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returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","docker","fastapi","healthcare","machine-learning","ml-model","multilingual","nlp","python","symptom-checker","telemedicine"],"created_at":"2025-12-24T01:45:54.807Z","updated_at":"2026-04-13T04:02:13.323Z","avatar_url":"https://github.com/radha1805.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"left\"\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/Python-3.10-blue.svg\" alt=\"Python Version\"/\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/FastAPI-API%20Backend-brightgreen.svg\" alt=\"FastAPI\"/\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/ML-Model-orange.svg\" alt=\"Machine Learning\"/\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" alt=\"License\"/\u003e\r\n  \u003cimg src=\"https://img.shields.io/github/last-commit/radha1805/symptom-checker-ml\" alt=\"Last Commit\"/\u003e\r\n  \u003cimg src=\"https://img.shields.io/github/repo-size/radha1805/symptom-checker-ml\" alt=\"Repo Size\"/\u003e\r\n\u003c/p\u003e\r\n\r\n\r\n# Symptom Checker ML Model\r\n\r\nAn AI-based symptom checker designed with a complete ML training pipeline, multilingual support, and a deployment-ready FastAPI backend.\r\n\r\n## 📌 Overview\r\n\r\nThis project implements a machine learning model that predicts possible diseases based on user-provided symptoms.  \r\nIt includes preprocessing, feature engineering, model training, inferencing APIs, multilingual handling, and Docker-based deployment.  \r\nThe architecture is clean, modular, and suitable for real-world telemedicine workflows.\r\n\r\n---\r\n\r\n## 🚀 Features\r\n\r\n- **Machine Learning Model** for symptom-based disease prediction  \r\n- **Training Pipeline** with preprocessing, feature engineering, and evaluation  \r\n- **Prediction API (FastAPI)** exposing inference endpoints  \r\n- **Multilingual Support** (English, Hindi, Punjabi)  \r\n- **Speech-to-Text \u0026 Text-to-Speech Support** (local + cloud options)  \r\n- **Docker Deployment** for easy containerization  \r\n- **Clean Modular Architecture** for scalability  \r\n\r\n---\r\n\r\n## 📁 Project Structure\r\n\r\n```\r\nsymptoms_checker/\r\n│\r\n├── api.py                     # FastAPI prediction server\r\n├── train_model.py             # Model training script\r\n├── feature_engineer.py        # Feature extraction \u0026 preprocessing\r\n├── symptom_extractor.py       # Extracts symptom keywords\r\n├── translator.py              # Multilingual translation logic\r\n├── stt_tts.py                 # Speech-to-text and text-to-speech\r\n│\r\n├── artifacts/                 # Saved ML models and encoders\r\n├── tests/                     # Test suite\r\n│\r\n├── requirements.txt           # Dependencies\r\n├── Dockerfile                 # Deployment configuration\r\n└── README.md                  # Documentation\r\n```\r\n\r\n---\r\n\r\n## 🔧 Installation\r\n\r\n### 1. Clone the repository\r\n```\r\ngit clone https://github.com/yourusername/symptom-checker-ml.git\r\ncd symptom-checker-ml\r\n```\r\n\r\n### 2. Install dependencies\r\n```\r\npip install -r requirements.txt\r\n```\r\n\r\n---\r\n\r\n## ▶️ Running the Model API\r\n\r\nStart the FastAPI inference server:\r\n```\r\nuvicorn api:app --host 0.0.0.0 --port 8000\r\n```\r\n\r\nOpen in browser:  \r\n**http://localhost:8000/docs**\r\n\r\n---\r\n\r\n## 📡 Example API Usage\r\n\r\n### POST `/predict`\r\n\r\n**Input**\r\n```json\r\n{\r\n  \"text\": \"I have fever and body pain\"\r\n}\r\n```\r\n\r\n**Output**\r\n```json\r\n{\r\n  \"predicted_disease\": \"Influenza\"\r\n}\r\n```\r\n\r\n---\r\n\r\n## 🧠 Model\r\n\r\nThe model uses classical ML techniques trained on symptom–disease mappings.  \r\nPipeline includes:\r\n\r\n- Text preprocessing  \r\n- Symptom feature extraction  \r\n- One-hot encodings  \r\n- Model training  \r\n- Artifact saving  \r\n\r\nArtifacts are stored in the `/artifacts` directory.\r\n\r\n---\r\n\r\n## 🌍 Multilingual Support\r\n\r\nSupported languages:\r\n- English  \r\n- Hindi  \r\n- Punjabi  \r\n\r\nInputs are normalized internally before prediction.\r\n\r\n---\r\n\r\n## 🐳 Docker Deployment\r\n\r\nBuild image:\r\n```\r\ndocker build -t symptom-checker .\r\n```\r\n\r\nRun container:\r\n```\r\ndocker run -p 8000:8000 symptom-checker\r\n```\r\n\r\n---\r\n\r\n## 🧪 Testing\r\n\r\nRun the test suite:\r\n```\r\npytest\r\n```\r\n\r\n---\r\n\r\n## 👩‍💻 Author\r\n\r\n**Radha Sarda**  \r\nMachine Learning | AI | Deployment  \r\nOpen to collaborations and improvements.\r\n\r\n---\r\n\r\n## 📜 License\r\n\r\nReleased under the MIT License.\r\n\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fradha1805%2Fsymptom-checker-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fradha1805%2Fsymptom-checker-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fradha1805%2Fsymptom-checker-ml/lists"}