| # 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 | |
| ''' |