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README.md
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Simply visit the [Lovable Project](https://lovable.dev/projects/REPLACE_WITH_PROJECT_ID) and start prompting.
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Changes made via Lovable will be committed automatically to this repo.
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**Use your preferred IDE**
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If you want to work locally using your own IDE, you can clone this repo and push changes. Pushed changes will also be reflected in Lovable.
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The only requirement is having Node.js & npm installed - [install with nvm](https://github.com/nvm-sh/nvm#installing-and-updating)
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Follow these steps:
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```sh
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# Step 1: Clone the repository using the project's Git URL.
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git clone <YOUR_GIT_URL>
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# Step 2: Navigate to the project directory.
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cd <YOUR_PROJECT_NAME>
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# Step 3: Install the necessary dependencies.
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npm i
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# Step 4: Start the development server with auto-reloading and an instant preview.
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npm run dev
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```
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**Edit a file directly in GitHub**
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- Navigate to the desired file(s).
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- Click the "Edit" button (pencil icon) at the top right of the file view.
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- Make your changes and commit the changes.
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**Use GitHub Codespaces**
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- Navigate to the main page of your repository.
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- Click on the "Code" button (green button) near the top right.
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- Select the "Codespaces" tab.
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- Click on "New codespace" to launch a new Codespace environment.
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- Edit files directly within the Codespace and commit and push your changes once you're done.
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## What technologies are used for this project?
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This project is built with:
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- Vite
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- TypeScript
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- React
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- shadcn-ui
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- Tailwind CSS
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## How can I deploy this project?
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Simply open [Lovable](https://lovable.dev/projects/REPLACE_WITH_PROJECT_ID) and click on Share -> Publish.
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## Can I connect a custom domain to my Lovable project?
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Yes, you can!
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To connect a domain, navigate to Project > Settings > Domains and click Connect Domain.
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Read more here: [Setting up a custom domain](https://docs.lovable.dev/features/custom-domain#custom-domain)
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## Deployment (Hugging Face Spaces)
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This project is set up as a **Docker Space**:
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- Frontend: Vite/React (built to `/app/static`)
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- Backend: FastAPI (`/api/analyze`) that runs your Keras model on video frames and returns EF + heart function.
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### Required files
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Place your trained Keras model in the repo root as:
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- `model.keras` (or set `MODEL_PATH` env var in the Space)
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The scaler is already referenced as:
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- `scaler.save` (loaded from `/app/scaler.save`)
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### Run locally
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```bash
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npm install
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npm run build
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python -m venv .venv && source .venv/bin/activate
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pip install -r requirements.txt
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MODEL_PATH=./model.keras uvicorn backend.main:app --reload --port 7860
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```
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Then open http://localhost:7860
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---
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title: Cardexa
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emoji: ❤️
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colorFrom: indigo
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colorTo: pink
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sdk: docker
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pinned: false
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---
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# Cardexa
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AI-powered cardiac analysis from echocardiography videos.
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Upload a heart scan video to estimate EDV, ESV, and Ejection Fraction.
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