Update app.py

app.py

@@ -1,17 +1,16 @@
-import gradio as gr
 import pandas as pd
+import matplotlib.pyplot as plt
+import gradio as gr
 import requests
+import os
 import datetime
 import tempfile
-import os
-import matplotlib.pyplot as plt
 from transformers import pipeline
 
-# Initialize Models
+# Initialize Summarizer and Chat Model
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 chat_model = pipeline("text-generation", model="tiiuae/falcon-7b-instruct", max_length=256)
 
-
 # API Key
 POLYGON_API_KEY = os.getenv("POLYGON_API_KEY")
 
@@ -23,16 +22,7 @@ sector_averages = {
     "Energy": {"P/E Ratio": 12, "P/S Ratio": 1.2, "P/B Ratio": 1.3},
 }
 
-# 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
+# Safe Request Function
 def safe_request(url):
     try:
         response = requests.get(url)
@@ -41,15 +31,15 @@ def safe_request(url):
     except:
         return None
 
+# 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')
-        # Dynamic Guess
         if sector.lower() == 'stocks':
-            sector = "Technology"
+            sector = 'Technology'
         return {
             'Name': data.get('name', 'N/A'),
             'Industry': data.get('sic_description', 'N/A'),
@@ -85,6 +75,7 @@ def get_historical_prices(symbol):
         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
@@ -112,7 +103,6 @@ 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)
@@ -133,7 +123,6 @@ def compare_to_sector(sector, ratios):
 
     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:
@@ -160,9 +149,6 @@ def answer_investing_question(question):
     response = chat_model(question)[0]['generated_text']
     return response
 
-
-
-
 def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
     info = get_company_info(symbol)
     price = get_current_price(symbol)
@@ -188,7 +174,7 @@ def stock_research(symbol, eps=5.0, growth=0.1, book=500000000):
 
     return summary, info_table, ratios_table, sector_comp, fig
 
-# --- Gradio UI ---
+# Gradio UI
 with gr.Blocks(theme="soft") as iface:
     with gr.Row():
         symbol = gr.Textbox(label="Stock Symbol (e.g., AAPL)")
@@ -220,17 +206,5 @@ with gr.Blocks(theme="soft") as iface:
     submit_btn.click(fn=stock_research, inputs=[symbol, eps, growth, book],
                      outputs=[output_summary, output_info, output_ratios, output_sector, output_chart])
 
-    # 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()