Update app.py

app.py

@@ -27,102 +27,100 @@ def doc_emb(doc):  # 文档向量化
         value="""操作说明 step 3：PDF解析提交成功！ 🙋 可以开始对话啦~"""), gr.Chatbot.update(visible=True)
 
 
-def get_response(open_ai_key, msg, bot, doc_text_list, doc_embeddings):
-    now_len = len(msg)
-    his_bg = -1
-    for i in range(len(bot) - 1, -1, -1):
-        if now_len + len(bot[i][0]) + len(bot[i][1]) > history_max_len:
+def get_response(open_ai_key, msg, bot, doc_text_list, doc_embeddings):  # 获取机器人回复
+    now_len = len(msg)  # 当前输入的长度
+    his_bg = -1  # 历史记录的起始位置
+    for i in range(len(bot) - 1, -1, -1):  # 从后往前遍历历史记录
+        if now_len + len(bot[i][0]) + len(bot[i][1]) > history_max_len:  # 如果超过了历史记录的最大长度，就不再加入
             break
-        now_len += len(bot[i][0]) + len(bot[i][1])
-        his_bg = i
-    history = [] if his_bg == -1 else bot[his_bg:]
-    query_embedding = embedder.encode([msg])
-    cos_scores = util.cos_sim(query_embedding, doc_embeddings)[0]
-    score_index = [[score, index] for score, index in zip(cos_scores, [i for i in range(len(cos_scores))])]
-    score_index.sort(key=lambda x: x[0], reverse=True)
+        now_len += len(bot[i][0]) + len(bot[i][1])  # 更新当前长度
+        his_bg = i  # 更新历史记录的起始位置
+    history = [] if his_bg == -1 else bot[his_bg:]  # 获取历史记录
+    query_embedding = embedder.encode([msg])  # 输入向量化
+    cos_scores = util.cos_sim(query_embedding, doc_embeddings)[0]  # 计算相似度
+    score_index = [[score, index] for score, index in zip(cos_scores, [i for i in range(len(cos_scores))])]  # 相似度和索引对应
+    score_index.sort(key=lambda x: x[0], reverse=True)  # 按相似度排序
     print('score_index:\n', score_index)
-    index_set, sub_doc_list = set(), []
-    for s_i in score_index:
-        doc = doc_text_list[s_i[1]]
-        if now_len + len(doc) > all_max_len:
+    index_set, sub_doc_list = set(), []  # 用于存储最终的索引和文档
+    for s_i in score_index:  # 遍历相似度和索引对应
+        doc = doc_text_list[s_i[1]]  # 获取文档
+        if now_len + len(doc) > all_max_len:  # 如果超过了最大长度，就不再加入
             break
-        index_set.add(s_i[1])
-        now_len += len(doc)
+        index_set.add(s_i[1])  # 加入索引
+        now_len += len(doc)  # 更新当前长度
         # 可能段落截断错误，所以把上下段也加入进来
-        if s_i[1] > 0 and s_i[1] - 1 not in index_set:
-            doc = doc_text_list[s_i[1] - 1]
-            if now_len + len(doc) > all_max_len:
+        if s_i[1] > 0 and s_i[1] - 1 not in index_set:  # 如果上一段没有加入
+            doc = doc_text_list[s_i[1] - 1]  # 获取上一段
+            if now_len + len(doc) > all_max_len:  # 如果超过了最大长度，就不再加入
                 break
-            index_set.add(s_i[1] - 1)
-            now_len += len(doc)
-        if s_i[1] + 1 < len(doc_text_list) and s_i[1] + 1 not in index_set:
-            doc = doc_text_list[s_i[1] + 1]
-            if now_len + len(doc) > all_max_len:
+            index_set.add(s_i[1] - 1)  # 加入索引
+            now_len += len(doc)  # 更新当前长度
+        if s_i[1] + 1 < len(doc_text_list) and s_i[1] + 1 not in index_set:  # 如果下一段没有加入
+            doc = doc_text_list[s_i[1] + 1]  # 获取下一段
+            if now_len + len(doc) > all_max_len:  # 如果超过了最大长度，就不再加入
                 break
-            index_set.add(s_i[1] + 1)
-            now_len += len(doc)
+            index_set.add(s_i[1] + 1)  # 加入索引
+            now_len += len(doc)  # 更新当前长度
 
