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import pandas as pd
import pickle
class PredictionModel:
def __init__(self, model_path, scaler_path):
# Load the trained model
with open(model_path, 'rb') as model_file:
self.model = pickle.load(model_file)
# Load the scaler
with open(scaler_path, 'rb') as scaler_file:
self.scaler = pickle.load(scaler_file)
def predict(self, features, feature_columns):
try:
# Convert features to DataFrame with proper column names
features_df = pd.DataFrame([features], columns=feature_columns)
# Scale features using the scaler
scaled_features = self.scaler.transform(features_df) # Make sure features_df has the correct column names
scaled_features = pd.DataFrame(scaled_features, columns=feature_columns)
# Make predictions
prediction = self.model.predict(scaled_features)
probability = self.model.predict_proba(scaled_features)[0][1]
return {
"prediction": int(prediction[0]),
"probability": probability
}
except Exception as e:
print(f"Error during prediction: {e}")
return None
# Example usage
if __name__ == "__main__":
model_path = "src/models/svm_model.pkl" # Path to the trained model
scaler_path = "src/models/scaler.pkl" # Path to the saved scaler
# Example input features (they should match the training data structure)
feature_columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',
'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
input_features = [6,148.0,72.0,35.0,155.5482233502538,33.6,0.627,50] # Replace with actual input
predictor = PredictionModel(model_path, scaler_path)
result = predictor.predict(input_features, feature_columns)
if result:
print(f"Prediction: {result['prediction']}, Probability: {result['probability']}")
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