README.md

---
title: EEE_AI
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
- inference-api
---

# 🎓 EEE_AI: Specialized Pedagogical Agent
### Powered by Llama-3.1-8B-Instant & Groq LPU™

**EEE_AI** is an AI-driven tutoring system designed to assist students with the fundamentals of **Electrical and Electronics Engineering (EEE)**. This project serves as a bridge between high-performance LLM engineering and domain-specific educational technology.

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## 🚀 The Technical "Core"
As an AIML student, I built this space to explore the limits of **low-latency inference** and **specialized guardrails**.

* **Inference Engine:** Optimized via **Groq LPU™**, achieving speeds of **500+ tokens per second**, making the tutoring experience feel instantaneous.
* **Model:** `llama-3.1-8b-instant` — chosen for its high-reasoning capabilities within a compact parameter count.
* **System Architecture:** A hybrid deployment using **Hugging Face Spaces** for the Gradio frontend and **Groq Cloud** for backend compute.

## 🎯 Key Functionalities
* **Domain Focus:** Provides structured explanations on Circuit Theory, Semiconductor Devices, and Power Systems.
* **Interactive Tutoring:** The model is prompted to act as a Socratic tutor—asking follow-up questions to test user understanding rather than just giving answers.
* **Safety Guardrails:** Includes a custom instruction layer that prevents the model from deviating into non-engineering topics, ensuring it remains a dedicated study tool.

## 🛠️ Tech Stack
* **Language:** Python
* **Interface:** Gradio
* **LLM Framework:** Groq API / Meta Llama 3.1
* **Deployment:** Hugging Face (Syncing with GitHub)

## 📖 Sample Interactions
> **User:** "Explain KVL in simple terms."
> **EEE_AI:** [Provides explanation] + "Would you like a practice circuit problem to test this law?"

> **User:** "What's the best movie to watch tonight?"
> **EEE_AI:** "I am focused on your engineering success! Let's get back to EEE—perhaps we can discuss how Signal Processing is used in movie audio instead?"

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## 👷 About the Developer
**Kushagra Gaur** | *Curious from Core*
This project was developed to master the integration of high-speed inference APIs and the implementation of domain-specific constraints in LLMs.