app.py

import gradio as gr
import joblib
import numpy as np

# Load model and scaler
model = joblib.load("stock_model.pkl")
scaler = joblib.load("scaler.pkl")
label_encoder = joblib.load("label_encoder.pkl")

def predict_rating(ticker, pe, de, roe, mcap, dividend):
    try:
        # Prepare input for model
        features = np.array([[float(pe), float(de), float(roe), float(mcap), float(dividend)]])
        scaled = scaler.transform(features)
        prediction = model.predict(scaled)
        rating = label_encoder.inverse_transform(prediction)[0]
        recommendation = f"📈 Stock **{ticker.upper()}** is recommended to: **{rating}**"
        return recommendation
    except Exception as e:
        return f"❌ Error: {str(e)}"

# Define the Gradio interface
iface = gr.Interface(
    fn=predict_rating,
    inputs=[
        gr.Textbox(label="Stock Ticker (e.g., INFY)", placeholder="Enter stock ticker symbol here"),
        gr.Slider(label="PE Ratio", minimum=0, maximum=100, step=0.1, value=15),
        gr.Slider(label="DE Ratio", minimum=0, maximum=10, step=0.1, value=1),
        gr.Slider(label="ROE (%)", minimum=0, maximum=100, step=0.1, value=15),
        gr.Slider(label="Market Cap (₹ Cr)", minimum=0, maximum=500000, step=1000, value=100000),
        gr.Slider(label="Dividend Yield (%)", minimum=0, maximum=20, step=0.1, value=2)
    ],
    outputs=gr.Textbox(label="Stock Advice", placeholder=" "),
    title="📊 Stock Recommendation ",
    description="Enter stock parameters to get a Buy/Hold/Sell recommendation."
)

iface.launch()