Update README.md

README.md

@@ -60,4 +60,96 @@ Training accuracy  - trained for 50 epochs, reaching 83% accuracy within our tra
 ![confusion matrix](https://drive.google.com/file/d/1lI7pps03RXTFKYjY_iv4UPeSOhqQhxQB/view)
 
 We submitted our model for validation to the [APTOS 2019 Blindness Detection Competition](https://www.kaggle.com/competitions/aptos2019-blindness-detection/submissions#), 
-achieving a private score of 0.869345
+achieving a private score of 0.869345
+
+## Trying the model
+
+Note: You can easily try our model [here](https://www.kaggle.com/code/josemauriciodelgado/proliferative-retinopathy)
+
+This application uses a trained model to detect the severity of diabetic retinopathy from a given retina image taken using fundus photography. The severity levels are:
+
+- 0 - No DR
+- 1 - Mild
+- 2 - Moderate
+- 3 - Severe
+- 4 - Proliferative DR
+
+### How to Use the Model
+
+To use the model, you need to provide an image of the retina taken using fundus photography. The model will then predict the severity of diabetic retinopathy and return a dictionary where the keys are the severity levels and the values are the corresponding probabilities.
+
+### Breakdown of the `app.py` File
+
+Here's a breakdown of what the `app.py` file is doing:
+
+1. **Import necessary libraries**: The file starts by importing the necessary libraries. This includes `gradio` for creating the UI, `fastai.vision.all` for loading the trained model, and `skimage` for image processing.
+
+2. **Define helper functions**: The `get_x` and `get_y` functions are defined. These functions are used to get the x and y values from the input dictionary. In this case, the x value is the image and the y value is the diagnosis.
+
+3. **Load the trained model**: The trained model is loaded from the `model.pkl` file using the `load_learner` function from `fastai`.
+
+4. **Define label descriptions**: A dictionary is defined to map label numbers to descriptions. This is used to return descriptions instead of numbers in the prediction result.
+
+5. **Define the prediction function**: The `predict` function is defined. This function takes an image as input, makes a prediction using the trained model, and returns a dictionary where the keys are the severity levels and the values are the corresponding probabilities.
+
+6. **Define title and description**: The title and description of the application are defined. These will be displayed in the Gradio UI.
+
+To run the application, you need to create a Gradio interface with the `predict` function as the prediction function, an image as the input, and a label as the output. You can then launch the interface to start the application.
+
+```import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+# Define the functions to get the x and y values from the input dictionary - in this case, the x value is the image and the y value is the diagnosis
+# needed to load the model since we defined them during training
+def get_x(r): return ""
+
+def get_y(r): return r['diagnosis']
+
+learn = load_learner('model.pkl')
+labels = learn.dls.vocab
+
+# Define the mapping from label numbers to descriptions
+label_descriptions = {
+    0: "No DR",
+    1: "Mild",
+    2: "Moderate",
+    3: "Severe",
+    4: "Proliferative DR"
+}
+
+def predict(img):
+    img = PILImage.create(img)
+    pred, pred_idx, probs = learn.predict(img)
+    # Use the label_descriptions dictionary to return descriptions instead of numbers
+    return {label_descriptions[labels[i]]: float(probs[i]) for i in range(len(labels))}
+
+title = "Diabetic Retinopathy Detection"
+description = """Detects severity of diabetic retinopathy from a given retina image taken using fundus photography -
+
+    0 - No DR
+
+    1 - Mild
+
+    2 - Moderate
+
+    3 - Severe
+
+    4 - Proliferative DR
+"""
+article = "<p style='text-align: center'><a href='https://www.kaggle.com/code/josemauriciodelgado/proliferative-retinopathy' target='_blank'>Notebook</a></p>"
+
+# Get a list of all image paths in the test folder
+test_folder = "test"  # replace with the actual path to your test folder
+image_paths = [os.path.join(test_folder, img) for img in os.listdir(test_folder) if img.endswith(('.png', '.jpg', '.jpeg'))]
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Label(num_top_classes=5),
+    examples=image_paths,  # set the examples parameter to the list of image paths
+    article=article,
+    title=title,
+    description=description,
+).launch()
+```