Sentiment Dashboard Pro

Sentiment Dashboard Pro is a full-stack web application designed to analyze text sentiment in real-time. It features a responsive, dark-themed frontend dashboard integrated with a Python-based Flask backend hosting a machine learning pipeline.

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🚀 Features

• Real-Time Sentiment Analysis: Instantly predicts sentiment from live text inputs using an optimized Machine Learning pipeline.
• Interactive Workspace: A dynamic, modern dark-themed user interface equipped with graph monitors to track sentiment distributions.
• Secure Authentication: Built-in portal for secure user onboarding, login, and registration workflows.
• Robust Backend API: Lightweight and scalable Flask server architecture managing API routes and incoming predictions.

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📁 Project Structure

The project repository is systematically organized into two primary architectural layers:

📦 Backend (Flask & Machine Learning)

• app.py: The main backend Flask server script hosting API routing configurations and receiving live inputs.
• model_helper.py: A dedicated helper utility containing regex-based text preprocessing and prediction wrapper functions.
• train.py: A standalone utility script executed once to build, train, and export the ML pipeline.
• model.pkl: Serialized binary file of the pre-trained Logistic Regression algorithm.
• vectorizer.pkl: Pre-trained TF-IDF vectorizer configuration that converts text strings into mathematical arrays.
• requirements.txt: Lists the exact configuration package versions required to reproduce and execute the backend framework.
• datasetFolder/: Contains the structured raw text rows used to clean, train, and evaluate the sentiment analysis model.

🎨 Frontend (User Interface)

• index.html: The primary marketing gateway landing page featuring product overviews and mock UI cards.
• dashboard.html: The core analytics workspace layout where live analysis is executed and tracked via graph monitors.
• auth.html: The onboarding portal handling secure user authentication routines like log-in and registration flows.
• css/: Directory containing custom stylesheets that control the responsive, dark-themed UI layouts.
• js/: Directory housing the asynchronous, event-driven JavaScript that orchestrates dynamic DOM updates and API calls.

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🛠️ Local Setup Instructions

Prerequisites

Make sure you have Python 3.x installed on your system.

1. Backend Setup

Run the following commands in your terminal to set up the backend:

# Navigate to the backend directory
cd backend

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows (PowerShell):
.\venv\Scripts\Activate.ps1
# On Mac/Linux:
source venv/bin/activate

# Install required dependencies
pip install -r requirements.txt

# Run the Flask server
python app.py
2. Frontend Setup
Simply open frontend/index.html directly in any modern web browser, or serve it using a local server extension (like Live Server in VS Code) to interact with the dashboard.

🤖 Tech Stack Used
Frontend: HTML5, CSS3 (Flexbox/Grid), JavaScript (ES6, Fetch API)

Backend: Python, Flask

Machine Learning: Scikit-Learn (Logistic Regression, TF-IDF Vectorization), Pandas, Regex