A comprehensive web-based platform for solving complex engineering problems, built entirely in Python with a Streamlit frontend and a FastAPI backend for heavy computation and video rendering.
Engineering Solver Suite is an all-in-one educational platform designed to help students and engineers tackle complex problems. Each solver provides not just answers but visual explanations and step-by-step animated breakdowns of the solution process, powered by a robust Python backend.
This project uses a modern decoupled architecture to handle long-running tasks like video rendering without timeouts, using a pure Python stack.
- Frontend (Streamlit): The user interacts with the Streamlit app, enters their data, and clicks "Render Video."
- API Request: The frontend sends a request to a
/api/start-...endpoint on the FastAPI backend. - Backend (FastAPI): The backend immediately accepts the request, creates a unique Job ID, and starts the Manim rendering process as a background task. It instantly returns the Job ID to the frontend.
- Polling: The Streamlit app then repeatedly calls a
/api/status/{job_id}endpoint every few seconds to check the job's status. - Result: Once the backend finishes rendering, it updates the job's status. The frontend sees the "completed" status, gets the final video URL, and displays it to the user.
- Problem: Design the hoisting mechanism for an Electric Overhead Traveling (EOT) crane.
- Features: Wire rope selection, pulley optimization, and animated design process using Manim.
- Input: Load (tonnes/kN), Speed (m/min or m/s), Lift height.
- Output: Complete design specifications or an animated explanation.
- Problem: Find the optimal minimum-cost assignment using the Hungarian Algorithm.
- Features: Step-by-step matrix reduction and a visual representation of the final assignments.
- Input: N×N cost matrix.
- Output: Optimal assignment pairs with minimum total cost.
- Problem: Find the optimal distribution plan from sources to destinations.
- Features: Vogel's Approximation Method, supply-demand balancing, and route visualization.
- Input: Supply array, Demand array, Cost matrix.
- Output: Optimal transportation plan with minimum cost.
- 🎬 Asynchronous Video Rendering - No more timeouts! Jobs run in the background.
- 📊 Interactive Inputs - User-friendly Streamlit widgets with real-time updates.
- 📱 Mobile Friendly - Responsive design that works on all devices.
- 📈 Real Engineering Data - Uses standard reference tables from the PSG Design Data Book.
- 🧮 Accurate Calculations - Implements industry-standard algorithms and methods.
- Python 3.10+ and pip
- Docker and Docker Compose
- Git
git clone https://github.com/ahmed241/Project-py cd engineering-solver-suite
cd backend docker-compose up --build
The backend will now be running on http://localhost:7000
cd .. # Go back to the root directory pip install -r requirements.txt streamlit run Home.py
The frontend will now be running on http://localhost:8501
🏗️ Project Structure engineering-solver-suite/ ├── Home.py # Main Streamlit homepage ├── requirements.txt # Frontend (Streamlit) Python dependencies ├── pages/ │ ├── 1_Transportation.py # Transportation solver page │ ├── 2_Assignment.py # Assignment solver page │ └── 3_EOT_Crane.py # EOT Crane solver page └── backend/ ├── main.py # FastAPI app with all API endpoints ├── requirements.txt # Backend Python dependencies ├── Dockerfile # Docker instructions for backend server ├── docker-compose.yml # Easy local startup for the backend ├── public/ │ └── videos/ # Stores generated videos └── backend/ ├── Transportation/ │ ├── animation.py │ └── VAM_solver.py ├── Assignment/ │ └── ... └── EOT_Crane/ ├── animation.py └── EOT_solver.py 🎯 Technology Stack Frontend Framework: Streamlit
Language: Python
Backend Framework: FastAPI
Language: Python
Animation: Manim
Computation: NumPy, SciPy
Deployment: Docker
🐛 Troubleshooting Connection Refused Error in Streamlit:
Make sure your backend Docker container is running. Use docker ps to check.
Verify the BACKEND_URL variable in your Streamlit .py files is correct (e.g., http://localhost:7000 for local development).
403 Forbidden Error on Deployed App:
This is a CORS error. Go to backend/main.py, find the origins list, and add the exact URL of your deployed Streamlit app (e.g., https://your-app-name.streamlit.app).
Videos Not Generating:
Check the logs from your Docker container for any Manim errors.
Ensure the public/videos directory exists within your backend structure.
🚀 Deployment This project requires deploying the frontend and backend separately.
- Backend (Python/FastAPI) Recommended Service: Render.
Method: Connect your GitHub repository. Render will automatically detect the Dockerfile and build your containerized backend.
Result: You will get a public URL like https://your-backend.onrender.com.
- Frontend (Python/Streamlit) Recommended Service: Streamlit Community Cloud.
Method: Connect your GitHub repository and point it to your app's main file (Home.py).
Crucial Step: You must set a Secret in your Streamlit app's settings. The secret key should be BACKEND_URL and its value should be the public URL of your deployed backend (e.g., https://your-backend.onrender.com). Your Streamlit code should read this secret to communicate with the backend.
🤝 Contributing We welcome contributions! Please fork the repository, create a feature branch, and open a pull request.
🌟 If this project helped you, please give it a star! ⭐ Made with ❤️ for the engineering community