Commit a0deb9f6 authored by Waleed Akbar's avatar Waleed Akbar
Browse files

feat: Implement AI Analytics Engine Skeletion - REST API with Flask 

- Added API blueprint for AI Analytics Engine with endpoints for analysis, health check, and configuration.
- Created clients for interacting with SIMAP and InfluxDB services.
- Implemented decision client for sending analysis results.
- Developed configuration module to manage environment settings.
- Established main orchestrator for initializing and running the Flask application.
- Added tests for API endpoints to ensure functionality and validate responses.
- Included deployment script for building and running the AI Analytics Engine in Docker.
parent de9a392d

src/tests/mwc26-f5ga/AI_analytics_engine/Dockerfile

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# Copyright 2022-2025 ETSI SDG TeraFlowSDN (TFS) (https://tfs.etsi.org/)

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#      http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



FROM python:3.13-slim



# Install system dependencies

RUN apt-get update && apt-get install -y --no-install-recommends \

    gcc \

    && rm -rf /var/lib/apt/lists/*



# Set working directory

WORKDIR /app



# Copy common requirements and AI engine requirements

COPY common_requirements_py313.in /app/common_requirements.in

COPY src/tests/mwc26-f5ga/AI_analytics_engine/requirements.in /app/ai_requirements.in



# Install Python dependencies

RUN pip install --no-cache-dir pip-tools && \

    pip-compile --output-file=requirements.txt common_requirements.in ai_requirements.in && \

    pip install --no-cache-dir -r requirements.txt



# Copy minimal TFS common module (required for Settings, etc.)

COPY src/common /app/src/common



# Copy AI Analytics Engine module

COPY src/tests/mwc26-f5ga/AI_analytics_engine /app/src/tests/mwc26-f5ga/AI_analytics_engine



# Set PYTHONPATH to include src directory

ENV PYTHONPATH=/app/src



# Expose default REST API port

EXPOSE 8080



# Set default environment variables (can be overridden at runtime)

ENV AI_ENGINE_REST_HOST=0.0.0.0

ENV AI_ENGINE_REST_PORT=8080

ENV SIMAP_SERVER_SCHEME=http

ENV LOG_LEVEL=INFO



# Note: SIMAP_SERVER_ADDRESS, SIMAP_SERVER_PORT, SIMAP_SERVER_USERNAME,

# and SIMAP_SERVER_PASSWORD should be provided at runtime via --env flags



# Run the AI Analytics Engine

CMD ["python", "-m", "tests.mwc26-f5ga.AI_analytics_engine"]

src/tests/mwc26-f5ga/AI_analytics_engine/__init__.py

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# Copyright 2022-2025 ETSI SDG TeraFlowSDN (TFS) (https://tfs.etsi.org/)

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#      http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



"""

AI Analytics Engine module.



Provides REST API for AI-driven SLA policy analysis and violation detection

using data from SIMAP (network topology/devices) and InfluxDB (telemetry metrics).



Module Structure:

    - ai_model: AI/ML processing logic and SLA policy definitions

    - api: Flask REST API endpoints

    - clients: External service clients (SIMAP, InfluxDB, Decision Engine)

    - config: Configuration management

    - tests: Test suite



Public API:

    - AIAnalyticsEngineAPI: Main orchestrator and Flask application

    - AIModelProcessor: AI/ML analysis engine

    - SLAPolicyConfig: SLA policy configuration data model

    - SimapDataFetcher: SIMAP client for device/topology data

    - InfluxDBFetcher: InfluxDB client for telemetry metrics

    - DecisionEngineClient: Decision engine notification client

    - create_ai_analytics_blueprint: Flask blueprint factory

"""



from .ai_model.ai_processor import AIModelProcessor

from .api.api_blueprint import create_ai_analytics_blueprint

from .engine import AIAnalyticsEngineAPI

from .clients.decision_client import DecisionEngineClient

from .clients.influxdb_fetcher import InfluxDBFetcher

from .clients.simap_fetcher import SimapDataFetcher

from .ai_model.sla_policy import SLAPolicyConfig



__all__ = [

    'AIAnalyticsEngineAPI',

    'AIModelProcessor',

    'DecisionEngineClient',

    'InfluxDBFetcher',

    'SimapDataFetcher',

    'SLAPolicyConfig',

    'create_ai_analytics_blueprint',

]

src/tests/mwc26-f5ga/AI_analytics_engine/__main__.py

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# Copyright 2022-2025 ETSI SDG TeraFlowSDN (TFS) (https://tfs.etsi.org/)

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#      http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



"""

AI Analytics Engine main entry point.



