# -*- coding: utf-8 -*-
"""Iris_Flower_Classifier.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1ddsKnOLQk_nPeF9zu0Qr9yTsvmg-0D8S
"""

import gradio as gr
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import make_pipeline

# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target
feature_names = iris.feature_names
target_names = iris.target_names

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train a RandomForest model
model = make_pipeline(StandardScaler(), RandomForestClassifier())
model.fit(X_train, y_train)

# Define the prediction function
def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
    feature_values = np.array([sepal_length, sepal_width, petal_length, petal_width]).reshape(1, -1)
    prediction = model.predict(feature_values)
    return target_names[prediction[0]]

# Create a Gradio interface
interface = gr.Interface(
    fn=predict_iris,
    inputs=[
        gr.Slider(minimum=float(X[:, 0].min()), maximum=float(X[:, 0].max()), value=float(np.mean(X[:, 0])), label="Sepal Length (cm)"), # Changed 'default' to 'value'
        gr.Slider(minimum=float(X[:, 1].min()), maximum=float(X[:, 1].max()), value=float(np.mean(X[:, 1])), label="Sepal Width (cm)"), # Changed 'default' to 'value'
        gr.Slider(minimum=float(X[:, 2].min()), maximum=float(X[:, 2].max()), value=float(np.mean(X[:, 2])), label="Petal Length (cm)"), # Changed 'default' to 'value'
        gr.Slider(minimum=float(X[:, 3].min()), maximum=float(X[:, 3].max()), value=float(np.mean(X[:, 3])), label="Petal Width (cm)") # Changed 'default' to 'value'
    ],
    outputs="text",
    title="Iris Flower Classifier",
    description="Select the features of the iris flower to predict its species."
)

# Launch the interface
if __name__ == "__main__":
    interface.launch(inline=False)