Delete app.py

app.py

@@ -1,52 +0,0 @@
-# Import the necessary libraries
-import gradio as gr  # Gradio is a library to quickly build and share demos for ML models
-import joblib        # joblib is used here to load the trained model from a file
-import numpy as np   # NumPy for numerical operations (if needed for array manipulation)
-
-# Load the pre-trained Decision Tree classifier from the joblib file
-pipeline = joblib.load("./models/iris_dt.joblib")
-
-# Define a function that takes the four iris measurements as input
-# and returns the predicted iris species label.
-def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
-    # Convert the input parameters into a 2D list/array because
-    # scikit-learn's predict() expects a 2D array of shape (n_samples, n_features)
-    input = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
-    prediction = pipeline.predict(input)
-    
-    # Convert the prediction to the string label
-    if prediction == 0:
-        return 'iris-setosa'
-    elif prediction == 1:
-        return 'Iris-versicolor'
-    elif prediction == 2:
-        return 'Iris-virginica'
-    else:
-        return "Invalid prediction"
-
-# Create a Gradio Interface:
-# - fn: the function to call for inference
-# - inputs: a list of component types to collect user input (in this case, four numeric values)
-# - outputs: how the prediction is displayed (in this case, as text)
-# - live: whether to update the output in real-time as the user types
-interface = gr.Interface(
-    fn=predict_iris,
-    inputs=["number", "number", "number", "number"],
-    outputs="text",
-    live=True,
-    title="Iris Species Identifier",
-    description="Enter the four measurements to predict the Iris species."
-)
-
-# Run the interface when this script is executed directly.
-# This will launch a local Gradio server and open a user interface in the browser.
-if __name__ == "__main__":
-    # To create a public link, set the parameter share=True
-    interface.launch()
-    
-'''
-# The Flag button allows users (or testers) to mark or “flag”
-# a particular input-output interaction for later review.
-# When someone clicks Flag, Gradio saves the input values (and often the output) to a log.csv file
-# letting you keep track of interesting or potentially problematic cases for debugging or analysis later on
-'''