diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/__pycache__/app.cpython-310.pyc b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/__pycache__/app.cpython-310.pyc
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diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/app.py b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/app.py
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+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/app.py	
@@ -0,0 +1,22 @@
+# Importing Necessary Modules
+from flask import Flask, request, render_template
+from gnb_model import make_predict
+app = Flask(__name__)
+
+result = []
+@app.route("/", methods=['GET', 'POST'])
+def input():
+	global result
+	if request.method == 'POST':
+		print(request.form)
+		if "clean" in request.form.keys():
+			result = []
+		sentence = request.form['sentence']
+		if len(sentence) != 0:
+			result.append((sentence, make_predict(sentence)))
+			# result.append((sentence, "不是"))
+	return render_template("input.html", result=result)
+
+# main route to start with
+if __name__ == '__main__':
+	app.run(debug=True, host='0.0.0.0')
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/gnb_model.py b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/gnb_model.py
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+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/gnb_model.py	
@@ -0,0 +1,77 @@
+import numpy as np
+import pandas as pd
+import jieba
+from pathlib import Path
+PATH_DATASET = Path.cwd().joinpath("../../dataset")
+DELETE_STOPWORDS = False
+
+df = pd.read_csv(PATH_DATASET.joinpath("message80W1.csv"), header=None)
+
+N_pos, N_neg = 10000, 10000
+df_positive = df[df[1]==0]
+df_negative = df[df[1]==1]
+np.random.seed(42)
+def sample_df(df, N):
+    indexs = np.random.choice(np.arange(len(df)), N)
+    return df.iloc[indexs,2]
+corpus_pos = sample_df(df_positive, N_pos)
+corpus_neg = sample_df(df_negative, N_neg)
+# corpus_pos = df_positive.sample(n=N_pos, random_state=42).iloc[:,2]
+# corpus_neg = df_negative.sample(n=N_neg, random_state=42).iloc[:,2]
+corpus = np.concatenate([corpus_pos, corpus_neg]).reshape(-1,1)
+y = np.concatenate([np.full(N_pos, 1), np.full(N_neg, 0)])
+
+corpus_cut = np.apply_along_axis(lambda x: ' '.join(jieba.cut(x[0])), axis=1, arr=corpus)
+
+from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
+
+with open(PATH_DATASET.joinpath("stopword.txt"), encoding='gbk') as file:
+    stopwords = file.read().split()
+
+# 频率向量化，token_pattern不取单个词作为特征的问题: https://blog.csdn.net/xxzhix/article/details/82685372
+if not DELETE_STOPWORDS:
+    vectorizer = CountVectorizer(token_pattern='[\u4e00-\u9fa5_a-zA-Z0-9]{1,}')
+else:
+    vectorizer_stopwords = CountVectorizer(token_pattern='[\u4e00-\u9fa5_a-zA-Z0-9]{1,}', stop_words=stopwords)
+X = vectorizer.fit_transform(corpus_cut)
+tfidf = TfidfTransformer()
+X = tfidf.fit_transform(X)
+
+def to_vector(X, stopwords=False):
+    X = np.array(X).reshape(-1, 1)
+    cut = np.apply_along_axis(lambda x: ' '.join(jieba.cut(x[0])), axis=1, arr=X)
+    vector = vectorizer.transform(cut)
+    return tfidf.transform(vector)
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)
+
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score
+gnb = GaussianNB()
+print("拟合中......")
+gnb.fit(X_train.toarray(), y_train)
+
+print("预测中......")
