ClaimReady

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  Welcome Page

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  The Process and Upload

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  LocalDB Visualization

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  CSV Output

Inspiration

For decades, wildfires have been a growing crisis for the American public, destroying homes and livelihoods. The January 2025 wildfires, among the most devastating in history, have left thousands of homeowners grieving and uncertain about their losses. Every wildfire season, billions of dollars in insurance claims are filed by individuals painstakingly piecing together the value of their belongings from old photos and memory. This slow, stressful process adds another burden to an already overwhelming situation. Our goal is to change that—by leveraging AI, we streamline the valuation process, providing homeowners with accurate estimates of their lost belongings, reducing uncertainty, and helping them rebuild faster.

What it does

At the core of our solution are three key pillars: Scan, Value, and Protect—each designed to simplify and streamline the insurance claims process for wildfire victims. Scan: Homeowners can effortlessly upload images or videos of their belongings, allowing our AI-powered system to detect and catalog each item automatically. Value: Using advanced reverse image search, our platform retrieves real-time market prices for every identified item, ensuring homeowners receive fair and accurate valuations for their insurance claims. Protect: By providing homeowners with an accurate, AI-driven record of their belongings, we help them safeguard the value of their possessions and ensure they receive fair compensation in the wake of disaster.

How we built it

Our website automates lost item valuation using AI and a structured backend. Users upload images and an address, which the frontend converts to Base64 before sending to a Flask API. The backend processes the image, segments objects using a YOLO v8 model, and uploads them to Imgur, stripping metadata for privacy. Google Lens performs a reverse image search to extract item details like name, brand, and price. The compiled data is sent to the frontend, stored locally, and allows users to edit values. The system sums item values, retrieves address-based pricing, and exports data as a CSV for insurance claims—replacing manual cataloging with a fast, AI-driven solution.

Challenges we ran into

One of our biggest challenges was finding an API that could reverse search an image directly from a file, but no existing solution fit our needs. Instead of abandoning the feature, we engineered the entire process ourselves. We discovered that the Google Lens API could perform reverse searches but only accepted image links, not local files. To work around this, we integrated Imgur’s API to securely upload images and generate shareable URLs, allowing us to feed them into Google Lens. Once we got image detection and valuation working, the next hurdle was integrating the frontend and backend, ensuring seamless communication between Flask, React, and our database. Finally, we refined the JSON output, transforming raw data into a readable, user-friendly format.

Accomplishments that we're proud of

We are particularly proud of developing a streamlined platform that simplifies the tedious process of inventorying and valuing lost belongings, providing disaster victims with a practical and efficient solution. Successfully integrating YOLOv8 for object detection and leveraging Google Lens for pricing validation showcases our ability to apply advanced AI models to real-world challenges. Additionally, our secure and seamless backend workflow, which ensures reliable data handling, represents a major achievement in building a scalable and trustworthy system. Most importantly, ClaimReady has the potential to make a meaningful impact by easing the recovery process for individuals affected by wildfires and other natural disasters.

What we learned

Developing ClaimReady provided valuable insights into the intersection of AI, disaster recovery, and user experience. We gained a deeper understanding of object detection models like YOLOv8 and their role in accurately cataloging personal belongings. Integrating reverse image search for valuation taught us the challenges of pricing accuracy and data reliability. Working with APIs, including Google Lens and Imgur, reinforced the importance of secure data handling and seamless backend workflows. Most importantly, this project highlighted the real-world impact of technology in aiding disaster victims, emphasizing the need for intuitive, scalable solutions that simplify recovery processes.

What's next for ClaimReady

Moving forward, we plan to expand ClaimReady by enhancing its AI capabilities and broadening its dataset to improve accuracy and usability. We aim to integrate more advanced machine learning models for object recognition, allowing for greater precision in identifying and valuing lost belongings. Additionally, we plan to incorporate multiple data sources for pricing validation, ensuring that users receive the most accurate estimates. To enhance user experience, we will refine our frontend interface and implement seamless integration with insurance providers, streamlining the claims process. As we continue to develop ClaimReady, our ultimate goal is to establish it as a trusted and indispensable resource for disaster victims, empowering them with the tools they need to recover efficiently and rebuild their lives.

Built With

• flask-api
• google-lens-api
• imgurapi
• localdb
• python
• react
• yolov8