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Leaf Diseases Detect

Leaf Diseases Detect is a Python web application for analyzing leaf images and returning disease identification, severity assessments, symptom information and treatment recommendations. It combines a Streamlit user interface with a FastAPI backend and uses Llama Vision through Groq for image analysis.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Leaf Diseases Detect — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a leaf photo can help frame an initial plant-health question and you want disease, severity, symptom and treatment information in one result.

Who it is for

  • Agricultural and horticultural users seeking an initial assessment of a leaf-health concern.
  • Developers interested in a Python project that pairs an image-analysis interface with a FastAPI backend.

Documented uses

  • Submit a leaf photo for possible disease identification.
  • Review a severity category and confidence score with reported symptoms.
  • Get treatment recommendations associated with the analysis.
  • Use the documented FastAPI service to accept an image for analysis.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. Local setup requires a Groq API key.

  • The analysis depends on Llama Vision through Groq and therefore requires a Groq API key for local use.
  • The project separates the Streamlit interface from the FastAPI backend.
  • The README describes severity levels and confidence percentages as part of the analysis results.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the Streamlit interface locally

Set up the project and open its interactive leaf-image analysis interface.

Before you start

  • Python 3.8 or later
  • Git
  • A Groq API key

Steps

  1. Clone the repository and navigate to its Front directory.

  2. Create and activate a Python virtual environment.

  3. Install dependencies with pip install -r requirements.txt.

  4. Create a .env file in the project root and set GROQ_API_KEY to your key.

  5. Launch with streamlit run main.py --server.port 8501 --server.address 0.0.0.0.

Expected outcome

The README directs users to open the Streamlit interface at http://localhost:8501, upload a leaf image, request detection and view the formatted results.

Guide limitations

  • A Groq API key is required for the AI analysis.
  • The README's described upload flow specifically lists JPG and PNG support.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
8 October 2026
Original website title
shukur-alom/leaf-diseases-detect · Official homepage title · retained in its source language
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