🏥 DocPal – Your Smart Patient Record & Symptom Checker

Welcome to DocPal, a simple and intelligent platform designed to help doctors and researchers manage patient records and assist in disease diagnosis using AI.

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✨ Features

👤 Patient Management

Easily add and track patient details like:

• Patient ID
• Name
• Age
• Gender
• Height & Weight
• Lifestyle Level (LL)

🩺 Symptom Checker

• Select symptoms the patient is experiencing.
• Get AI-powered diagnosis suggestions powered by a neural network model.

📂 Organized Records

• Keep patient data structured and easy to access.

⚡ Quick & Reliable

• Simple web interface built with Flask, HTML, and CSS.
• Backed by MySQL for database management.

🧠 Machine Learning Model

• Model Type: Neural Network (TensorFlow / Keras)
• Training Accuracy: ~83%
• Validation Accuracy: ~85%
• Task: Predict disease based on patient symptoms.

The model was trained on a dataset of symptoms → disease mappings and is integrated into the app for quick predictions.

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⚙️ Tech Stack

• Backend: Flask (Python)
• Frontend: HTML, CSS, Bootstrap
• Database: MySQL
• ML / AI: TensorFlow, scikit-learn, Keras
• Version Control: Git + GitHub

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📁 Project Structure

DocPal/ │── templates/ # HTML files (frontend UI) │── static/ # CSS, JS, assets │── app.py # Flask application │── model/ # Trained neural network model │── requirements.txt # Python dependencies │── README.md

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🚀 How to Run the App Locally

1. Clone the Repository

git clone https://github.com/surabhipandey18/DocPal.git
cd DocPal
2. Set Up the Environment
Create a virtual environment and activate it:

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python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate
Install the required Python packages:

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pip install -r requirements.txt
3. Configure the Database
Ensure you have a MySQL server running locally.

Create a new database and a user with full permissions for the app.

Create a .env file in the project's root directory with your database credentials:

ini
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MYSQL_HOST=localhost
MYSQL_USER=your_username
MYSQL_PASSWORD=your_password
MYSQL_DATABASE=your_database_name
4. Run the App
Start the Flask application:

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python app.py
Your application will now be running on http://localhost:5000.

💡 About ML_API_URL
The ML_API_URL environment variable is a crucial part of this project's decoupled architecture.
It tells the main Flask app where to find the machine learning API.

In a local environment, set this to:

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http://localhost:5001
because the ML API is running on the same computer on port 5001.