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Pole_Managements_02 / modules /ai_engine.py

Create ai_engine.py

2856072 verified over 1 year ago

622 Bytes

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import pandas as pd

class AIEngine:
    def __init__(self):
        self.model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
        self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
    
    def predict_health(self, df):
        # Example prediction logic (using simple logic for now)
        df['HealthScore'] = df['Solar Gen (kWh)'].apply(lambda x: 1.0 if x > 5 else 0.5)
        df['ML_Anomaly'] = df['HealthScore'].apply(lambda x: 'Normal' if x > 0.7 else 'Risk')
        return df

	import torch
	from transformers import AutoModelForSequenceClassification, AutoTokenizer
	import pandas as pd

	class AIEngine:
	def __init__(self):
	self.model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
	self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

	def predict_health(self, df):
	# Example prediction logic (using simple logic for now)
	df['HealthScore'] = df['Solar Gen (kWh)'].apply(lambda x: 1.0 if x > 5 else 0.5)
	df['ML_Anomaly'] = df['HealthScore'].apply(lambda x: 'Normal' if x > 0.7 else 'Risk')
	return df