app.py

import gradio as gr
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
import pandas as pd

# Load and train model
iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = iris.target

model = DecisionTreeClassifier()
model.fit(X, y)

# Prediction function
def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
    input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
    pred = model.predict(input_data)[0]
    return iris.target_names[pred]

# Gradio interface
iface = gr.Interface(
    fn=predict_iris,
    inputs=[
        gr.Number(label="Sepal Length (cm)"),
        gr.Number(label="Sepal Width (cm)"),
        gr.Number(label="Petal Length (cm)"),
        gr.Number(label="Petal Width (cm)")
    ],
    outputs=gr.Text(label="Predicted Iris Species"),
    title="Iris Flower Classifier",
    description="A Decision Tree model to classify Iris species based on flower measurements."
)

# Launch app with explicit host and port for Hugging Face
if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)