57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
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from flask import Flask, render_template
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import pandas as pd
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from sklearn.manifold import TSNE
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from sklearn.preprocessing import LabelEncoder
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from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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from io import BytesIO
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import base64
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app = Flask(__name__)
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@app.route('/')
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def index():
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# Load the dataset
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df = pd.read_csv('top_240_restaurants_recommended_in_los_angeles_2.csv')
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# Select the columns for clustering
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columns_for_clustering = ['Rank', 'StarRating', 'NumberOfReviews', 'Style']
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# Encode the 'Style' column
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label_encoder = LabelEncoder()
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df['Style_encoded'] = label_encoder.fit_transform(df['Style'])
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# Select the columns for clustering
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data_for_clustering = df[['Rank', 'StarRating', 'NumberOfReviews', 'Style_encoded']]
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# Perform t-SNE for dimensionality reduction
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tsne = TSNE(n_components=2, random_state=42)
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tsne_result = tsne.fit_transform(data_for_clustering)
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# Perform k-means clustering
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kmeans = KMeans(n_clusters=3, random_state=42)
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df['Cluster'] = kmeans.fit_predict(data_for_clustering)
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# Add t-SNE results to the DataFrame
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df['tsne_1'] = tsne_result[:, 0]
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df['tsne_2'] = tsne_result[:, 1]
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# Save the figure to a BytesIO object
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plt.scatter(df['tsne_1'], df['tsne_2'], c=df['Cluster'], cmap='viridis')
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plt.title('t-SNE Clustering')
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plt.xlabel('t-SNE Component 1')
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plt.ylabel('t-SNE Component 2')
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plt.tight_layout()
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img_data = BytesIO()
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plt.savefig(img_data, format='png')
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img_data.seek(0)
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# Convert the BytesIO object to base64 for embedding in HTML
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img_base64 = base64.b64encode(img_data.getvalue()).decode('utf-8')
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# Render the HTML template with the embedded image
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return render_template('index.html', img_base64=img_base64)
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if __name__ == '__main__':
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app.run(debug=True)
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