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import pandas as pd
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt
import requests
import os
from transformers import pipeline
import datetime
import tempfile
# Initialize Summarizer
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")
print(f"DEBUG: Using API Key: {api_key}")
url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={api_key}"
print(f"DEBUG: Fetching company info from URL: {url}")
try:
response = requests.get(url)
print(f"DEBUG: Company Info Status Code: {response.status_code}")
print(f"DEBUG: Company Info Response: {response.text}")
response.raise_for_status()
data = response.json()['results']
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
}
except Exception as e:
print(f"DEBUG: Error fetching company info: {e}")
return None
def get_current_price(symbol):
url = f"https://api.polygon.io/v2/aggs/ticker/{symbol}/prev?adjusted=true&apiKey={POLYGON_API_KEY}"
print(f"DEBUG: Fetching current price from URL: {url}")
try:
response = requests.get(url)
print(f"DEBUG: Current Price Status Code: {response.status_code}")
print(f"DEBUG: Current Price Response: {response.text}")
response.raise_for_status()
data = response.json()['results'][0]
return float(data['c'])
except Exception as e:
print(f"DEBUG: Error fetching current price: {e}")
return None
def get_dividends(symbol):
url = f"https://api.polygon.io/v3/reference/dividends?ticker={symbol}&apiKey={POLYGON_API_KEY}"
try:
response = requests.get(url)
response.raise_for_status()
data = response.json()['results'][0]
return {
'Dividend Amount': data.get('cash_amount', 0),
'Ex-Dividend Date': data.get('ex_dividend_date', 'N/A')
}
except Exception as e:
print(f"DEBUG: Error fetching dividends: {e}")
return {'Dividend Amount': 0, 'Ex-Dividend Date': 'N/A'}
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}"
try:
response = requests.get(url)
print(f"DEBUG: Historical Prices Status Code: {response.status_code}")
print(f"DEBUG: Historical Prices Response: {response.text}")
response.raise_for_status()
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
except Exception as e:
print(f"DEBUG: Error fetching historical prices: {e}")
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"
# Much better structured prompt
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
# (Rest of the code remains the same)
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
return {
'P/E Ratio': pe_ratio,
'P/S Ratio': ps_ratio,
'P/B Ratio': pb_ratio,
'PEG Ratio': peg_ratio,
'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 stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
if assumed_eps is None:
assumed_eps = 5.0
if growth_rate is None:
growth_rate = 0.1
if book_value is None:
book_value = 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 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")
ax.set_title(f"{symbol} Historical Price (1 Year)")
ax.set_xlabel("Date")
ax.set_ylabel("Price ($)")
ax.legend()
ax.grid(True)
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, 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.Row():
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"):
output_summary = gr.Textbox()
with gr.Tab("Company Snapshot"):
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_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()
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