aml-project-deploy / verify_extended.py
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Stable deployment version: Lazy loading and Docker optimized
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import sys
import os
import torch
import numpy as np
import pandas as pd
import librosa
from models_loader import loader
def test_extended():
print("--- Starting Extended Model Verification ---")
# 1. STT (Speech to Text)
print("\nTesting STT (Whisper)...")
if loader.stt_pipeline:
try:
# Create a 1-second silent audio array
audio_array = np.zeros(16000, dtype=np.float32)
res = loader.stt_pipeline(audio_array)
print(f"STT Result: {res}")
except Exception as e:
print(f"FAILED: STT pipeline error: {e}")
else:
print("FAILED: STT pipeline not loaded")
# 2. DBSCAN
print("\nTesting DBSCAN...")
try:
from sklearn.cluster import DBSCAN
data = np.random.rand(10, 2)
db = DBSCAN(eps=0.3, min_samples=2).fit(data)
print(f"DBSCAN labels: {db.labels_}")
except Exception as e:
print(f"FAILED: DBSCAN error: {e}")
# 3. Apriori
print("\nTesting Apriori...")
try:
from mlxtend.frequent_patterns import apriori, association_rules
from mlxtend.preprocessing import TransactionEncoder
dataset = [['Milk', 'Onion', 'Nut', 'Kidney Beans', 'Eggs', 'Yogurt'],
['Dill', 'Onion', 'Nut', 'Kidney Beans', 'Eggs', 'Yogurt'],
['Milk', 'Apple', 'Kidney Beans', 'Eggs'],
['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],
['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]
te = TransactionEncoder()
te_ary = te.fit(dataset).transform(dataset)
df = pd.DataFrame(te_ary, columns=te.columns_)
freq = apriori(df, min_support=0.6, use_colnames=True)
rules = association_rules(freq, metric="lift", min_threshold=0.7)
print(f"Apriori rules found: {len(rules)}")
except Exception as e:
print(f"FAILED: Apriori error: {e}")
print("\n--- Extended Verification Complete ---")
if __name__ == "__main__":
test_extended()