import numpy as np
import pandas as pd
import gradio as gr
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from PIL import Image
from io import BytesIO

# Load your dataset
dataset = pd.read_csv('Flipcart.com Clusturing Model.csv')
X = dataset.iloc[:, [2, 4]].values

# Create a K-Means clustering model
kmeans = KMeans(n_clusters=4, init='k-means++', random_state=42)
y_means = kmeans.fit_predict(X)

# Function to perform clustering and return cluster labels and the cluster visualization image
def cluster_data(age, purchase_rating):
    features = np.array([age, purchase_rating]).reshape(1, -1)
    cluster = kmeans.predict(features)[0]

    # Scatter plot to visualize clusters
    plt.figure(figsize=(8, 6))
    plt.scatter(X[y_means == 0, 0], X[y_means == 0, 1], s=100, c='magenta', label='Cluster 1')
    plt.scatter(X[y_means == 1, 0], X[y_means == 1, 1], s=100, c='blue', label='Cluster 2')
    plt.scatter(X[y_means == 2, 0], X[y_means == 2, 1], s=100, c='red', label='Cluster 3')
    plt.scatter(X[y_means == 3, 0], X[y_means == 3, 1], s=100, c='cyan', label='Cluster 4')
    plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='black', label='Centroids')
    plt.title('Cluster of Amazon users')
    plt.xlabel('Age')
    plt.ylabel('Purchase Rating')
    plt.legend()
    plt.grid(True)

    # Save the plot as an image
    image_buffer = BytesIO()
    plt.savefig(image_buffer, format='png')
    image_buffer.seek(0)

    # Create a PIL image from the buffer
    pil_image = Image.open(image_buffer)
    
    return f'Data point belongs to Cluster {cluster}', pil_image

# Create a Gradio interface for the clustering model
iface = gr.Interface(
    fn=cluster_data,
    inputs=[
        gr.inputs.Number(label="Age"),
        gr.inputs.Number(label="Purchase Rating")
    ],
    outputs=[
        gr.outputs.Textbox(label="Cluster"),
        gr.outputs.Image(label="Cluster Visualization", type="pil")
    ],
     examples = [[23,44],
                [26,91],
                 [72,5]],
    live = True, 
    description = " Press flag if any erroneous output comes ",
    theme=gr.themes.Soft(),
    title = "FlipKart User Segmentation"
)

# Launch the Gradio app
iface.launch(inline=False)