| import joblib |
| import pandas as pd |
| import os |
| from huggingface_hub import hf_hub_download |
| from opik import Opik, track |
|
|
| |
| |
| |
| if "OPIK_API_KEY" not in os.environ: |
| os.environ["OPIK_API_KEY"] = os.environ.get( |
| "EXPO_PUBLIC_OPIK_API_KEY", "" |
| ) |
|
|
| |
| |
| |
| MODEL_REPO = "obx0x3/sensei-model" |
| MODEL_FILE = "impulse_model.pkl" |
|
|
| model_path = hf_hub_download( |
| repo_id=MODEL_REPO, |
| filename=MODEL_FILE |
| ) |
|
|
| impulse_model = joblib.load(model_path) |
|
|
| |
| |
| |
| try: |
| opik_client = Opik(project_name="budgetbuddy-hackathon") |
| except Exception as e: |
| print("Opik disabled:", e) |
| opik_client = None |
|
|
|
|
| |
| |
| |
| @track(project_name="budgetbuddy-hackathon") |
| def predict_impulse(category, amount, payment_method, day): |
| input_data = { |
| "category": category, |
| "amount": float(amount), |
| "payment_method": payment_method, |
| "day": day |
| } |
|
|
| df = pd.DataFrame([input_data]) |
|
|
| pred = impulse_model.predict(df)[0] |
| prob = impulse_model.predict_proba(df)[0].max() |
|
|
| result = { |
| "impulsive": bool(pred), |
| "confidence": round(float(prob), 3), |
| "label": "Impulsive" if pred else "Normal Spend" |
| } |
|
|
| |
| |
| |
| if opik_client: |
| try: |
| opik_client.log_event( |
| name="Impulse Analysis Result", |
| input=input_data, |
| output=result, |
| model="sensei-impulse-model", |
| metadata={ |
| "ui": "hf-space", |
| "feature": "impulse-detection", |
| "confidence_band": ( |
| "high" if prob > 0.75 else |
| "medium" if prob > 0.5 else |
| "low" |
| ) |
| } |
| ) |
| except Exception as e: |
| print("Opik logging failed:", e) |
|
|
| return result |
