🏆 PODIUM_AI

Podium AI is a Flask-based web application that leverages Computer Vision and multiple Generative AI models (Google Gemini, OpenAI, and Groq) to analyze and classify uploaded media.

📄 Project Overview

This application serves as an AI-powered engine that processes user-uploaded images/videos using a custom Computer Vision engine (cv_engine.py) and passes the data through an AI classifier (ai_classifier.py).

Key Capabilities:

• Multi-Model Support: Integrates with Google Generative AI, OpenAI, and Groq for flexible LLM processing.
• Computer Vision Pipeline: Uses opencv-python and scikit-image for pre-processing and feature extraction.
• Web Interface: A lightweight Flask frontend allowing users to upload files for analysis.

🛠️ Tech Stack

• Backend: Python, Flask
• Computer Vision: OpenCV (opencv-python-headless), Scikit-Image, Imutils, Numpy
• GenAI Providers: * google-generativeai (Gemini)
  • openai (GPT models)
  • groq (High-speed inference)
• Utilities: Python-Dotenv (Environment management)

📂 Project Structure

PODIUM_AI/
├── static/          # CSS/JS files
├── templates/       # HTML Templates (Jinja2)
├── uploads/         # Directory for user-uploaded media
├── ai_classifier.py # Logic for LLM interaction/classification
├── app.py           # Main Flask application entry point
├── cv_engine.py     # Core Computer Vision processing logic
├── check_models.py  # Utility script to verify model availability
├── requirements.txt # Project dependencies
└── .env             # Environment variables (API Keys)