} Historical Price (1Y)")
+    ax.set_xlabel("Date")
+    ax.set_ylabel("Price ($)")
+    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
 with gr.Blocks(theme=theme) as iface:
     with gr.Row():
@@ -129,7 +278,13 @@ with gr.Blocks(theme=theme) as iface:
 
     reset_btn.click(fn=reset_fields, inputs=[], outputs=[symbol, eps, growth, book, output_summary, output_info, output_ratios, output_sector, output_chart])
 
-if __name__ == "__main__":
-    iface.launch()
 
-# Note: Smooth historical price chart and rounding ratios in output is planned. Financial Health metrics added.
+    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_sector, output_chart])
+
+
+
+if __name__ == "__main__":
+    iface.launch()