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right context with sdk

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- license: openrail
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- license_name: open-rail-m
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- license_link: https://raw.githubusercontent.com/Clay-foundation/model/main/LICENSE-MODEL.md
 
 
 
 
 
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- # Clay Foundation Model
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- ## An open source AI model for Earth
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- Clay is a foundational model of Earth. It uses an expanded visual transformer upgraded to understand geospatial and temporal relations on Earth data. The model is trained as a self-supervised Masked Autoencoder (MAE).
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- The Clay model can be used in three main ways:
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- - Generate semantic embeddings for any location and time.
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- - Fine-tune the model for downstream tasks such as classification, regression, and generative tasks.
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- - Use the model as a backbone for other models.
 
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- ## Where is what
 
 
 
 
 
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- - Our **website** is [madewithclay.org](https://madewithclay.org).
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- - The Clay model **code** lives on [Github](https://github.com/Clay-foundation/model). License: [Apache](https://github.com/Clay-foundation/model/LICENSE). The latest release is [v0.0.1](https://github.com/Clay-foundation/model/releases/tag/v0.0.1)
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- - The Clay model **weights** on [Hugging Face](https://huggingface.co/made-with-clay/Clay/). License: [OpenRAIL-M](https://github.com/Clay-foundation/model/blob/main/LICENSE-MODEL.md).
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- - The Clay **documentation** [lives on this site](https://clay-foundation.github.io/model/). License: [CC-BY](http://creativecommons.org/licenses/by/4.0/).
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- - *Coming Soon* > We maintain a set of **embeddings** on [Source Cooperative](https://beta.source.coop/clay/). License: [ODC-BY](https://opendatacommons.org/licenses/by/).
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- CLAY is a fiscal sponsored project of the 501c3 non-profit [Radiant Earth Foundation](https://www.radiant.earth/).
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+ title: Terrasentinels
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+ emoji: πŸ“š
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+ colorFrom: pink
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+ colorTo: yellow
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+ sdk: streamlit
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+ sdk_version: 1.38.0
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+ app_file: app.py
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+ pinned: false
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+ # Data Fetcher AI Agent
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+ This Streamlit app simulates fetching Sentinel-2 satellite imagery data and storing it in a PostgreSQL database. It's designed to demonstrate the process of data collection and storage for further analysis using tools like MindsDB for anomaly detection.
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+ ## Features
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+ - Input your OpenAI API key securely
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+ - Input geographical coordinates (latitude and longitude)
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+ - Specify date range for data collection
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+ - Simulate data storage in PostgreSQL
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+ - Prepare data for analysis with MindsDB
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+ ## How to Use
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+ 1. Enter your OpenAI API key in the sidebar (this is required to run the app)
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+ 2. Enter your Earth Engine Project ID (or use the default)
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+ 3. Input the latitude and longitude of your area of interest
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+ 4. Select the start and end dates for your data collection period
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+ 5. Provide a name for your image
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+ 6. Click "Fetch Sentinel-2 Image" to simulate the data collection and storage process
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+ ## Security Note
 
 
 
 
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+ Your API key is not stored and is only used for the current session. It's securely handled and not displayed after entry.
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+ ## Note
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+ This app is a simulation and does not actually connect to Earth Engine or store data in PostgreSQL. It's designed to demonstrate the user interface and workflow of such a system.
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+ ## Future Improvements
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+ - Implement actual connection to Earth Engine
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+ - Set up real-time data storage in PostgreSQL
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+ - Integrate MindsDB for anomaly detection and data analysis
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+ Feel free to contribute to this project or use it as a starting point for your own data analysis workflows!