Update app.py

app.py

@@ -1,17 +1,16 @@
-import pandas as pd
-import numpy as np
 import gradio as gr
-import matplotlib.pyplot as plt
+import pandas as pd
 import requests
-import os
-from transformers import pipeline
 import datetime
 import tempfile
+import os
+from transformers import pipeline
 
-# Initialize Summarizer
+# Initialize Models
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+chat_model = pipeline("text-generation", model="google/flan-t5-large", max_length=256)
 
-# Polygon API Key
+# API Key
 POLYGON_API_KEY = os.getenv("POLYGON_API_KEY")
 
 # Sector Averages
@@ -22,67 +21,60 @@ sector_averages = {
     "Energy": {"P/E Ratio": 12, "P/S Ratio": 1.2, "P/B Ratio": 1.3},
 }
 
-# Helper Functions
+# Tooltip dictionary
+tooltips = {
+    "P/E Ratio": "Price/Earnings: Lower can indicate better value.",
+    "P/S Ratio": "Price/Sales: Lower can indicate better value relative to sales.",
+    "P/B Ratio": "Price/Book: Lower can indicate undervaluation.",
+    "PEG Ratio": "Price/Earnings to Growth: Closer to 1 is ideal.",
+    "Dividend Yield": "Annual dividend income relative to price."
+}
 
+# Helper Functions
 def safe_request(url):
     try:
         response = requests.get(url)
         response.raise_for_status()
         return response
-    except requests.exceptions.HTTPError as http_err:
-        print(f"DEBUG: HTTP error occurred: {http_err}")
-    except Exception as err:
-        print(f"DEBUG: Other error occurred: {err}")
-    return None
-
+    except:
+        return None
 
 def get_company_info(symbol):
-    api_key = os.getenv("POLYGON_API_KEY")
-    if not api_key:
-        print("DEBUG: API Key is missing!")
-        return None
-    url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={api_key}"
+    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')
+        # Dynamic Guess
+        if sector.lower() == 'stocks':
+            sector = "Technology"
         return {
             'Name': data.get('name', 'N/A'),
             'Industry': data.get('sic_description', 'N/A'),
-            'Sector': data.get('market', '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):
-    api_key = os.getenv("POLYGON_API_KEY")
-    url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/prev?adjusted=true&apiKey={api_key}"
+    url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/prev?adjusted=true&apiKey={POLYGON_API_KEY}"
     response = safe_request(url)
     if response:
-        data = response.json()['results'][0]
-        return float(data['c'])
+        return response.json()['results'][0]['c']
     return None
 
-
 def get_dividends(symbol):
-    api_key = os.getenv("POLYGON_API_KEY")
-    url = f"https://api.polygon.io/v3/reference/dividends?ticker={symbol}&apiKey={api_key}"
+    url = f"https://api.polygon.io/v3/reference/dividends?ticker={symbol}&apiKey={POLYGON_API_KEY}"
     response = safe_request(url)
     if response:
-        data = response.json()['results'][0]
-        return {
-            'Dividend Amount': data.get('cash_amount', 0),
-            'Ex-Dividend Date': data.get('ex_dividend_date', 'N/A')
-        }
-    return {'Dividend Amount': 0, 'Ex-Dividend Date': 'N/A'}
-
+        return response.json()['results'][0].get('cash_amount', 0)
+    return 0
 
 def get_historical_prices(symbol):
-    api_key = os.getenv("POLYGON_API_KEY")
     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={api_key}"
+    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']
@@ -91,58 +83,18 @@ def get_historical_prices(symbol):
         return dates, prices
     return [], []
 
