app.py

import gradio as gr
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Test the model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

# Prediction function
def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
    input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
    prediction = model.predict(input_data)
    prediction_name = iris.target_names[prediction[0]]
    return f"Predicted Iris class: {prediction_name}"

# Gradio Interface
description = f"""
### Logistic Regression on Iris Dataset
Model Accuracy on Test Data: {accuracy * 100:.2f}%
"""
interface = gr.Interface(
    fn=predict_iris,
    inputs=[
        gr.inputs.Number(label="Sepal Length (cm)"),
        gr.inputs.Number(label="Sepal Width (cm)"),
        gr.inputs.Number(label="Petal Length (cm)"),
        gr.inputs.Number(label="Petal Width (cm)")
    ],
    outputs="text",
    title="Iris Flower Classification",
    description=description
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()