app.py

# app.py
import panda as pd 
import gradio
import numpy as np
import pickle


from huggingface_hub import hf_hub_download

# Allow overriding from Space secrets / env
HF_TOKEN = os.environ.get("HF_TOKEN")  # set this in Space secrets if the model is private
# Let repo id be overridable too, just in case the owner/name changes
HF_MODEL_ID = os.environ.get("HF_MODEL_ID", "SpringyBon/entrepreneur-readiness-regressor")

def _download(fname: str) -> str:
    return hf_hub_download(
        repo_id=HF_MODEL_ID,
        filename=fname,
        token=HF_TOKEN,          # <--- token is None for public; required for private
        repo_type="model"        # optional but explicit
    )

model_path = _download("model.pkl")
feat_path  = _download("feature_names.json")

reg = pickle.load(model_path)
with open(feat_path, "r") as f:
    FEATURES = json.load(f)

def predict(
    savings_amount, monthly_income, monthly_expenses, monthly_entertainment,
    sales_skills_1to10, age, dependents, assets_count, risk_tolerance_1to10,
    confidence_1to10, business_idea_difficulty_1to10, disposable_income,
    runway_months, income_stability_1to10, prior_experience_years, credit_score
):
    sample = {
        "savings_amount": savings_amount,
        "monthly_income": monthly_income,
        "monthly_expenses": monthly_expenses,
        "monthly_entertainment": monthly_entertainment,
        "sales_skills_1to10": sales_skills_1to10,
        "age": age,
        "dependents": dependents,
        "assets_count": assets_count,
        "risk_tolerance_1to10": risk_tolerance_1to10,
        "confidence_1to10": confidence_1to10,
        "business_idea_difficulty_1to10": business_idea_difficulty_1to10,
        "disposable_income": disposable_income,
        "runway_months": runway_months,
        "income_stability_1to10": income_stability_1to10,
        "prior_experience_years": prior_experience_years,
        "credit_score": credit_score,
    }
    try:
        x = [sample[f] for f in FEATURES]  # preserve training order
    except KeyError as e:
        return f"Missing feature: {e}"
    pred = float(reg.predict(np.array(x, dtype=float).reshape(1, -1))[0])
    return round(pred, 2)

with gr.Blocks(title="Entrepreneur Readiness Regressor") as demo:
    gr.Markdown("## Entrepreneur Readiness — Prediction Demo")
    with gr.Row():
        with gr.Column():
            savings_amount = gr.Number(label="Savings Amount", value=25000)
            monthly_income = gr.Number(label="Monthly Income", value=5000)
            monthly_expenses = gr.Number(label="Monthly Expenses", value=3000)
            monthly_entertainment = gr.Number(label="Monthly Entertainment", value=200)
            sales_skills_1to10 = gr.Slider(1, 10, value=7, step=1, label="Sales Skills (1-10)")
            age = gr.Number(label="Age", value=32)
            dependents = gr.Number(label="Dependents", value=1, precision=0)
        with gr.Column():
            assets_count = gr.Number(label="Assets Count", value=2, precision=0)
            risk_tolerance_1to10 = gr.Slider(1, 10, value=6, step=1, label="Risk Tolerance (1-10)")
            confidence_1to10 = gr.Slider(1, 10, value=8, step=1, label="Confidence (1-10)")
            business_idea_difficulty_1to10 = gr.Slider(1, 10, value=5, step=1, label="Idea Difficulty (1-10)")
            disposable_income = gr.Number(label="Disposable Income", value=2000)
            runway_months = gr.Number(label="Runway (months)", value=12, precision=0)
            income_stability_1to10 = gr.Slider(1, 10, value=7, step=1, label="Income Stability (1-10)")
            prior_experience_years = gr.Number(label="Prior Experience (years)", value=4)
            credit_score = gr.Number(label="Credit Score", value=710, precision=0)
    output = gr.Number(label="Predicted Readiness Score", precision=2)
    gr.Button("Predict Readiness").click(
        predict,
        inputs=[savings_amount, monthly_income, monthly_expenses, monthly_entertainment,
                sales_skills_1to10, age, dependents, assets_count, risk_tolerance_1to10,
                confidence_1to10, business_idea_difficulty_1to10, disposable_income,
                runway_months, income_stability_1to10, prior_experience_years, credit_score],
        outputs=output
    )

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