Differences

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--- ai_interface_perdiction [2025/05/27 20:09] – [Key Features] eagleeyenebula
+++ ai_interface_perdiction [2025/05/27 20:17] (current) – [Best Practices] eagleeyenebula
@@ Line 44: / Line 44: @@
 ===== Class Overview =====
-```python
+<code>
+python
 import logging
@@ Line 71: / Line 72: @@
         logging.info(f"Predictions: {predictions}")
         return predictions
-```
+</code>
 **Core Attributes**:
-- `model`: The AI model instance responsible for generating predictions.
+  * **model**: The AI model instance responsible for generating predictions.
-- `handle_prediction_request(input_data)`: Method that handles input data, generates predictions, and logs activity.
+  * **handle_prediction_request(input_data)**: Method that handles input data, generates predictions, and logs activity.
----
 ===== Modular Workflow =====
 . **Initialize Interface with Model**:
-   Pass any compatible machine learning model instance to the `PredictionInterface` during initialization.
+   * Pass any compatible machine learning model instance to the `PredictionInterface` during initialization.
 . **Handle Prediction Requests**:
-   Use the `handle_prediction_request()` method to process incoming data and retrieve prediction results.
+   * Use the `handle_prediction_request()` method to process incoming data and retrieve prediction results.
 . **Extend for Functionality**:
-   Add input pre-processing, output post-processing, or advanced error handling as required.
+   * Add input pre-processing, output post-processing, or advanced error handling as required.
----
 ===== Usage Examples =====
 Here are practical and advanced examples that demonstrate how to use the **PredictionInterface** class for real-world machine learning applications.
----
 ==== Example 1: Basic Mock Prediction ====
 This example demonstrates using the `PredictionInterface` with placeholder logic for mock prediction.
-```python
+<code>
+python
 from ai_interface_prediction import PredictionInterface
+</code>
-# Mock model (placeholder for an actual ML model)
+**Mock model (placeholder for an actual ML model)**
+<code>
 mock_model = None
+</code>
-# Initialize the PredictionInterface
+**Initialize the PredictionInterface**
+<code>
 interface = PredictionInterface(mock_model)
+</code>
-# Input data for prediction
+**Input data for prediction**
+<code>
 input_data = [1, 2, 3, 4, 5]
+</code>
-# Perform prediction
+**Perform prediction**
+<code>
 predictions = interface.handle_prediction_request(input_data)
 print("Predictions:", predictions)
@@ Line 122: / Line 119: @@
 # INFO:root:Predictions: [2, 4, 6, 8, 10]
 # Predictions: [2, 4, 6, 8, 10]
-```
+</code>
 **Explanation**:
-- Uses the mock prediction logic (`x * 2`) as a placeholder for real AI model predictions.
+   * Uses the mock prediction logic (`x * 2`) as a placeholder for real AI model predictions.
-- Logs the prediction process for traceability.
+   * Logs the prediction process for traceability.
----
 ==== Example 2: Integrating with a Pre-Trained Model ====
 Extend the interface by incorporating an actual machine learning model, such as a scikit-learn or TensorFlow model.
-```python
+<code>
+python
 from sklearn.linear_model import LinearRegression
 import numpy as np
+</code>
-# Define a simple linear regression model and train it
+**Define a simple linear regression model and train it**
+<code>
 model = LinearRegression()
 X = np.array([[1], [2], [3], [4], [5]])  # Features
 y = np.array([2, 4, 6, 8, 10])         # Target values
 model.fit(X, y)
+</code>
-# Integrate the model with the PredictionInterface
+**Integrate the model with the PredictionInterface**
+<code>
 interface = PredictionInterface(model)
+</code>
-# Prediction input data
+**Prediction input data**
+<code>
 input_data = np.array([[6], [7], [8]])
+</code>
-# Perform prediction using the trained model
+**Perform prediction using the trained model**
+<code>
 predictions = interface.handle_prediction_request(input_data)
 print("Predictions:", predictions)
-```
+</code>
 **Explanation**:
-- Replaces the placeholder logic with real predictions from a trained scikit-learn `LinearRegression` model.
+   * Replaces the placeholder logic with real predictions from a trained scikit-learn `LinearRegression` model.
-- Adapts for advanced scenarios with actual models.
