Inspiration
Our inspiration came from the difficulty people face in identifying a snake after a snakebite. In many situations, people may not know the snake's name or whether it is venomous. This can create confusion and delay appropriate medical attention. We wanted to build a simple web page that can help users identify the snake based on an image and provide useful information quickly.
What it does
Our web page allows users to upload or capture an image of a snake. The system analyzes the image and attempts to identify the snake species. It then displays the possible snake name along with important information such as whether it is venomous and other relevant details.
The main goal is to provide quick identification support during an emergency, while making it clear that the result should not replace professional medical treatment.
How we built it
We built the project using web technologies such as HTML, CSS, and JavaScript for the user interface and functionality.
The basic workflow is:
User uploads or captures a snake image.
The image is processed by the identification system.
The system predicts the possible snake species.
The result is displayed on the web page.
Important safety and emergency information is provided to the user.
For future development, the system can be connected to a trained machine-learning/computer-vision model containing images of different snake species.
Challenges we ran into
Identifying snakes accurately from images can be difficult because different species can look very similar.
Images may have poor lighting, low resolution, or unclear views of the snake.
Building a system that works reliably with different snake species requires a large and diverse dataset.
Preventing incorrect identification is especially important because the application may be used after a snakebite.
Designing a simple interface that can be used quickly during an emergency was another challenge.
Accomplishments that we're proud of
Created a simple and user-friendly web interface for snake identification.
Developed a workflow for uploading and analyzing snake images.
Made the concept accessible even to users who may not have knowledge about snake species.
Focused on making the system useful for emergency awareness and quick information.
Created a foundation that can later be improved with a more accurate AI model and a larger snake-image dataset.
What we learned
Through this project, we learned how web development, artificial intelligence, and real-world problem solving can be combined to address a practical safety issue.
We also learned that AI-based identification systems need high-quality and diverse training data to produce reliable results. Most importantly, we learned that when technology is used in a medical or emergency-related situation, accuracy, clear communication, and safety must be prioritized over simply producing a prediction.
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