🎓 AI-Powered Student Performance Feedback System

A Streamlit-based application that analyzes student test performance, visualizes learning trends, and uses the Google Gemini API to generate personalized academic feedback. It outputs professional, downloadable reports to help students and educators track and improve learning outcomes.

---

✨ Key Features

• 🤖 AI-Generated Feedback
  Leverages the Google Gemini API to provide detailed, constructive, and motivational feedback—like having a virtual academic coach.

• 📊 In-Depth Data Analysis
  Processes JSON test data to compute:

  • Overall performance (score, accuracy, time utilization)
  • Subject-wise analysis
  • Chapter-level strengths and weaknesses
  • Concept-level mastery insights

• 📈 Rich Visualizations

  • Subject Performance Radar Chart
  • Time vs. Accuracy Scatter Plot
  • Chapter vs. Difficulty Heatmap

• 📄 Professional Report Generation

  • Primary: Styled PDF via ReportLab
  • Fallback: Matplotlib-generated PDF
  • Final fallback: Simple .txt report if PDF libraries are unavailable

• 🖥️ Interactive UI
  Built with Streamlit: clean layout, metric cards, visual explorer, and AI feedback section.

• 📂 Flexible Data Input

  • Upload custom JSON test data
  • Or use the built-in data.json for demo purposes

---

📹 Video Demo

Watch a short demo of the app in action:

🎥 Click here to watch the demo

---

🖼️ Screenshots

Here are some previews of the application:

📊 Dashboard Overview

📈 Visualization

📄 Performance Table

---

⚙️ Installation & Setup

1. Clone the Repository

git clone <your-repository-url>
cd <your-repository-directory>

2. Create a Virtual Environment

# Windows
python -m venv venv
.\venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure API Key

Create a .env file in the root directory:

GEMINI_API_KEY="YOUR_API_KEY_HERE"

5. Prepare Sample Data

Ensure a data.json file is present in the root directory to demo the system.

---

🚀 Running the Application

streamlit run your_script_name.py

---

📁 File Structure

.
├── your_script_name.py     # Main Streamlit application
├── data.json               # Sample student test data
├── .env                    # API key (not included in git)
├── requirements.txt        # Project dependencies
├── Images/                 # Folder for demo images
│   ├── Performance_data.png
│   ├── Visualization.png
└── README.md               # Project documentation

---

📜 License

MIT License

---

🌟 Credits & Acknowledgements

• Developed using Streamlit
• Feedback generation powered by Google Gemini API
• Visualizations via Matplotlib and Seaborn