garbAIge

garbAIge model sorts dumped waste into categories: organic, hazardous, solid, liquid, recyclable with % env impact amounts.

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Inspiration --->

In the face of modern environmental challenges, including pollution and climate change, I found inspiration to develop a generative AI model dedicated to addressing these issues. Leveraging the power of the Gemini API, my aim is to create a garbAIge gen-AI model capable of accurately analyzing images of garbage and efficiently categorizing them for proper recycling. By harnessing the potential of artificial intelligence, we can streamline waste management processes, enhance recycling efforts, and promote environmental sustainability. Through innovation and technology, we can empower individuals and communities to make a meaningful impact in preserving our planet for future generations.

What it does --->

The garbAIge model utilizes the Gemini API to sort dumped waste into categories: organic, hazardous, solid, liquid, and recyclable, providing percentages of environmental impact caused by each category. With this capability, it facilitates informed waste management decisions, contributing to environmental sustainability and resource conservation.

How we built it --->

I built the garbAIge model by integrating the Gemini Pro Vision model with the Gemini API, Streamlit, Python, and PIL libraries. This allowed me to develop an interactive web application capable of analyzing uploaded images of garbage and generating insights on waste composition and environmental impact. Through collaborative efforts and leveraging advanced AI technologies, I aimed to enhance waste management practices and promote sustainable living.

Challenges we ran into --->

I encountered challenges integrating the Gemini Pro Vision model with the application, particularly in handling image data processing and managing API interactions effectively. Ensuring the accuracy and reliability of the model's analysis posed difficulties in interpreting diverse types of garbage images accurately. Despite these obstacles, I persisted through collaborative problem-solving and experimentation to deliver a robust and functional solution.

Accomplishments that we're proud of --->

I'm proud of successfully integrating the Gemini Pro Vision model into the garbAIge application, enabling accurate analysis of garbage images and categorization into relevant waste types. Additionally, achieving a user-friendly interface through Streamlit and providing valuable insights on waste composition and environmental impact marks significant accomplishments. Finally, contributing to environmental sustainability efforts by promoting informed waste management decisions is a source of pride in this project.

What we learned --->

Through this project, I gained hands-on experience in utilizing the Gemini-Pro-Vision API, unlocking its potential to revolutionize waste management practices. I deepened my understanding of AI-driven solutions and their role in addressing pressing global challenges. Moreover, I learned the intricacies of integrating external APIs into web applications, enhancing my technical skill set. Additionally, I gained a newfound appreciation for the power of technology to drive positive change and promote sustainability. Overall, this experience broadened my perspective and equipped me with valuable knowledge for future endeavors in leveraging AI for societal impact.

What's next for garbAIge ---> In Future I would like to make this model more accurate and if this can be used in any waste cleaning vehicle ,then without any human help all wastes will be properly segregated and recycled for a BETTER , CLEANER & GREENER Mother Earth. Finally I would like to end with a quote of Andrew NG --- "Artificial Intelligence is the new Electricity"

Built With

  • gemini-api
  • gemini-pro-vision
  • google-generativeai
  • pil
  • python
  • python-dotenv
  • streamlit
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