app.py

import gradio as gr
import numpy as np
import pickle

# Load model
with open("iris_model.pkl", "rb") as f:
    model = pickle.load(f)

# Define prediction function
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)[0]
    species = ["Setosa 🌼", "Versicolor 🌷", "Virginica 🌹"]
    return f"The predicted species is: **{species[prediction]}**"

# Create the app layout
with gr.Blocks(theme="soft") as demo:
    gr.Markdown("# 🌺 Iris Flower Classifier")
    gr.Markdown("Enter the flower measurements below to predict its species using a trained Machine Learning model.")

    with gr.Row():
        sepal_length = gr.Number(label="📏 Sepal Length (cm)")
        sepal_width = gr.Number(label="📏 Sepal Width (cm)")
    with gr.Row():
        petal_length = gr.Number(label="🌸 Petal Length (cm)")
        petal_width = gr.Number(label="🌸 Petal Width (cm)")

    predict_btn = gr.Button("🔍 Predict Species")
    output = gr.Markdown()

    predict_btn.click(fn=predict, inputs=[sepal_length, sepal_width, petal_length, petal_width], outputs=output)

# Launch the app
demo.launch()