Virtual-AI-Gemini-maixCAM-chatGPT-3.5 Documentation

Welcome to the Virtual-AI-Gemini-maixCAM-chatGPT-3.5 project – a cutting-edge, modular system designed for real-time fall detection with integrated intelligent virtual assistant support. This project leverages advanced machine learning, computer vision, and conversational AI to deliver a robust solution for monitoring and alerting in environments where fall detection is critical.

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Project Overview

Virtual-AI-Gemini-maixCAM-chatGPT-3.5 is built to:

• Capture Video Streams: Utilize maixCAM (or any compatible camera) for real-time video feed.
• Detect Falls: Analyze video frames using a TensorFlow-based ML model to determine if a fall has occurred.
• Notify Stakeholders: Send immediate alerts via Telegram when a fall is detected.
• Engage a Virtual Assistant: Use the ChatGPT API to offer intelligent insights, context-aware support, and conversation-based troubleshooting through a virtual assistant.

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Architecture and Modules

The project is designed in a modular fashion to promote maintainability and extensibility. Each service or module is responsible for a specific aspect of the overall system.

1. Camera Service

• File: services/maxcam_service.py
• Purpose:
  Provides an interface for capturing video frames from the maixCAM device. It handles camera initialization, setting the desired resolution, frame acquisition, and proper resource cleanup.
• Key Features:
  • Connection with maixCAM hardware.
  • Frame display for debugging and visual feedback.
  • Easy integration with downstream services.

2. Fall Detection Service

• File: fall_service.py
• Purpose:
  Implements an ML-based approach for fall detection. The service loads a pre-trained TensorFlow model, preprocesses the captured frames, and runs inference to determine whether a fall has occurred.
• Key Features:
  • Model loading and error handling.
  • Frame preprocessing (resizing, normalization, etc.).
  • Inference logic with a configurable probability threshold.
  • Real-time performance for continuous video stream analysis.

3. Notification Service

• File: notification_service.py
• Purpose:
  Sends alert notifications via the Telegram Bot API when a fall is detected. This allows for immediate human intervention or further automated processes.
• Key Features:
  • Integration with Telegram using secure API calls.
  • Flexible configuration for Telegram Bot Token and Chat ID.
  • Logging of successful and failed notification attempts.

4. GPT Service

• File: gpt_service.py
• Purpose:
  Acts as a fully smart virtual assistant by interfacing with the ChatGPT API. This service maintains conversation context, allowing for multi-turn dialogue and providing intelligent responses based on user prompts.
• Key Features:
  • Conversation context management to provide contextual and coherent responses.
  • Retry logic to handle API failures gracefully.
  • Detailed logging for debugging and operational transparency.
  • Customizable system prompt to define the assistant's persona.

5. REST API Service

• File: main.py
• Purpose:
  Exposes a REST API using Flask, allowing clients to send images for fall detection and triggering appropriate responses. The API integrates fall detection, notification, and optionally the virtual assistant functionalities.
• Key Features:
  • Endpoint /detect_fall for processing uploaded images.
  • JSON responses indicating fall detection results and alert status.
  • Seamless integration with the other modules (Fall Detection and Notification).

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How It Works

1. Video Capture:
   The system initializes the maixCAM through the Camera Service. It continuously captures video frames, which are forwarded to the Fall Detection Service.

2. Fall Detection:
   Each frame is preprocessed (resized, normalized, etc.) and passed to the ML model in the Fall Detection Service. If the model determines that the fall probability exceeds a preset threshold, a fall event is triggered.

3. Notification:
   On detecting a fall, the Notification Service sends an immediate alert message to a pre-configured Telegram chat. This ensures that responsible parties are notified in real-time.

4. Virtual Assistant Interaction:
   The GPT Service, acting as a virtual assistant, can engage in multi-turn conversations to help interpret the event, offer troubleshooting advice, or provide further guidance. The conversation context is maintained across interactions to offer a “smart” and personalized experience.

5. API Exposure:
   The REST API, exposed via Flask in main.py, provides an interface for external systems to interact with the fall detection engine. Clients can send images, receive analysis results, and trigger notifications.