Add application file

app.py

@@ -2,14 +2,21 @@
 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
 
-# Load the pre-trained Decision Tree classifier from the joblib file
+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="brjapon/iris-dt",
+    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
+)
 
-#pipeline = joblib.load("./models/iris_dt.joblib")
-#Use the model hosted on huggingface. Usar de guia archivo de Bernardo para no ejecutar en local
-
-pipeline = joblib.load("ManuelMC/iris_dt.joblib")
+# 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.
@@ -55,3 +62,5 @@ if __name__ == "__main__":
 # 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
 '''
+
+