From eab3f1f0d526c7d90c0f7ed8132ccaa44f5830f1 Mon Sep 17 00:00:00 2001
From: 2302_79014065 <2302_79014065@devide>
Date: Sun, 24 Mar 2024 11:18:00 +0800
Subject: [PATCH] Sun Mar 24 11:18:00 CST 2024 inscode

---
 main.py | 63 ++++++++++++++++++++++++++++++++++++++++++---------------
 1 file changed, 47 insertions(+), 16 deletions(-)

diff --git a/main.py b/main.py
index fb9ab94..e00b7bc 100644
--- a/main.py
+++ b/main.py
@@ -1,24 +1,55 @@
 import streamlit as st
-from streamlit_option_menu import option_menu
+import inscode
+import pandas as pd
+import matplotlib.pyplot as plt 
+import seaborn as sns
 
-# 设置Streamlit应用程序的标题
-st.set_page_config(page_title="app name", layout="wide")
+st.set_page_config(page_title="Text Analysis App", 
+                   page_icon=":memo:", 
+                   layout="wide")
 
-menu1="菜单1"
-menu2="菜单2"
+st.title("文本分析应用")
 
-with st.sidebar:
-    menu = option_menu("菜单", [menu1, menu2], 
-        icons=['house', "list-task"], 
-        menu_icon="cast", default_index=0)
+# 用户输入框
+user_input = st.text_input("请输入需要分析的文本：")
 
-def main():
+# 判断用户是否输入
+if user_input:
+    # 使用 AI 处理文本，获取分析结果
+    prompt = "分析结果如下："
+    content = user_input
+    result = inscode.ai(prompt, content)
 
-    if menu == menu1:
-        st.subheader(f"{menu1}")
+    # 展示情感分析结果
+    st.write("情感分析结果：")
+    st.write(result.sentiment)
 
-    if menu == menu2:
-        st.subheader(f"{menu2}")
+    # 展示主题分析结果
+    st.write("主题分析结果：")
+    topic_df = pd.DataFrame(result.topics, columns=["Topic", "Relevance"])
+    st.table(topic_df)
 
-if __name__ == '__main__':
-    main()
+    # 展示关键字分析结果
+    st.write("关键字分析结果：")
+    keywords_df = pd.DataFrame(result.keywords, columns=["Keyword", "Relevance"])
+    st.table(keywords_df)
+
+    # 绘制主题分析结果的图表
+    st.write("主题分析结果可视化：")
+    plt.figure(figsize=(10,6))
+    sns.barplot(x="Relevance", y="Topic", data=topic_df, color="b")
+    plt.title("主题分析结果")
+    plt.xlabel("相关性")
+    plt.ylabel("主题")
+    st.pyplot()
+
+    # 绘制关键字分析结果的图表
+    st.write("关键字分析结果可视化：")
+    plt.figure(figsize=(10,6))
+    sns.barplot(x="Relevance", y="Keyword", data=keywords_df, color="g")
+    plt.title("关键字分析结果")
+    plt.xlabel("相关性")
+    plt.ylabel("关键字")
+    st.pyplot()
+else:
+    st.write("请输入需要分析的文本")
\ No newline at end of file
-- 
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