From 3bd2b44d14efd220265a366b33f32a6b0abbf9a0 Mon Sep 17 00:00:00 2001
From: cxy <1295423769@qq.com>
Date: Tue, 17 Sep 2024 19:04:09 +0800
Subject: [PATCH] =?UTF-8?q?=E6=97=A0?=
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---
 app.py | 114 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 114 insertions(+)
 create mode 100644 app.py

diff --git a/app.py b/app.py
new file mode 100644
index 0000000..ff9c80a
--- /dev/null
+++ b/app.py
@@ -0,0 +1,114 @@
+import logging
+import random
+import re
+import jieba
+import pandas as pd
+from flask import Flask, render_template, jsonify
+from nltk.corpus import stopwords
+
+import utils
+
+app = Flask(__name__)
+
+
+@app.before_request
+def setup_logging():
+    # 确保日志处理器已正确初始化
+    if not logging.getLogger('werkzeug').handlers:
+        logging.getLogger('werkzeug').addHandler(logging.StreamHandler())
+
+    class RequestFilter(logging.Filter):
+        def filter(self, record):
+            return 'GET /time' not in record.getMessage()
+
+    handler = logging.getLogger('werkzeug').handlers[0]
+
+    handler.addFilter(RequestFilter())
+
+
+@app.route('/')
+def hello_world():  # put application's code here
+    return render_template("main.html")
+
+
+@app.route('/time')
+def get_time():
+    return utils.get_time()
+
+
+@app.route('/data')
+def get_data():
+    df = pd.read_csv('./static/csv/barrage_clustered.csv')
+    data = df.to_dict(orient='records')
+    return jsonify(data)
+
+
+@app.route('/wordcloud')
+def wordcloud_data():
+    file_name = './static/csv/barrage.csv'
+    with open(file_name, encoding='utf-8') as f:
+        txt = f.read()
+
+    txt_list = jieba.lcut(txt)
+
+    stopwords_list = set(stopwords.words('chinese'))
+    stopwords_target = ['都', '不', '好', '哈哈哈', '说', '还', '很', '没']
+    for i in stopwords_target:
+        stopwords_list.add(i)
+
+    word_freq = {}
+    for word in txt_list:
+        if re.match(r'^[\u4e00-\u9fa5]+$', word) and word not in stopwords_list:
+            if word in word_freq:
+                word_freq[word] += 1
+            else:
+                word_freq[word] = 1
+    word_freq_list = [{'name': word, 'value': freq} for word, freq in word_freq.items()]
+    return jsonify(word_freq_list)
+
+
+@app.route('/world_comment')
+def get_world_comment():
+    file_path = './static/csv/world_comment.csv'
+    df = pd.read_csv(file_path)
+    data = []
+    grouped = df.groupby('url')
+    times = ['20s', '30s', '40s', '50s', '60s']
+    for url, group in grouped:
+        items = group['content'].tolist()
+        time = random.choice(times)
+        data.append({'time': time, 'items': items})
+    return jsonify(data)
+
+
+@app.route('/barrage_sentiment')
+def get_barrage_comment():
+    df = pd.read_csv('./static/csv/barrage_sentiment.csv')
+    data = df.to_dict(orient='records')
+    return jsonify(data)
+
+
+@app.route('/barrage_count')
+def count_rows():
+    df = pd.read_csv('./static/csv/barrage.csv')
+    row_count = len(df['barrage'])
+    return jsonify({'row_count': row_count})
+
+
+@app.route('/average_sentiment')
+def average_sentiment():
+    df = pd.read_csv('./static/csv/barrage_sentiment.csv')
+    avg_sentiment = df['sentiment'].mean()
+    return jsonify({'average_sentiment': avg_sentiment})
+
+
+@app.route('/count_keywords')
+def count_keywords():
+    df = pd.read_csv('./static/csv/barrage.csv')
+    keyword_count = df['barrage'].str.contains('AI技术|人工智能|科技|智能').sum()
+    keyword_count = int(keyword_count)
+    return jsonify({'keyword_count': keyword_count})
+
+
+if __name__ == '__main__':
+    app.run()