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import gradio as gr |
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import numpy as np |
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import pandas as pd |
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from joblib import load |
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from huggingface_hub import hf_hub_download |
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REPO_ID = "eyu1belay/entrepreneurial-readiness-model" |
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MODEL_FILE = "readiness_model.joblib" |
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try: |
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local_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILE, repo_type="model") |
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bundle = load(local_path) |
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model = bundle["model"] |
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feature_cols = bundle["feature_cols"] |
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load_err = None |
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except Exception as e: |
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model, feature_cols = None, [] |
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load_err = f"⚠️ Could not load model bundle: {e}" |
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def _validate(savings_amount, income, bills, entertainment, sales, dependents, assets, age, risk, confidence, idea_diff): |
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for name, v in [("savings_amount", savings_amount), ("income", income), ("bills", bills), |
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("entertainment", entertainment), ("assets", assets)]: |
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if v is None or v < 0: |
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return f"Invalid {name}: must be >= 0" |
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if not (1 <= sales <= 5): return "Sales must be in [1,5]" |
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if not (0 <= dependents <= 10): return "Dependents must be in [0,10]" |
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if not (18 <= age <= 80): return "Age must be in [18,80]" |
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if not (1 <= risk <= 5): return "Risk must be in [1,5]" |
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if not (1 <= confidence <= 10): return "Confidence must be in [1,10]" |
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if not (1 <= idea_diff <= 5): return "Idea difficulty must be in [1,5]" |
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return None |
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def predict_readiness(savings_amount, income, bills, entertainment, sales, dependents, assets, age, risk, confidence, idea_diff, debug=False): |
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if load_err: |
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return {"error": load_err} |
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msg = _validate(savings_amount, income, bills, entertainment, sales, dependents, assets, age, risk, confidence, idea_diff) |
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if msg: |
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return {"error": msg} |
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row_dict = { |
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"savings_amount": float(savings_amount), |
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"monthly_income": float(income), |
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"monthly_bills": float(bills), |
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"monthly_entertainment": float(entertainment), |
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"sales_skills": float(sales), |
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"dependents": float(dependents), |
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"assets": float(assets), |
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"age": float(age), |
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"risk": float(risk), |
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"confidence": float(confidence), |
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"idea_difficulty": float(idea_diff), |
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} |
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try: |
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X = pd.DataFrame([row_dict])[feature_cols] |
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except Exception as e: |
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return {"error": f"Feature mismatch. Expected: {feature_cols}, Got: {list(row_dict.keys())}, Err: {e}"} |
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y_hat = int(model.predict(X)[0]) |
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proba = model.predict_proba(X)[0].tolist() if hasattr(model, "predict_proba") else None |
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out = {"model_label_1_5": y_hat, "proba": proba} |
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if debug: |
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out["echo_row_in_training_order"] = X.to_dict(orient="records")[0] |
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out["feature_cols"] = feature_cols |
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return out |
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with gr.Blocks(title="Entrepreneurial Readiness Predictor") as app: |
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gr.Markdown("## Entrepreneurial Readiness Predictor") |
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with gr.Row(): |
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savings_amount = gr.Number(label="Savings Amount ($)", value=1000) |
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income = gr.Number(label="Monthly Income ($)", value=4000) |
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bills = gr.Number(label="Monthly Bills ($)", value=2500) |
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entertainment = gr.Number(label="Monthly Entertainment ($)", value=300) |
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with gr.Row(): |
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sales = gr.Slider(1, 5, step=1, value=3, label="Sales Skills (1–5)") |
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dependents = gr.Slider(0, 10, step=1, value=1, label="Dependents (0–10)") |
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assets = gr.Number(label="Assets ($)", value=8000) |
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age = gr.Slider(18, 80, step=1, value=28, label="Age") |
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with gr.Row(): |
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risk = gr.Slider(1, 5, step=1, value=3, label="Risk (1–5)") |
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confidence = gr.Slider(1, 10, step=1, value=6, label="Confidence (1–10)") |
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idea_diff = gr.Slider(1, 5, step=1, value=2, label="Idea Difficulty (1=easy, 5=hard)") |
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debug_ck = gr.Checkbox(label="Debug", value=False) |
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predict_btn = gr.Button("Predict") |
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pred_out = gr.JSON(label="Prediction") |
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predict_btn.click( |
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predict_readiness, |
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inputs=[savings_amount, income, bills, entertainment, sales, dependents, assets, age, risk, confidence, idea_diff, debug_ck], |
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outputs=pred_out, |
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api_name="predict_readiness" |
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) |
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app.launch() |
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