Exam Analytics Pro: Agentic Difficulty Prediction System 🎓🤖

Exam Analytics Pro is a high-performance system designed to predict and analyze the difficulty of assessment items using a hybrid approach of Traditional Machine Learning and Agentic AI (LangGraph/LangChain).

🌟 Key Features

1. Hybrid Intelligence Engine

• Vectorized Inference: Uses TF-IDF and specialized scikit-learn models (Logistic Regression & Decision Trees) for fast, data-driven baseline predictions.
• Agentic Reasoning: Employs a LangGraph state machine to orchestrate multi-step analysis by LLMs (OpenAI, Gemini, Groq).
• Pedagogical Analysis: Provides deep insights into Bloom's Taxonomy levels, cognitive depth, and linguistic complexity.

2. Multi-Provider LLM Support

Switch between state-of-the-art LLM providers seamlessly:

• Google Gemini: Reliable and fast pedagogical analysis.
• Groq (Llama 3): Ultra-low latency inference for real-time reasoning.
• OpenAI (GPT-4o): High-fidelity conceptual complexity mapping.

3. Interactive Analytics Dashboard

• CSV Batch Processing: Upload hundreds of questions for instant difficulty distribution reports.
• Advanced Insights: Correlate question length, tag count, and engagement scores with predicted difficulty.
• Explainable AI: Click a button to see exactly why an AI agent classified a question a certain way.

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🏗️ System Architecture

graph TD
    A[Raw Exam Dataset] -->|CSV Upload| B[Dashboard]
    B -->|Preprocessing| C[Feature Matrix]
    
    subgraph Agentic_Workflow
        direction TB
        Start((START)) --> ML_Predictor[ML classification Node]
        ML_Predictor -->|Baseline: Hard/Med/Easy| LLM_Reasoner[LLM Reasoning Node]
        LLM_Reasoner -->|Contextual Analysis| Synthesizer[Synthesis Node]
        Synthesizer --> End((END))
    end
    
    C --> Start
    ML_Predictor -.-> Models[(scikit-learn Models)]
    LLM_Reasoner -.-> APIs{LLM APIs}
    
    End -->|Final Verdict| B
    B -->|UI Output| Visualization[Distribution Charts & Reports]

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🚀 Getting Started

Prerequisites

• Python 3.12+
• Virtual Environment (recommended)

Installation

1. Clone the repository:

   git clone https://github.com/praggCode/Exam-Analytics-System.git
   cd Exam-Analytics-System

2. Set up virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Running the Application

1. Start the Streamlit Server:

   streamlit run ui/app.py

2. Configure AI Settings:

   • Open the app at http://localhost:8501.
   • In the sidebar, select your preferred LLM Provider (Groq, Gemini, or OpenAI).
   • Enter your API Key.

3. Analyze Data:

   • Upload a CSV containing a question column.
   • Click "Analyze Difficulty" to see ML results.
   • Use the "Generate AI Reasoning" button for a deep-dive analysis of your top questions.

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📁 Project Structure

├── data/               # Processed and raw datasets
├── src/
│   ├── ai_engine.py    # LangGraph workflow definition
│   ├── model_train.py  # Model training pipeline
├── models/             # Saved joblib models (.joblib)
├── ui/
│   ├── app.py          # Streamlit UI implementation
├── requirements.txt    # Project dependencies
└── README.md           # Project documentation

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🛠️ Technology Stack

• Dashboard: Streamlit
• ML Framework: scikit-learn, joblib
• Orchestration: LangChain, LangGraph
• NLP: Beautiful Soup (BS4), re, TF-IDF
• Data Science: Pandas, NumPy, Seaborn, Matplotlib

📝 License

This project is for educational and professional assessment analytics. Built with ❤️ and AI.

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For a detailed report on the LangChain implementation, see langchain_report.md.