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| title: Astral.AI | |
| emoji: 🏃 | |
| colorFrom: pink | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 5.37.0 | |
| app_file: app.py | |
| pinned: false | |
| license: afl-3.0 | |
| short_description: An AI Powered Tracker | |
| # 🩺 Pediatric Respiratory Triage Assistant (NLP-Powered) | |
| An intelligent, interactive assistant that helps parents and caregivers **triage common pediatric respiratory symptoms**. | |
| Built using interpretable machine learning, custom NLP pipelines, and deployed with a friendly **Gradio chatbot interface**. | |
| > ⚠️ This tool provides **non-diagnostic guidance** only. It is not a substitute for medical advice. | |
| --- | |
| ## 🚀 Live Demo | |
| Try the app on [Hugging Face Spaces](https://huggingface.co/spaces/your-username/pediatric-triage) | |
| *(replace with actual URL when deployed)* | |
| --- | |
| ## 🎯 Project Objective | |
| - Enable users to describe symptoms in **natural language** | |
| - Use an ML model to classify input into **one of four triage levels** | |
| - Return a **safe, easy-to-understand recommendation** | |
| - Support early triage decisions, especially in low-resource or high-volume contexts | |
| --- | |
| ## 🧠 How It Works | |
| 1. User enters a free-text symptom description | |
| 2. The text is processed via `TF-IDF` vectorization | |
| 3. A **Decision Tree classifier** predicts the triage category | |
| 4. A friendly chatbot message is displayed based on the prediction | |
| ### Triage Labels: | |
| | Label | Guidance | | |
| |--------------------|-------------------------------------------------------------------------| | |
| | 🟢 Monitor at Home | Mild symptoms, low risk. Watch and observe. | | |
| | 🟡 Consult GP | Suggests seeing a doctor for further evaluation. | | |
| | 🫁 Use Inhaler | Asthma-like symptoms. Use prescribed inhaler and monitor closely. | | |
| | 🔴 Visit Emergency | Serious symptoms. Seek urgent medical attention. | | |
| --- | |
| ## 🧪 Model Summary | |
| - Vectorizer: `TfidfVectorizer` with bigrams | |
| - Best model: `DecisionTreeClassifier` (Accuracy: 96%) | |
| - Other tested models: Linear SVM, XGBoost, LightGBM, Random Forest | |
| --- | |
| ## 🗃️ Files in This Repo | |
| ```mathematica | |
| ├── app.py # Gradio app entry point | |
| ├── best_model_decision_tree.joblib # Trained ML model | |
| ├── requirements.txt # All required libraries | |
| ├── README.md # You're reading it | |
| ``` | |
| --- | |
| ## 🧰 Tech Stack | |
| - `Gradio` – chatbot interface | |
| - `Scikit-learn` – model training + TF-IDF pipeline | |
| - `XGBoost`, `LightGBM`, `Random Forest` – tested alternates | |
| - `Joblib` – model persistence | |
| - `Matplotlib`, `Seaborn` – EDA & diagnostics | |
| --- | |
| ## 🧠 Limitations | |
| - Not a diagnostic system — designed only for **low-risk triage advice** | |
| - Based on synthetic and heuristic-labeled data from medical education text | |
| - Requires expansion for multilingual or multi-condition support | |
| --- | |
| ## 🧭 Future Plans | |
| - Integrate BERT for deeper semantic understanding | |
| - Add class weighting or active learning | |
| - Train on verified clinical text or real-world triage logs | |
| - Publish as an embeddable widget or API | |
| --- | |
| ## ⚠️ Disclaimer | |
| This project is for educational and research purposes only. It is not a certified medical device or diagnostic tool. | |
| Always consult a licensed healthcare provider for professional medical advice. | |
| --- | |
| ## 📄 License | |
| MIT License – feel free to modify, fork, and improve responsibly. | |
| --- | |
| ## 🙌 Credits | |
| Built with ❤️ by [SilverDragon9](https://huggingface.co/SilverDragon9) | |
| Inspired by pediatricians, caregivers, and the need for accessible, responsible healthcare AI. | |