app.py

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
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")

# 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])

if __name__ == "__main__":
    iface.launch()

# Note: Smooth historical price chart and rounding ratios in output is planned. Financial Health metrics added.