Streamlit Tensorflow ML App

Abstract

1. Introduction

An application that facilitates farmers, scientists and botanists to detect the type of plant or crops, detect any kind of diseases in them. The app sends the image of the plant to the server where it is analysed using CNN classifier model. Once detected, the disease and its solutions are displayed to the user.

Trained to identify 5 classes for Disease Detection and 24 classes for Disease Classification

2. Objective

Web App for Plant Disease Detection using Tensorflow and streamlit

This project demonstrates how Python, Machine Learning, Streamlit can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/AmeyaUpalanchi/streamlit-tensorflow-ml-app.git
cd streamlit-tensorflow-ml-app

1. Install the required dependencies
2. Command for running app

pip install -r requirements.txt

streamlit run app.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by AmeyaUpalanchi and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.