wilame/patient_feedback_analysis

Patient Feedback Analysis

Overview

Patient Feedback Analysis is a Python-based project designed to provide a simple interface for users to analyse patient feedback data. The application allows users to upload a CSV file containing patient feedback data or input feedback manually. The feedback data is then analysed using a pre-trained model to determine the main topics, sentiments, and recommendations. The application is built using the Streamlit library and Hugging Face Inference API, together with a Llama-family model.

Contents

• —functions.py: Contains various functions used in the project.
• —config.py: Configuration settings for the project.
• —requirements.txt: Lists the dependencies required to run the project.
• —app.py: The main application file.
• —.gitignore: Specifies files and directories to be ignored by git.
• —.gitattributes: Configuration for git attributes.

Getting Started

Prerequisites

Ensure you have the following installed:

• —Python 3.x
• —pip (Python package installer)

Installation

1. 1.Clone the repository:
2. 2.Navigate to the project directory:

    cd patient_feedback_analysis

1. 1.Install the required dependencies:

    pip install -r requirements.txt

Running the Application

Run the main application file:

streamlit run app.py