-
-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\n"
-        f"Please provide a detailed financial analysis based on the information above."
-    )
-    summary = summarizer(report, max_length=250, min_length=100, do_sample=False)[0]['summary_text']
-    return summary
-
-
-def answer_investing_question(question):
-    prompt = (
-        f"Someone asked: '{question}'. "
-        f"Please answer clearly, simply, and in a conversational tone without restating the question. "
-        f"Keep the answer beginner-friendly and encouraging."
-    )
-    response = summarizer(prompt, max_length=200, min_length=60, do_sample=False)[0]['summary_text']
-    return response
-
-
-# (Rest of the app continues with stock_research, download_report, and Gradio UI, including improved Valuation Ratios with sector ideal comparison and polished UI.)
-
-
-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
+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_ratio,
-        'P/S Ratio': ps_ratio,
-        'P/B Ratio': pb_ratio,
-        'PEG Ratio': peg_ratio,
-        'Dividend Yield (%)': dividend_yield
+        '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):
@@ -158,7 +110,6 @@ def compare_to_sector(sector, ratios):
         "Sector Average": [],
         "Difference": []
     }
-
     for key in averages:
         stock_value = ratios.get(key, 0)
         sector_value = averages.get(key, 0)
@@ -167,59 +118,65 @@ def compare_to_sector(sector, ratios):
         data["Stock Value"].append(round(stock_value, 2))
         data["Sector Average"].append(round(sector_value, 2))
         data["Difference"].append(round(diff, 2))
-
     return pd.DataFrame(data)
 
-def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
+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"
+    )
+    return summarizer(report, max_length=250, min_length=100, do_sample=False)[0]['summary_text']
+
+def answer_investing_question(question):
+    prompt = (
+        f"Explain the following investing question clearly and simply for a beginner:\n\n"
+        f"{question}\n\n"
+        f"Be conversational and encouraging."
+    )
+    return chat_model(prompt)[0]['generated_text']
+
+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: Could not fetch stock information. Please check your API Key or ticker.", None, None, None, None, None
+        return "⚠️ Error fetching stock info", None, None, None, None
 
-    ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends['Dividend Amount'], assumed_eps, growth_rate, book_value)
+    ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, dividends, eps, growth, book)
     summary = generate_summary(info, ratios)
-
-    # Apply fallback for sector
-    sector = info.get('Sector', 'Technology')
-    sector_comp = compare_to_sector(sector, ratios)
+    sector_comp = compare_to_sector(info['Sector'], ratios)
 
     fig, ax = plt.subplots()
-    ax.plot(dates, prices, label=f"{symbol} Price")
-    ax.set_title(f"{symbol} Historical Price (1 Year)")
+    ax.plot(dates, prices)
+    ax.set_title(f"{symbol} Historical Price (1Y)")
     ax.set_xlabel("Date")
     ax.set_ylabel("Price ($)")
-    ax.legend()
     ax.grid(True)
 
-    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()]
-    })
-    ratios_table = pd.DataFrame({
-        "Ratio": list(ratios.keys()),
-        "Value": [f"{v:.3f}" if isinstance(v, float) else v for v in ratios.values()]
-    })
+    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
 
-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)
-        f.write("\n")
-        ratios_table.to_csv(f, index=False)
-        f.write("\n")
-        sector_comp.to_csv(f, index=False)
-        f.write("\nSummary\n")
-        f.write(summary)
-        file_path = f.name
-    return file_path
-
 # --- Gradio UI ---
-
-with gr.Blocks() as iface:
+with gr.Blocks(theme="soft") as iface:
     with gr.Row():
         symbol = gr.Textbox(label="Stock Symbol (e.g., AAPL)")
         eps = gr.Number(label="Assumed EPS", value=5.0)
@@ -238,8 +195,8 @@ with gr.Blocks() as iface:
         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")
+            user_question = gr.Textbox(label="Ask about investing...")
+            answer_box = gr.Textbox()
             ask_button = gr.Button("Get Answer")
             ask_button.click(fn=answer_investing_question, inputs=[user_question], outputs=[answer_box])
 
@@ -250,8 +207,17 @@ with gr.Blocks() as iface:
     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)
+    # Sector Comparison Color Highlight
+    def style_sector(df):
+        def highlight(val):
+            if isinstance(val, (int, float)):
+                if val < 0:
+                    return 'color: green'
+                elif val > 0:
+                    return 'color: red'
+            return ''
+        return df.style.applymap(highlight, subset=['Difference'])
+    output_sector.style_fn = style_sector
 
 if __name__ == "__main__":
     iface.launch()