+  * Adapts for advanced scenarios with actual models.
----
 ==== Example 3: Adding Input Validation ====
 This example adds validation to ensure input data integrity.
-```python
+<code>
+python
 class ValidatingPredictionInterface(PredictionInterface):
     """
@@ Line 179: / Line 176: @@
         return super().handle_prediction_request(input_data)
+</code>
-# Usage
+**Usage**
+<code>
 interface = ValidatingPredictionInterface(None)
@@ Line 187: / Line 185: @@
 except ValueError as e:
     print(e)  # Output: Input data must be a list of numeric values.
-```
+</code>
 **Explanation**:
-- Ensures only numeric data is passed to the prediction process, preventing invalid inputs.
+   * Ensures only numeric data is passed to the prediction process, preventing invalid inputs.
----
 ==== Example 4: Batch Predictions with Logging ====
 This example improves the interface by introducing batch processing.
-```python
+<code>
+python
 class BatchPredictionInterface(PredictionInterface):
     """
@@ Line 215: / Line 211: @@
             all_predictions.append(self.handle_prediction_request(batch))
         return all_predictions
+</code>
+**Usage**
-# Usage
+<code>
 interface = BatchPredictionInterface(None)
 batch_data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
+</code>
-# Perform batch predictions
+**Perform batch predictions**
+<code>
 batch_results = interface.batch_predictions(batch_data)
 print("Batch Predictions:", batch_results)
@@ Line 229: / Line 227: @@
 # INFO:root:Processing batch: [4, 5, 6]
 # INFO:root:Processing batch: [7, 8, 9]
-```
+</code>
 **Explanation**:
-- Designed for scenarios requiring predictions over multiple datasets in a single operation.
+   * Designed for scenarios requiring predictions over multiple datasets in a single operation.
----
 ==== Example 5: Persistent Prediction Results ====
 Save prediction results to a file for further analysis.
-```python
+<code>
+python
 import json
@@ Line 257: / Line 253: @@
             json.dump(predictions, file)
         logging.info(f"Predictions saved to {filename}.")
+</code>
+**Usage**
-# Usage
+<code>
 interface = PersistentPredictionInterface(None)
 predictions = interface.handle_prediction_request([1, 2, 3])
 interface.save_predictions(predictions, "predictions.json")
-```
+</code>
 **Explanation**:
-- Ensures prediction results can be stored and loaded later by saving them in a JSON file.
+   * Ensures prediction results can be stored and loaded later by saving them in a JSON file.
----
 ===== Use Cases =====
 . **Real-Time Model Serving**:
-   Create a prediction-serving pipeline for real-time applications (e.g., APIs).
+   * Create a prediction-serving pipeline for real-time applications (e.g., APIs).
 . **Batch Prediction Systems**:
-   Efficiently process batch inputs for large datasets.
+   * Efficiently process batch inputs for large datasets.
 . **Data Validation Before Inference**:
-   Ensure input data meets pre-defined conditions (e.g., type checks or range validation).
+   * Ensure input data meets pre-defined conditions (e.g., type checks or range validation).
 . **Logging and Debugging Predictions**:
-   Leverage integrated logging to identify issues during the prediction process.
+   * Leverage integrated logging to identify issues during the prediction process.
 . **Persistent Predictions**:
-   Save results for offline analysis or inclusion in reporting pipelines.
+   * Save results for offline analysis or inclusion in reporting pipelines.
----
 ===== Best Practices =====
 . **Validate Input Data**:
-   Always validate input data before feeding it to machine learning models.
+   * Always validate input data before feeding it to machine learning models.
 . **Implement Error Handling**:
-   Account for potential prediction errors or invalid inputs.
+   * Account for potential prediction errors or invalid inputs.
 . **Optimize for Batch Processing**:
-   Use batch predictions to improve efficiency for applications involving large datasets.
+   * Use batch predictions to improve efficiency for applications involving large datasets.
 . **Leverage Logging**:
-   Enable detailed logging for easier debugging and transparency in prediction outputs.
+   * Enable detailed logging for easier debugging and transparency in prediction outputs.
 . **Integrate with Real Models**:
-   Replace mock logic with actual AI/ML models for robust production-ready systems.
+   * Replace mock logic with actual AI/ML models for robust production-ready systems.
----
 ===== Conclusion =====