I added an Air Jordan classifier

aj_classifier.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e21d89dc770349163a0565ab5fa5d5f0d2a30b00b0a08db9c388a1192e1bb0a1
+size 46988022

app.py

@@ -1,7 +1,37 @@
-import gradio as gr
+import gradio as gr #Gradio for creating the web interface
+from fastai.vision.all import load_learner, PILImage #FastAI fxns for model loading and image processing 
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load the trained model
+learner = load_learner('aj_classifier.pkl')
+
+def classify_image(img):
+    """
+    Function to classify an uploaded image using the trained model.
+    
+    Args:
+    - img: The image uploaded by the user, received as a PILImage.
+    
+    Returns:
+    - A string with the prediction and the probability of that prediction.
+    """
+    # Use the model to predict the class of the image
+    # 'predict' method returns three values: the predicted class, its index, and the probabilities of all classes.
+    pred, pred_idx, probs = learner.predict(img)
+    
+    # Format the prediction and its probability as a string to show to the user
+    return f"This is an Air Jordan {pred}; {(probs[pred_idx]* 100):.02f} accurate"
+
+# Create a Gradio interface
+# This part sets up the Gradio web interface, specifying the function to call for predictions,
+# the type of input it expects (an image), and the type of output (text).
+iface = gr.Interface(fn=classify_image,
+                     inputs=gr.Image(type='pil'),  # Specifies that the input should be an image, automatically converted to PILImage
+                     outputs="text",  # Specifies that the output is text (the prediction and probability)
+                     title="Air Jordan Model Classifier",  # Title of the web interface
+                     description="Upload an image of Air Jordan sneakers, and the classifier will predict the model.") 
+
+# This condition checks if the script is being run as the main program and launches the Gradio interface.
+# It ensures that the Gradio server starts only when this script is executed directly, not when imported as a module.
+if __name__ == "__main__":
+    iface.launch(share=True)  # Starts the Gradio interface
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()