| |
| import pandas as pd |
| import requests |
| import random |
| import time |
| import sys |
|
|
| API_URL = "http://localhost:8000/predict" |
| SOURCE_DATA = "data/processed/current_data.csv" |
|
|
| |
| MIN_SLEEP = 2 |
| MAX_SLEEP = 8 |
| MIN_BATCH = 1 |
| MAX_BATCH = 5 |
|
|
| print("Starting inference traffic daemon...") |
| print(f"Target API: {API_URL}") |
| print(f"Source data: {SOURCE_DATA}") |
| print("Press Ctrl+C to stop.\n") |
|
|
| |
| try: |
| df = pd.read_csv(SOURCE_DATA) |
| except Exception as e: |
| print("Failed to load source data:", e) |
| sys.exit(1) |
|
|
| required_cols = set(df.columns) |
|
|
| while True: |
| try: |
| |
| batch_size = random.randint(MIN_BATCH, MAX_BATCH) |
| sample = df.sample(batch_size) |
|
|
| |
| csv_bytes = sample.to_csv(index=False).encode("utf-8") |
|
|
| |
| response = requests.post( |
| API_URL, |
| files={"file": ("sample.csv", csv_bytes, "text/csv")}, |
| timeout=10, |
| ) |
|
|
| if response.status_code == 200: |
| payload = response.json() |
| print( |
| f"[OK] rows={payload['n_rows']} " |
| f"predictions_logged=True" |
| ) |
| else: |
| print( |
| f"[WARN] HTTP {response.status_code} " |
| f"{response.text}" |
| ) |
|
|
| except KeyboardInterrupt: |
| print("\nTraffic daemon stopped by user.") |
| break |
|
|
| except Exception as e: |
| print("[ERROR] Inference request failed:", e) |
|
|
| |
| sleep_time = random.uniform(MIN_SLEEP, MAX_SLEEP) |
| time.sleep(sleep_time) |
|
|
