Classify Manufacturing Defects with GLM-4.5V and Weights & Biases

                      
                     
from typing import List, Dict, Any
import os
import wandb
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

# Configuration
API_KEY = os.getenv('WANDB_API_KEY')
wandb.login(key=API_KEY)

# Initialize Weights & Biases
wandb.init(project='manufacturing_defects')

# Load dataset
# Replace with actual dataset path
data = pd.read_csv('defects_data.csv')

# Data Preprocessing
X = data.drop('defect_label', axis=1)
Y = data['defect_label']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)

# Model Training
model = LogisticRegression(max_iter=1000)
try:
    model.fit(X_train, Y_train)
except Exception as e:
    print(f'Error during model training: {str(e)}')

# Model Evaluation
try:
    predictions = model.predict(X_test)
    report = classification_report(Y_test, predictions)
    print(report)
    wandb.log({'classification_report': report})
except Exception as e:
    print(f'Error during model evaluation: {str(e)}')

if __name__ == '__main__':
    wandb.finish()