+pred_train = gnb.predict(X_train.toarray())
+pred_test = gnb.predict(X_test.toarray())
+acc_train = accuracy_score(y_train, pred_train)
+acc_test = accuracy_score(y_test, pred_test)
+print(f"准确率 train/test: {acc_train:.4f}/{acc_test:.4f}")
+
+def make_predict(string:str):
+    string = [string]
+    vector = to_vector(string).toarray()
+    ret = "是垃圾" if gnb.predict(vector) == 0 else "不是垃圾"
+    return ret
+
+if __name__ == '__main__':
+    print(make_predict("尊敬的客上，感谢您一直的支持，亿美亿康美容部特在本月的x、x、x三天举办秒杀活动，现场更是优惠多多，开抢倒计时还有两天，欲抢从速！xx号艳艳"))
+    print(make_predict("CSC喜欢打游戏"))
+    print(make_predict("一刀999"))
+    print(make_predict("你好"))
+    print(make_predict("尊敬的客上，感谢您一直的支持，亿美亿康美容部特在本月的x、x、x三天举办秒杀活动，现场更是优惠多多，开抢倒计时还有两天，欲抢从速！xx号艳艳"))
+    print(make_predict("秒杀价格8848，8848你值得拥有"))
+    print(make_predict("有博主做过同类防晒霜的对比"))
+    print(make_predict("csc每天打游戏"))
+    print(make_predict("今天电脑爆炸了"))
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diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/Temp.html b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/Temp.html
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+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/Temp.html	
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+<!DOCTYPE html>
+<html>
+    <head>
+        <meta charset="utf-8">
+        <title>进入</title>
+        <style>
+            body {
+                text-align: center;
+                background-color: green;
+            }
+            form {
+                display: inline-block;
+            }
+        </style>
+    </head>
+    <body>
+        <h3>There is a form.</h3>
+        <form action="/passing" method="post">
+            <p>Name <input type="text", name="name"></p>
+            <p>Email <input type="email", name="email"></p>
+            <p>Phon nume <input type="text", name="phone"></p>
+            <p><input type="submit" value="Submit!!!"></p>
+        </form>
+        <h4>HA!HA!HA!HA!</h4>
+    </body>
+</html>
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diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/hello.html b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/hello.html
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+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/hello.html	
@@ -0,0 +1,14 @@
+<!DOCTYPE html>
+<html>
+    <head>
+        <meta charset="utf-8">
+        <title>Hello from Flask</title>
+    </head>
+    <body>
+        {% if name %}
+        <h1>Hello {{ name }}!</h1>
+        {% else %}
+        <h1>Hello, World!</h1>
+        {% endif %}
+    </body>
+</html>
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diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/input.html b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/input.html
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+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/input.html	
@@ -0,0 +1,39 @@
+<!DOCTYPE html>
+<html>
+    <header>
+        <style>
+            body {
+                text-align: center;
+            }
+        </style>
+        <meta charset="utf-8">
+        <title>CSC识别器</title>
+    </header>
+    <body>
+        <form action="/" method="post">
+            <p>输入待识别文本：<input type="text" name="sentence" minlength="1"></p>
+            <p><button>提交</button></p>
+            <p><button name="clean">清空历史信息</button></p>
+            <!-- <p><input type="submit" name="提交"></p> -->
+        </form>
+        {% if result|length >= 1 %}
+        <table align="center" border="1">
+            <thead><tr><th colspan="2">
+                <strong>历史信息</strong>
+            </th></tr></thead>
+            <tbody>
+                <tr>
+                    <td>文本</td>
+                    <td>是/否为垃圾</td>
+                </tr>
+                {% for sentence, predict in result %}
+                <tr>
+                    <td>{{ sentence }}</td>
+                    <td>{{ predict }}</td>
+                </tr>
+                {% endfor %}
+            </tbody>
+        </table>
+        {% endif %}
+    </body>
+</html>
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/result_data.html b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/result_data.html
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--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/result_data.html	
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+<!DOCTYPE html>
+<html>
+    <head>
+        <style>
+            body {
+                text-align: center;
+                background-color: orange;
+            }
+            table {
+                display: inline-block;
+                border-collapse: collapse;
+            }
+        </style>
+    </head>
+    <body>
+        <p><strong>Your Details</strong></p>
+        <table border=1>
+            {% for key, value in result.items() %}
+            <tr>
+                <th>{{ key }}</th>
+                <th>{{ value }}</th>
+            </tr>
+            {% endfor %}
+        </table>
+    </body>
+</html>
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