IrisModel.py

!pip install gradio 

import gradio as gr
import pickle
import numpy as np
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

# Load iris dataset and train the model
iris = load_iris()
X = iris.data
y = iris.target

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

# Save the model
with open('model.pkl', 'wb') as f:
    pickle.dump(model, f)

# Load the trained model
with open('model.pkl', 'rb') as f:
    model = pickle.load(f)

def predict(sepal_length, sepal_width, petal_length, petal_width):
    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
    prediction = model.predict(input_data)
    return iris.target_names[prediction[0]]

interface = gr.Interface(
    fn=predict,
    inputs=["number", "number", "number", "number"],
    outputs="text",
    title="Iris Flower Classifier",
    description="Enter the features of the iris flower to predict its species."
)

interface.launch()