File size: 1,168 Bytes
3873e40 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | from __future__ import annotations
import json
import os
import requests
MODEL_REPO = "arun-gharami/lead-ai-customer-predictor"
HF_TOKEN = os.environ.get("HF_TOKEN", "")
def call_hf_inference_api(payload: dict) -> dict:
api_url = f"https://api-inference.huggingface.co/models/{MODEL_REPO}"
headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
response = requests.post(api_url, headers=headers, json={"inputs": payload}, timeout=30)
response.raise_for_status()
return response.json()
if __name__ == "__main__":
sample_payload = {
"customer_tenure": 11,
"total_spent": 900,
"last_purchase_days": 17,
"visit_count": 26,
"email_open_rate": 0.62,
"discount_usage": 0.22,
"support_tickets": 1,
"satisfaction_score": 8.4,
}
print("Payload:")
print(json.dumps(sample_payload, indent=2))
print("\nResponse:")
try:
print(json.dumps(call_hf_inference_api(sample_payload), indent=2))
except Exception as exc:
print(f"API call failed: {exc}")
print("Run local scoring by importing predict_customer_status from app.py.")
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