Create README.md

README.md

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+---
+library_name: keras
+license: mit
+language:
+- en
+pipeline_tag: image-classification
+tags:
+- cnn
+- medical-imaging
+- diabetic-retinopathy
+- transfer-learning
+- efficientnet
+- keras
+- tensorflow
+- deep-learning
+datasets:
+- diagnosis-of-diabetic-retinopathy
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- google/efficientnet-b3
+---
+
+# Model Card for TransferLearningDR: Diabetic Retinopathy Detector
+
+**TransferLearningDR** is a convolutional neural network (CNN) model built on **EfficientNetB3** and trained to classify retinal fundus images into two categories: **No DR** and **Diabetic Retinopathy (DR)**. The model leverages **transfer learning** from ImageNet weights to accelerate training and improve generalization.
+
+> ⚠️ **Disclaimer:** This model is intended **for research and educational purposes only**. It is **not a substitute for professional medical diagnosis**. Always consult a licensed healthcare provider for medical decisions.
+
+---
+
+## **Model Details**
+
+**Key Features:**
+- Binary classification of retinal images (**No DR vs DR**)  
+- Transfer learning using **EfficientNetB3** pretrained on ImageNet  
+- Input size: 224x224 RGB images, normalized  
+- Custom dense head with batch normalization, dropout, and L1/L2 regularization  
+- Evaluated on separate validation and test sets with high accuracy  
+
+**Skills & Technologies Used:**
+- TensorFlow / Keras for model design & training  
+- ImageDataGenerator for preprocessing and optional augmentation  
+- EfficientNetB3 for feature extraction  
+- Matplotlib & Seaborn for visualization  
+- Hugging Face Hub for model hosting  
+- Kaggle for dataset and training environment  
+
+---
+
+- **Developed by:** [Rawan Alwadeya](https://www.linkedin.com/in/rawan-alwadeya-17948a305/)  
+- **Model type:** Convolutional Neural Network (CNN) with Transfer Learning  
+- **Language(s):** N/A (Image model)  
+- **License:** MIT  
+
+---
+
+## **Uses**
+
+This model can be used for:  
+- Research in **diabetic retinopathy detection** and AI-assisted diagnosis  
+- Educational demonstrations of transfer learning on medical images  
+- Portfolio projects showcasing preprocessing, model training, and deployment  
+
+---
+
+### **Example Usage**
+
+```python
+import numpy as np
+from keras.models import load_model
+from PIL import Image
+import requests
+from io import BytesIO
+from huggingface_hub import hf_hub_download
+
+# Load the model from Hugging Face Hub
+model_path = hf_hub_download(repo_id="RawanAlwadeya/TransferLearningDR", filename="TransferLearningDR.h5")
+model = load_model(model_path)
+
+# Preprocess an example image
+def preprocess_image(image):
+    IMG_SIZE = (224, 224)
+    image = image.convert("RGB")  
+    image = image.resize(IMG_SIZE)
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)  # add batch dimension
+    return img_array
+
+# Example: load an image from URL
+url = "https://example.com/sample_fundus.jpg"
+response = requests.get(url)
+image = Image.open(BytesIO(response.content))
+
+img_array = preprocess_image(image)
+prediction = model.predict(img_array)[0][0]
+
+if prediction >= 0.5:
+    print("⚠️ Diabetic Retinopathy likely detected")
+else:
+    print("✅ Likely No DR")