Update README.md

README.md

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 ---
 license: mit
+inference: false
+tags:
+- medical
+- code
 ---
+# EfficientNet Parkinson's Prediction Model 🤗
+
+This repository contains the Hugging Face EfficientNet model for predicting Parkinson's disease using patient drawings with an accuracy of around 65%.
+Made w/ EfficientNet and Torch.
+
+## Overview
+
+Parkinson's disease is a progressive nervous system disorder that affects movement. Symptoms start gradually, sometimes starting with a barely noticeable tremor in just one hand. Tremors are common, but the disorder also commonly causes stiffness or slowing of movement.
+
+My model uses the EfficientNet architecture to predict the likelihood of Parkinson's disease in patients by analysing their drawings. 
+Feel free to open a pull request and contribute if you want to.
+
+## Dataset
+
+The dataset used to train this model was provided by [Kaggle](https://www.kaggle.com/datasets/kmader/parkinsons-drawings).
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModel
+from torch import nn
+from PIL import Image
+import numpy as np
+
+# Set the device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load the trained model
+model = AutoModel.from_pretrained('/content/final')
+
+# Move the model to the device
+model = model.to(device)
+
+# Load and resize new image(s)
+image_size = (224, 224)
+new_image = Image.open('/content/health.png').convert('RGB').resize(image_size)
+new_image = np.array(new_image)
+new_image = torch.from_numpy(new_image).transpose(0, 2).float().unsqueeze(0)
+
+# Move the data to the device
+new_image = new_image.to(device)
+
+# Make predictions using the trained model
+with torch.no_grad():
+    predictions = model(new_image)
+    logits = predictions.last_hidden_state
+    logits = logits.view(logits.shape[0], -1)
+    num_classes=2
+    feature_reducer = nn.Linear(logits.shape[1], num_classes)
+
+    logits = logits.to(device)
+    feature_reducer = feature_reducer.to(device)
+
+    logits = feature_reducer(logits)
+    predicted_class = torch.argmax(logits, dim=1).item()
+    confidence = torch.softmax(logits, dim=1)[0][predicted_class].item()
+    if(predicted_class == 0):
+        print(f'Predicted class: Parkinson\'s with confidence {confidence:.2f}')
+    else:
+        print(f'Predicted class: Healthy with confidence {confidence:.2f}')
+
+