app.py

import joblib
import numpy as np
from sklearn import datasets
import gradio as gr
import os

# Load the model and class names
if os.path.exists('new_iris_model.pkl'):
    model = joblib.load('new_iris_model.pkl')
else:
    model = joblib.load('iris_model.pkl')

iris = datasets.load_iris()
class_names = iris.target_names

# Define prediction function
def predict_species(sepal_length, sepal_width, petal_length, petal_width):
    features = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
    prediction = model.predict(features)[0]
    return f"Iris {class_names[prediction]}"

# Create Gradio interface
demo = gr.Interface(
    fn=predict_species,
    inputs=[
        gr.Number(label="Sepal Length"),
        gr.Number(label="Sepal Width"),
        gr.Number(label="Petal Length"),
        gr.Number(label="Petal Width")
    ],
    outputs="text",
    title="Iris Flower Classifier"
)

demo.launch()