Project: Artificial Intelligence - PresentAI Pro

[Ameena2244]

Track

Creative Apps (GitHub Copilot)

Project Name

PresentAI Pro — AI Presentation Analyzer

GitHub Username

Ameena2244

Repository URL

https://github.com/Ameena2244/PresentAI-Pro-AI-Presentation-Analyzer

Project Description

PresentAI Pro – AI-Powered Presentation Analyzer

PresentAI Pro is an intelligent web application that helps students and professionals improve their project presentations using Artificial Intelligence. Users can upload presentation documents in PDF, DOCX, or TXT format, and the system automatically analyzes the content to provide meaningful insights.

The application evaluates presentation quality, identifies missing sections, highlights weaknesses, and generates improvement suggestions. It also provides a Project Readiness Score, predicts the likelihood of success in competitions or hackathons, and generates potential judge questions to help users prepare for presentations and viva sessions.

Built using Flask, JavaScript, Chart.js, and AI-based content analysis, PresentAI Pro transforms traditional presentation review into an interactive and intelligent experience. The platform helps users enhance content quality, improve confidence, and increase their chances of delivering successful project presentations.

Demo Video or Screenshots

https://github.com/Ameena2244/PresentAI-Pro-AI-Presentation-Analyzer/blob/main/README.md

Primary Programming Language

Python

Key Technologies Used

• Python
• Flask
• HTML5
• CSS3
• JavaScript
• Bootstrap 5
• Chart.js
• Google Gemini AI
• PDF Processing
• DOCX Processing
• REST API Integration
• Git & GitHub

Submission Type

Individual

Team Members

No response

Submission Requirements

• My project meets the track-specific challenge requirements
• My repository includes a comprehensive README.md with setup instructions
• My code does not contain hardcoded API keys or secrets
• I have included demo materials (video or screenshots)
• My project is my own work with proper attribution for any third-party code
• I agree to the Code of Conduct
• I have read and agree to the Disclaimer
• My submission does NOT contain any confidential, proprietary, or sensitive information
• I confirm I have the rights to submit this content and grant the necessary licenses

Quick Setup Summary

1. Clone or download the project repository.

2. Open the project folder in VS Code or PowerShell.

3. Create a virtual environment:

python -m venv .venv

4. Activate the virtual environment:

.venv\Scripts\activate

5. Install required dependencies:

pip install -r requirements.txt

6. Run the Flask application:

python app.py

7. Open the application in your browser:

http://127.0.0.1:5000/

8. Upload a PDF, DOCX, or TXT presentation file and view the AI-powered analysis, project readiness score, judge questions, and success prediction dashboard.

Technical Highlights

• Developed an AI-powered presentation analysis system that automatically evaluates uploaded project documents.

• Implemented document processing for PDF, DOCX, and TXT files, enabling users to analyze different presentation formats.

• Designed an intelligent feedback engine that identifies missing sections, weaknesses, and improvement opportunities in presentation content.

• Created a Project Readiness Score that helps users understand how prepared their project is for reviews, presentations, and competitions.

• Built a Judge Question Generator that predicts potential questions evaluators may ask during project demonstrations and viva sessions.

• Integrated interactive data visualizations using Chart.js to present analysis results in an easy-to-understand dashboard.

• Developed a Success Prediction feature that estimates project performance based on presentation quality and completeness.

• Focused on a clean, responsive, and user-friendly interface using Flask, Bootstrap, HTML, CSS, and JavaScript for a smooth user experience.

Challenges & Learnings

• One of the main challenges was handling different file formats (PDF, DOCX, TXT) and extracting clean, structured text from them for analysis.

• Integrating AI-based analysis with a fallback system was important to ensure the application works even without external API access.

• Designing meaningful evaluation logic (readiness score, weakness detection, and judge question generation) required careful tuning to avoid overly generic outputs.

• Managing frontend-backend communication using Flask and JavaScript helped improve understanding of REST API workflows.

• A key learning was how to transform raw unstructured text into structured insights that are useful for real-world decision-making.

• Learned how to design a full-stack AI application combining document processing, AI analysis, and interactive visualization in a single workflow.

• Improved problem-solving skills in debugging, especially in JavaScript execution flow and Python-Flask integration issues.

Contact Information

ameenababu2244@gmail.com

Country/Region

India

[Ameena2244]

Learning