I’m putting together a small college assignment that demonstrates how a Django web app can integrate a simple machine-learning model for product detection. The goal is strictly educational, so the ML portion only needs to prove the concept—high accuracy is not required. Core workflow Users will sign up or log in, upload an image of a product, and immediately see the detected product name. Behind the scenes a lightweight, preferably pre-trained, TensorFlow or OpenCV model can handle the detection. In the admin panel, product detection should sit front-and-center so I can verify images, correct labels if needed, and keep a record of the results. Inventory management Once a product is detected, I should be able to add or edit its description, adjust stock counts, and toggle status between In Stock, Out of Stock, or Pending. A straightforward Bootstrap interface is all that’s necessary; polish is less important than clarity and ease of demonstration. Documentation expectations Because this is for a viva, I’ll need a clear write-up that explains the code at a moderate depth, showing key snippets without drowning in theory. A concise slide deck (around eight slides) will reinforce the main points. Please deliver: • Clean, well-commented Django project with the ML model integrated • PDF report containing architecture, key code snippets, and setup steps • PowerPoint (6–8 slides) for presentation • Short, plain-language walkthrough I can use to prepare for the viva Everything has to be ready within five days so I have a buffer to review before submission. Budget: ₹2000–₹3000 Looking for a low-cost freelancer. ML model can be basic/pre-trained but must be working (not dummy). Also need explanation for viva.