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# 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)
from huggingface_hub import hf_hub_download
HF_TOKEN = 'hf_your_token_here' # Replace with your actual Hugging Face token
# Replace with your actual Hugging Face model repo ID and file names
# For example, repo_id="username/iris-decision-tree"
# Use repo_type="model" if it's a model repository
model_path = hf_hub_download(
repo_id="Jesus02/iris-model-clase",
filename="iris_dt.joblib", # The model file stored in the HF repo
repo_type="model" # Could also be 'dataset' if you're storing it that way
)
# Load the trained model
pipeline = joblib.load(model_path)
# 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
''' |