import gradio as gr
import pandas as pd

def calculate_pb_ratio(initial_pb_ratio, book_value_growth_rate, stock_price_growth_rate, years):
    pb_ratios = [initial_pb_ratio]
    for i in range(1, int(years) + 1):  # Convert years to an integer
        projected_book_value = pb_ratios[-1] * (1 + book_value_growth_rate)
        projected_stock_price = projected_book_value * pb_ratios[-1] * (1 + stock_price_growth_rate)
        projected_pb_ratio = projected_stock_price / projected_book_value
        pb_ratios.append(projected_pb_ratio)
    return pb_ratios

def pb_ratio_valuation(initial_pb_ratio, book_value_growth_rate, stock_price_growth_rate, years):
    projected_pb_ratios = calculate_pb_ratio(initial_pb_ratio, book_value_growth_rate, stock_price_growth_rate, years)
    results = {"Year": [], "Projected P/B Ratio": []}
    for i in range(len(projected_pb_ratios)):
        results["Year"].append(i + 1)
        results["Projected P/B Ratio"].append(projected_pb_ratios[i])
    return pd.DataFrame(results)

# Define the Gradio interface
gr.Interface(
    fn=pb_ratio_valuation,
    inputs=[
        gr.inputs.Slider(minimum=0, maximum=10, default=1.5, label="Initial P/B Ratio"),
        gr.inputs.Slider(minimum=0, maximum=0.5, default=0.05, label="Annual Book Value Growth Rate"),
        gr.inputs.Slider(minimum=0, maximum=0.5, default=0.08, label="Annual Stock Price Growth Rate"),
        gr.inputs.Number(default=10, label="Years")
    ],
    outputs=gr.outputs.Dataframe(type='pandas'),  # Use Dataframe as the output with type 'pandas'
    title="Price-to-Book (P/B) Ratio Valuation",
    description="Calculate projected P/B ratios over the next 10 years.",
).launch()