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import pandas as pd
import matplotlib.pyplot as plt
import gradio as gr
import requests
import os
import datetime
import tempfile
import numpy as np # Added for smoothing historical prices
# Your Hugging Face API Token (set this safely)
HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
# Mistral Inference API URL
API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
# Headers for authentication
headers = {
"Authorization": f"Bearer {HF_TOKEN}"
}
# Function to query Mistral API
def query_mistral(question):
payload = {
"inputs": question,
"parameters": {"max_length": 256}
}
response = requests.post(API_URL, headers=headers, json=payload)
output = response.json()
return output[0]["generated_text"]
# API Key
POLYGON_API_KEY = os.getenv("POLYGON_API_KEY")
# Sector Averages
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},
}
# Safe Request Function
def safe_request(url):
try:
response = requests.get(url)
response.raise_for_status()
return response
except:
return None
# 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
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
# New Financial Health Metrics
debt_equity = np.random.uniform(0.2, 2.0) # Placeholder: random for now
roe = np.random.uniform(5, 25) # Placeholder: random for now
free_cash_flow = np.random.uniform(50000000, 500000000) # Placeholder: random for now
beta = np.random.uniform(0.8, 1.5) # Placeholder: random for now
return {
'P/E Ratio': pe,
'P/S Ratio': ps,
'P/B Ratio': pb,
'PEG Ratio': peg,
'Dividend Yield': div_yield,
'Debt/Equity Ratio': debt_equity,
'Return on Equity (%)': roe,
'Free Cash Flow ($)': free_cash_flow,
'Beta (Volatility)': beta
}
# Theme Selection
selected_theme = os.getenv("APP_THEME", "light")
if selected_theme == "dark":
theme = gr.themes.Base()
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():
symbol = gr.Textbox(label="Stock Symbol (e.g., AAPL)")
eps = gr.Number(label="Assumed EPS", value=5.0)
growth = gr.Number(label="Assumed Growth Rate", value=0.1)
book = gr.Number(label="Assumed Book Value", value=500000000)
with gr.Tabs() as 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()
with gr.Tab("Ask About Investing"):
user_question = gr.Textbox(label="Ask about investing...")
answer_box = gr.Textbox(label="Answer")
ask_button = gr.Button("Get Answer")
with gr.Row():
ask_button.click(fn=lambda q: query_mistral(q),
inputs=[user_question],
outputs=[answer_box],
api_name="query_mistral").then(
lambda: "",
inputs=[],
outputs=[user_question]
)
with gr.Row():
submit_btn = gr.Button("Run Analysis")
reset_btn = gr.Button("Reset All Fields")
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])
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])
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() |