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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
# Initialize Summarizer
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
# Polygon API Key
POLYGON_API_KEY = os.getenv("POLYGON_API_KEY")
# Helper Functions
def get_company_info(symbol):
url = f"https://api.polygon.io/v3/reference/tickers/{symbol}?apiKey={POLYGON_API_KEY}"
try:
response = requests.get(url)
response.raise_for_status()
data = response.json()['results']
return {
'Name': data.get('name', 'N/A'),
'Industry': data.get('sic_description', 'N/A'),
'Market Cap': data.get('market_cap', 0),
'Total Revenue': data.get('total_employees', 0) * 100000 # Rough estimation
}
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}"
try:
response = requests.get(url)
response.raise_for_status()
data = response.json()['results'][0]
return float(data['c'])
except Exception as e:
print(f"DEBUG: Error fetching 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)
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 calculate_ratios(market_cap, total_revenue, price, 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
return {
'P/E Ratio': pe_ratio,
'P/S Ratio': ps_ratio,
'P/B Ratio': pb_ratio,
'PEG Ratio': peg_ratio
}
def generate_summary(info, ratios):
text = (f"{info['Name']} operates in the {info['Industry']} sector. It has a market capitalization of "
f"${info['Market Cap']:,.2f}. The company exhibits a P/E ratio of {ratios['P/E Ratio']:.2f}, "
f"P/S ratio of {ratios['P/S Ratio']:.2f}, and P/B ratio of {ratios['P/B Ratio']:.2f}. "
f"This suggests a {'potential undervaluation' if ratios['P/E Ratio'] < 20 else 'higher valuation'} relative to the market.")
summary = summarizer(text, max_length=120, min_length=30, do_sample=False)[0]['summary_text']
return summary
def stock_research(symbol, assumed_eps=5.0, growth_rate=0.1, book_value=500000000):
info = get_company_info(symbol)
price = get_current_price(symbol)
dividends = get_dividends(symbol)
dates, prices = get_historical_prices(symbol)
if not info or not price:
return "Error fetching stock information.", None, None, None
ratios = calculate_ratios(info['Market Cap'], info['Total Revenue'], price, assumed_eps, growth_rate, book_value)
summary = generate_summary(info, ratios)
# Create historical price chart
fig, ax = plt.subplots()
ax.plot(dates, prices, label=f"{symbol} Price")
ax.set_title(f"{symbol} Historical Price (1 Year)")
ax.set_xlabel("Date")
ax.set_ylabel("Price ($)")
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, fig
iface = gr.Interface(
fn=stock_research,
inputs=[
gr.Textbox(label="Stock Symbol (e.g., AAPL)"),
gr.Number(label="Assumed EPS (default 5.0)"),
gr.Number(label="Assumed Growth Rate (e.g., 0.1 for 10%)"),
gr.Number(label="Assumed Book Value ($, default 500M)")
],
outputs=[
gr.Textbox(label="AI Research Summary"),
gr.Dataframe(label="Company Snapshot"),
gr.Dataframe(label="Valuation Ratios"),
gr.Plot(label="Historical Price Chart")
],
title="AI-Powered Stock Researcher",
description="Enter a stock symbol to get company info, valuation ratios, a 1-year price chart, and an AI-generated research summary based on live Polygon.io data."
)
if __name__ == "__main__":
iface.launch()
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