A mobile app designed to assist visually impaired individuals by leveraging object detection, text-to-speech conversion, and environmental interaction features. The app uses Flutter and Dart to create a user-friendly interface, making it easier for users to navigate and interact with their surroundings.
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Object Detection: Utilize machine learning models to track and identify objects in the user's surroundings using the mobile camera.
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Text-to-Speech Conversion: Convert text information, such as object labels or environmental details, into spoken words to aid users in understanding their surroundings.
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Adaptive Interface: Design an adaptive and user-friendly interface with features like gesture controls, voice commands, and customizable settings to accommodate various user preferences.
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Accessibility: Ensure compatibility with screen readers and adherence to accessibility guidelines, making the app usable for individuals with visual impairments.
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Real-time Interaction: Enable real-time interaction with the camera feed, allowing users to receive instant feedback about objects and their environment.
These instructions will help you get a copy of the project up and running on your local machine for development and testing purposes.
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Clone the repository:
git clone https://github.com/Manu-N-S/ZILCKATHON-HFT-SerVIsta.git
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Navigate to the project directory:
cd ZILCKATHON-HFT-SerVIsta
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Install dependencies:
flutter pub get
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Running the App
Now that you have the project and its dependencies installed, you can run the app on your local machine.
```bash
flutter run
Llava7B is a versatile multi-model AI designed for extracting features from both images and text inputs. This README provides a quick guide to the most important features for seamless integration into your project.
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Multi-Model Support:
- Extract features from both image and text inputs, providing a unified solution for diverse data types.
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Feature Extraction:
- Identify patterns, structures, and relevant information in input data for valuable insights.
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Easy Integration:
- Simple API for seamless integration into existing projects, minimizing development effort.