app.py

# app.py
import gradio as gr
import pandas as pd
import joblib

# Load pre-trained model and dataset
data = pd.read_csv('Iris.csv')
data.drop(columns=['Id'], inplace=True)

# Load the saved model
model = joblib.load('best_random_forest_model.pkl')

def classify_iris(sepal_length, sepal_width, petal_length, petal_width):
    """Classify iris species based on input features."""
    input_features = [[sepal_length, sepal_width, petal_length, petal_width]]
    prediction = model.predict(input_features)[0]
    return prediction

# Define the Gradio interface
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.Textbox(label="Predicted Iris Species")

description = "This app classifies iris species (Setosa, Versicolor, Virginica) based on the given features."

gr.Interface(fn=classify_iris, inputs=inputs, outputs=outputs, title="Iris Species Classifier", description=description).launch()