replit.md

Lung Detector

A Gradio web app that detects lung diseases from chest X-ray images using a CNN model.

Run & Operate

• PORT=8000 python3 app.py — start the Gradio app (workflow: "Lung Detector")
• pip install -r requirements.txt — install Python dependencies

Stack

• Python 3.11
• Gradio (UI)
• TensorFlow / Keras (CNN model inference)
• Pillow, NumPy (image preprocessing)
• model_setup.py — file path resolver (local + HuggingFace Hub)

Where things live

• app.py — main Gradio application
• model_setup.py — maps filenames to local paths (or HF Hub downloads)
• model_config.json — HuggingFace Hub config (hub_repo_id, hub_files)
• cnn_model_lung_detection.keras — the trained CNN model
• requirements.txt — Python dependencies

Architecture decisions

• Always load model files via model_setup.paths["filename"] — never hard-coded paths
• Model input: 128×128 RGB, normalized to [0, 1]
• Model output: 4-class softmax (COVID-19, Normal, Viral Pneumonia, Lung Opacity)
• CSS + theme passed to demo.launch() (Gradio 6.0 API)
• Port read from PORT env var, defaulting to 7860

Product

Users upload a chest X-ray image. The app runs CNN inference and displays:

• Primary finding label (COVID-19 / Normal / Viral Pneumonia / Lung Opacity)
• Confidence percentage
• Clinical description of the finding
• Probability bar chart for all 4 classes

User preferences

• Pure Python project — no TypeScript, React, or database scaffolding
• Flat file tree (no lib/, no artifacts/ for the Python app)
• Medical tool visual style: dark background, thin letters, professional
• Always use model_setup.paths["<filename>"] for file loading

Gotchas

• Port 8080 is used by the API server artifact; use 8000 for the Python app
• Gradio 6.0: pass css and theme to launch(), not Blocks()
• CUDA warnings at startup are expected (CPU inference only)
• cnn_model_lung_detection.keras — 4 output classes with softmax activation