README.md

metadata

title: braingpt_implement
emoji: 🦀
colorFrom: gray
colorTo: blue
sdk: docker
pinned: false
short_description: Implementing braingpt using BrainGPT-7B-v0.1

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

This app currently uses BrainGPT as the model engine for generating and evaluating abstracts.

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🚀 Features

• 🧪 Presents users with neuroscience abstracts (either original or altered).
• ✅ Users decide if an abstract is AI-modified and rate their confidence.
• 🤖 BrainGPT model evaluates the same abstract.
• 📊 Results compare user guesses vs model output in a clear, styled interface.
• 📂 Backed by a curated dataset hosted on Hugging Face Datasets.
• 🛠️ Fully Dockerized FastAPI application deployed via Hugging Face Spaces.

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📦 Tech Stack

• Frontend: Jinja2 + Tailwind-style CSS
• Backend: FastAPI
• Model: BrainGPT-7B-v0.1 via Hugging Face Transformers
• Hosting: Hugging Face Spaces (Docker SDK)
• Dataset: Custom neuroscience benchmark (Parquet format)

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🔧 Endpoints

• /: Home page
• /start: Begin a new session (random abstract trials)
• /trial: Active abstract assessment
• /submit-trial: Submit a response, compare with model
• /results: View summary of performance
• /predict: POST endpoint used internally to get BrainGPT-7B-v0.1 output

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🧠 Example Use Case

1. A neuroscience researcher lands on the app.
2. They read an abstract and guess whether it’s been altered.
3. They rate their confidence.
4. BrainGPT-7B-v0.1 also analyzes the abstract.
5. After 3 rounds, the app displays a comparison: who got what right?

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📌 Current Model

• Model: BrainGPT-7B-v0.1
• Reason: Fast, lightweight, deployable in CPU-only Spaces

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🗺️ Roadmap

• Add leaderboard tracking
• Expand dataset with more domains

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🙌 Credits

Built by Dr. David Andai — a general practitioner & data scientist passionate about mental health and AI.