Readme file

README.md

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-title: DR Classification
-emoji: 🐨
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
-license: mit
+# 🐨 DR Classification
+
+This is a Streamlit-based web app for **Diabetic Retinopathy (DR) Classification** using fundus images. The model classifies retinal images into different DR severity levels to assist in early detection and monitoring.
+
+## 💡 Features
+- Upload a fundus image and get an instant DR classification.
+- Preprocessing pipeline (CLAHE, gamma correction, normalization, etc.) to enhance input quality.
+- Uses a fine-tuned DenseNet-121 model pretrained on ImageNet.
+- Supports visual output like prediction label and optionally Grad-CAM heatmaps for model explainability.
+
+## 🖼 Dataset
+The dataset used is uploaded on the Hugging Face Hub:  
+👉 [**your-username/your-dataset-name**](https://huggingface.co/datasets/Ci-Dave/DDR_dataset_train_test)
+
+It includes fundus images categorized into the following DR stages:
+- 0: No DR  
+- 1: Mild  
+- 2: Moderate  
+- 3: Severe  
+- 4: Proliferative DR
+
+## 🚀 How to Use
+1. Click the “Open in Spaces” button or visit the live app.
+2. Upload a fundus image (JPEG or PNG).
+3. View the model prediction and (optional) heatmap.
+
+## 🧠 Model Details
+- **Architecture**: DenseNet-121
+- **Pretrained on**: ImageNet
+- **Fine-tuned on**: Fundus images from the uploaded dataset
+
+## 🛠 Tools & Libraries
+- Streamlit
+- PyTorch / TensorFlow (depending on what you're using)
+- OpenCV for image preprocessing
+- Hugging Face Datasets
+
+## 📄 License
+This project is licensed under the MIT License.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+**Check the app 👉 [Live Demo](https://huggingface.co/spaces/Ci-Dave/DR_Classification)**