Science Lab Assistant

• 

  AI-Powered Experiment science Lab & Report Analysis

Inspiration

As former science students, we experienced firsthand the struggles of designing experiments and writing lab reports. The frustration of unclear hypotheses, missing sections in reports, and lack of actionable feedback inspired us to create an AI-powered solution. We wanted to build a tool that would make science accessible, engaging, and effective for every student - transforming anxiety into excitement about discovery.

What it does

Science Lab Assistant is an all-in-one platform that: 🔬 Designs Experiments: Generates step-by-step guides with safety protocols and scientific explanations 📝 Analyzes Lab Reports: Provides AI-powered feedback on completeness, structure, and content 💡 Explains Concepts: Simplifies complex science terms through interactive Q&A 📊 Scores Reports: Gives visual completeness scores with actionable improvement tips 📥 Creates Resources: Generates downloadable PDF experiment guides

How we built it

AI Brain: Groq API with Llama 3 70B model for intelligent science reasoning Document Processing: PyMuPDF for PDFs and Tesseract OCR for image text extraction **Interface: **Streamlit for responsive web app with custom science-themed UI **Report Generation: **FPDF for creating professional experiment guides **Deployment: **Hugging Face Spaces with secure API key management

Challenges we ran into

API Limitations: OpenRouter authentication issues requiring switch to Groq Model Selection: Testing multiple AI models to find optimal educational responses PDF Extraction: Handling complex document layouts and formatting Error Handling: Creating user-friendly messages for technical failures

Accomplishments that we're proud of

✅ Creating a complete science workflow from experiment design to report analysis ✅ Achieving <2s response times with Groq's lightning-fast inference ✅ Positive feedback from student testers ✅ Solving real-world educational pain points with AI innovation ✅ Implementing robust error handling for diverse failure scenarios

What we learned

Prompt Engineering: Crafting effective queries for educational content PDF Processing: Advanced techniques for text extraction from scientific documents AI Limitations: Understanding when models struggle with complex reasoning Deployment Challenges: Managing secrets across different hosting platforms User-Centric Design: Balancing features with simplicity for student users

What's next for Science Lab Assistant

Mobile App: iOS/Android version with experiment recording features Multilingual Support: Breaking language barriers in science education Lab Equipment Integration: Connecting with Arduino/Raspberry Pi sensors 3D Visualizations: Interactive molecular models and experiment simulations Teacher Dashboard: Classroom analytics and assignment tracking Collaborative Features: Real-time group project workspace

Built With

• cloud
• github
• groq
• huggingface
• numpy
• openrouterapi
• pillow
• pymupdf
• python
• request
• streamlit
• vscode