This module provides the entry point for running the AI Analytics Engine REST API.

"""



# Standard library imports

import logging

import sys



# Local imports

from .engine import AIAnalyticsEngineAPI



# Module-level logger

LOGGER = logging.getLogger(__name__)





def main() -> None:

    """Main entry point for the AI Analytics Engine."""

    # Configure logging

    logging.basicConfig(

        level=logging.DEBUG,

        format="[%(asctime)s] %(levelname)s:%(name)s:[%(funcName)s:%(lineno)d] - %(message)s"

    )

    logging.getLogger('werkzeug').setLevel(logging.DEBUG)



    try:

        LOGGER.info("Starting AI Analytics Engine")

        engine = AIAnalyticsEngineAPI()



        logging.getLogger('werkzeug').setLevel(logging.DEBUG)



        engine.run()

    except KeyboardInterrupt:

        LOGGER.info("AI Analytics Engine stopped by user")

        sys.exit(0)

    except Exception as e:

        LOGGER.exception(f"AI Analytics Engine failed to start: {e}")

        sys.exit(1)





if __name__ == '__main__':

    main()

src/tests/mwc26-f5ga/AI_analytics_engine/ai_model/__init__.py

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# Copyright 2022-2025 ETSI SDG TeraFlowSDN (TFS) (https://tfs.etsi.org/)

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#      http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

src/tests/mwc26-f5ga/AI_analytics_engine/ai_model/ai_processor.py

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# Copyright 2022-2025 ETSI SDG TeraFlowSDN (TFS) (https://tfs.etsi.org/)

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#      http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



"""

AI Model Processor module.

Provides AI/ML processing functionality for SLA analysis.

"""



import logging

from datetime import datetime, UTC

from random import Random

from typing import Any, Dict



from .sla_policy import SLAPolicyConfig



LOGGER = logging.getLogger(__name__)





class AIModelProcessor:

    """

    Processes data through AI models for SLA analysis.



    This class implements the AI/ML processing logic to analyze device

    and performance data against SLA policies, detecting violations and

    generating recommendations.

    """



    def __init__(self) -> None:

        """

        Initialize the AIModelProcessor.



        Loads AI models and prepares the processor for data analysis.

        """

        LOGGER.info("AIModelProcessor initialized")

        # TODO: Load AI/ML models here

        # Example: self.model = load_model('sla_violation_detector.h5')



    def process_data(

        self,

        device_data: Dict[str, Any],

        performance_data: Dict[str, Any],

        sla_policy: SLAPolicyConfig

    ) -> Dict[str, Any]:

        """

        Process device and performance data through AI models.



        Analyzes the collected data against the SLA policy thresholds,

        detects violations, and generates recommendations for remediation.



        Args:

            device_data: Device and topology data from SIMAP.

            performance_data: Performance metrics from InfluxDB.

            sla_policy: The SLA policy configuration with thresholds.



        Returns:

            Dictionary containing:

                - 'violations': List of detected SLA violations with details.

                - 'recommendations': List of recommended actions.

                - 'summary': Dictionary with analysis summary statistics.

        """

        LOGGER.debug("Processing data through AI models")

        # TODO: Implement actual AI/ML processing logic

        # Example:

        # violations = self.model.predict(features)

        # recommendations = self.generate_recommendations(violations)

        return {

            'violations': [],

            'confidence_scores': round(Random().random(), 2),  

            'summary': {

            'sla_policy': sla_policy.to_dict(),

            'timestamp': datetime.now(UTC).isoformat()

            }

        }