Added Disease predictor

Domains/AI-ML/MiniProjects/disease-predictor/README.md

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+**Contributor:** shravanidorle
+
+# Disease Predictor Web Service
+
+## 🩺 Project Overview
+
+This is a simple Flask-based web service that utilizes a pre-trained machine learning model to predict a disease based on a list of symptoms provided by the user. It provides both a simple web interface (assuming the necessary templates exist) and a dedicated API endpoint for programmatic access.
+
+---
+
+## ✨ Features
+
+- **RESTful API Endpoint:** A dedicated `/predict` endpoint for JSON-based predictions.
+- **Machine Learning Integration:** Uses `joblib` to load and utilize a pre-trained model.
+- **Web Interface:** Serves simple front-end pages (`/`, `/about`, `/services`) using Flask templating (requires `index.html`, `about.html`, and `services.html`).
+
+---
+
+## 🛠️ Prerequisites
+
+Before running the application, ensure you have the following installed:
+
+- **Python 3.x**
+- **Flask**
+- **joblib**
+
+---
+
+## 🚀 Setup and Installation
+
+### 1. File Structure
+
+Your project directory should be structured as follows:
+
+disease-predictor/ ├── app.py ├── disease_predictor_model_5000.pkl <-- Your trained model file (REQUIRED) ├── templates/ │ ├── index.html <-- Home page template (REQUIRED) │ ├── about.html <-- About page template (REQUIRED) │ └── services.html <-- Services page template (REQUIRED) └── README.md
+
+### 2. Install Dependencies
+
+You can install the required Python packages using `pip`:
+
+```bash
+pip install Flask joblib
+
+```

Domains/AI-ML/MiniProjects/disease-predictor/app_5000.py

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+from flask import Flask, request, jsonify, render_template
+import joblib
+
+app = Flask(__name__)
+model = joblib.load('disease_predictor_model_5000.pkl')
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/about')
+def about():
+    return render_template('about.html')
+
+@app.route('/services')
+def services():
+    return render_template('services.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    symptoms = data.get('symptoms', '')
+    prediction = model.predict([symptoms])
+    return jsonify({'disease': prediction[0]})
+
+if __name__ == '__main__':
+    app.run(debug=True)