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from flask import Flask,render_template,request
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import pickle
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import numpy as np
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merged=pickle.load(open('models/merged_df.pkl', 'rb'))
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pt=pickle.load(open('models/pt.pkl', 'rb'))
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anime_dt=pickle.load(open('models/anime_dt.pkl', 'rb'))
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similarity_scores=pickle.load(open('models/similarity_scores.pkl', 'rb'))
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app = Flask(__name__)
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@app.route('/')
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def index():
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return render_template('index.html',
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anime_name=list(merged['Name'].values),
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score=list(merged['score'].values),
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scored_by=list(merged['scored_by'].values),
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Genres=list(merged['Genres'].values),
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Studios=list(merged['Studios'].values),
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image=list(merged['Image URL'].values)
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)
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@app.route('/recommend')
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def recommend_ui():
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return render_template('recommend.html')
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@app.route('/recommend_animes',methods=['post'])
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def recommend():
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user_input = request.form.get('user_input')
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index = np.where(pt.index == user_input)[0][0]
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similar_items = sorted(list(enumerate(similarity_scores[index])), key=lambda x: x[1], reverse=True)[1:5]
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data = []
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for i in similar_items:
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item = []
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temp_df = anime_dt[anime_dt['Name'] == pt.index[i[0]]]
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item.extend(list(temp_df.drop_duplicates('Name')['Name'].values))
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item.extend(list(temp_df.drop_duplicates('Name')['Genres'].values))
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item.extend(list(temp_df.drop_duplicates('Name')['Image URL'].values))
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item.extend(list(temp_df.drop_duplicates('Name')['Studios'].values))
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data.append(item)
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print(data)
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return render_template('recommend.html',data=data)
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if __name__ == '__main__':
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app.run(debug=True) |
