Introduction
The ai_interface_perdiction.py module bridges AI models with user-facing interfaces by enabling real-time or batch prediction.
It supports integrating machine learning models into RESTful APIs, UI components, or automated systems seamlessly.
The purpose is to allow system interfaces to directly fetch predictive insights from AI models.
The module implements robust, lightweight, and scalable prediction mechanisms that handle data preparation, error handling, and response formatting. With extensibility in mind, this module is designed to allow rapid inclusion of multiple ML models into production workflows.
Purpose
- Serve as an intermediary layer between AI models and system interfaces.
- Provide normalized predictions suitable for integration with UI, API, or automation processes.
- Support batch and real-time prediction requests effectively.
- Handle preprocessing, postprocessing, and error recovery during prediction workflows.
Key Features
- Model Prediction: Facilitate predictions from one or more machine learning models.
- Error Resolution: Capture and handle preprocessing or runtime prediction errors.
- Data Normalization: Automatically normalize input data to the format expected by the model.
- Batch Prediction: Support batch predictions for bulk predictions in high-throughput workflows.
- Extensible: Easily add support for integrating additional machine learning or deep learning models.
Logic and Implementation
This script provides a centralized prediction handler for ML models. Below is a conceptual implementation:
import joblib
import numpy as np
import os
import logging
class InterfacePrediction:
"""
AI Interface Prediction layer for ML/DL workflows.
"""
def __init__(self, model_path="models/model.pkl"):
"""
Initialize the predictor.
:param model_path: Path to the serialized ML/DL model.
"""
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found at {model_path}")
self.model = joblib.load(model_path)
logging.info("[INFO] Model loaded successfully.")
def preprocess_input(self, data):
"""
Preprocess input data into the format expected by the model.
:param data: Raw input data for the model.
:return: Preprocessed data.
"""
try:
# Example: Ensure input is a numpy array
processed_data = np.array(data, dtype=np.float32)
return processed_data
except Exception as e:
logging.error("[ERROR] Preprocessing failed: " + str(e))
raise ValueError("Invalid input data format.")
def predict(self, data):
"""
Use the model to make predictions on the provided data.
:param data: Input data for prediction.
:return: Model predictions.
"""
data = self.preprocess_input(data)
predictions = self.model.predict(data)
# Format predictions (example: Convert to list for easier serialization)
return predictions.tolist()
def batch_predict(self, batch_data):
"""
Batch prediction for multiple examples.
:param batch_data: List of input datasets for prediction.
:return: List of predictions for all inputs.
"""
try:
batch_predictions = [self.predict(data) for data in batch_data]
return batch_predictions
except Exception as e:
logging.error("[ERROR] Batch prediction failed: " + str(e))
return {"error": "Batch prediction failed. Check input data."}
# Example Usage
if __name__ == "__main__":
predictor = InterfacePrediction(model_path="models/model.pkl")
# Single prediction
input_data = [5.1, 3.5, 1.4, 0.2] # Example input
single_prediction = predictor.predict(input_data)
print("Single Prediction:", single_prediction)
# Batch prediction
batch_data = [[5.1, 3.5, 1.4, 0.2], [6.2, 3.2, 4.7, 1.4]]
batch_predictions = predictor.batch_predict(batch_data)
print("Batch Predictions:", batch_predictions)
Dependencies
joblib: Used for loading serialized machine learning models.numpy: Handles numerical data processing.logging: Logs prediction-related processes to monitor and debug issues.os: Handles file/directory operations.
Usage
The InterfacePrediction class is designed to simplify integrating AI-generated predictions into
APIs, dashboards, or command-line scripts. Below is a usage example:
# Initialize the predictor
predictor = InterfacePrediction(model_path="models/iris_model.pkl")
# Perform a single prediction
print(predictor.predict([6.0, 3.0, 4.8, 1.8]))
# Perform batch predictions
print(predictor.batch_predict([[6.0, 3.0, 4.8, 1.8], [5.4, 3.9, 1.7, 0.4]]))
System Integration
- REST APIs: Use predictions as backend responses in REST frameworks like Flask or FastAPI.
- Dashboards: Display live or batch predictions on user-facing interfaces.
- Automation: Integrate predictive workflows into business automation pipelines.
Future Enhancements
- Add support for ONNX or TensorFlow models.
- Introduce asynchronous prediction for faster processing of large batches.
- Implement configurable preprocessing pipelines via JSON configuration files.
- Support cloud-hosted model APIs for distributed inference.