-    index_list = list(index_set)
-    index_list.sort()
-    for i in index_list:
-        sub_doc_list.append(doc_text_list[i])
-    document = '' if len(sub_doc_list) == 0 else '\n'.join(sub_doc_list)
+    index_list = list(index_set)  # 转换成list
+    index_list.sort()  # 排序
+    for i in index_list:  # 遍历索引
+        sub_doc_list.append(doc_text_list[i])  # 加入文档
+    document = '' if len(sub_doc_list) == 0 else '\n'.join(sub_doc_list)  # 拼接文档
     messages = [{
         "role": "system",
         "content": "你是一个有用的助手，可以使用文章内容准确地回答问题。使用提供的文章来生成你的答案，但避免逐字复制文章。尽可能使用自己的话。准确、有用、简洁、清晰。"
-    }, {"role": "system", "content": "文章内容：\n" + document}]
-    for his in history:
-        messages.append({"role": "user", "content": his[0]})
-        messages.append({"role": "assistant", "content": his[1]})
-    messages.append({"role": "user", "content": msg})
-    req_json = {'messages': messages, 'key': open_ai_key, 'model': "gpt-3.5-turbo"}
-    data = {"content": json.dumps(req_json)}
+    }, {"role": "system", "content": "文章内容：\n" + document}]  # 角色人物定义
+    for his in history:  # ���历历史记录
+        messages.append({"role": "user", "content": his[0]})  # 加入用户的历史记录
+        messages.append({"role": "assistant", "content": his[1]})  # 加入机器人的历史记录
+    messages.append({"role": "user", "content": msg})  # 加入用户的当前输入
+    req_json = {'messages': messages, 'key': open_ai_key, 'model': "gpt-3.5-turbo"}  # 请求json
+    data = {"content": json.dumps(req_json)}  # 请求data
     print('data:\n', req_json)
     result = requests.post(url=chat_url,
                            data=json.dumps(data),
                            headers=headers
-                           )
-    res = result.json()['content']
-    bot.append([msg, res])
-    return bot[max(0, len(bot) - 3):]
+                           )  # 请求
+    res = result.json()['content']  # 获取回复
+    bot.append([msg, res])  # 加入历史记录
+    return bot[max(0, len(bot) - 3):]  # 返回最近3轮的历史记录
 
 
-def up_file(files):
-    doc_text_list = []
-    for idx, file in enumerate(files):
+def up_file(files):  # 上传文件
+    doc_text_list = []  # 用于存储文档
+    for idx, file in enumerate(files):  # 遍历文件
         print(file.name)
-        with pdfplumber.open(file.name) as pdf:
-            for i in range(len(pdf.pages)):
+        with pdfplumber.open(file.name) as pdf:  # 打开pdf
+            for i in range(len(pdf.pages)):  # 遍历pdf的每一页
                 # 读取PDF文档第i+1页
                 page = pdf.pages[i]
-                res_list = page.extract_text().split('\n')[:-1]
-
-                for j in range(len(page.images)):
+                res_list = page.extract_text().split('\n')[:-1]  # 提取文本
+                for j in range(len(page.images)):  # 遍历图片
                     # 获取图片的二进制流
                     img = page.images[j]
-                    file_name = '{}-{}-{}.png'.format(str(time.time()), str(i), str(j))
-                    with open(file_name, mode='wb') as f:
+                    file_name = '{}-{}-{}.png'.format(str(time.time()), str(i), str(j))  # 生成文件名
+                    with open(file_name, mode='wb') as f:  # 保存图片
                         f.write(img['stream'].get_data())
                     try:
-                        res = ocr.ocr(file_name)
+                        res = ocr.ocr(file_name)  # 识别图片
                     except Exception as e:
-                        res = []
-                    if len(res) > 0:
-                        res_list.append(' '.join([re['text'] for re in res]))
+                        res = []  # 识别失败
+                    if len(res) > 0:  # 如果识别成功
+                        res_list.append(' '.join([re['text'] for re in res]))  # 加入识别结果
 
-                tables = page.extract_tables()
-                for table in tables:
+                tables = page.extract_tables()  # 提取表格
+                for table in tables:  # 遍历表格
                     # 第一列当成表头：
                     df = pd.DataFrame(table[1:], columns=table[0])
                     try:
-                        records = json.loads(df.to_json(orient="records", force_ascii=False))
-                        for rec in records:
-                            res_list.append(json.dumps(rec, ensure_ascii=False))
+                        records = json.loads(df.to_json(orient="records", force_ascii=False))  # 转换成json
+                        for rec in records:  # 遍历json
+                            res_list.append(json.dumps(rec, ensure_ascii=False))  # 加入json
                     except Exception as e:
-                        res_list.append(str(df))
-
-                doc_text_list += res_list
-    doc_text_list = [str(text).strip() for text in doc_text_list if len(str(text).strip()) > 0]
+                        res_list.append(str(df))  # 如果转换识别，直接把表格转为str
+                doc_text_list += res_list  # 加入文档
+    doc_text_list = [str(text).strip() for text in doc_text_list if len(str(text).strip()) > 0]  # 去除空格
     print(doc_text_list)
     return gr.Textbox.update(value='\n'.join(doc_text_list), visible=True), gr.Button.update(
         visible=True), gr.Markdown.update(
@@ -144,11 +142,11 @@ with gr.Blocks() as demo:
             chat_bot = gr.Chatbot(visible=False)  # 聊天机器人
             msg_txt = gr.Textbox(label='消息框', placeholder='输入消息，点击发送', visible=False)  # 消息框
             with gr.Row():
-                chat_bu = gr.Button(value='发送', visible=False)
+                chat_bu = gr.Button(value='发送', visible=False)  # 发送按钮
 
-    file.change(up_file, [file], [txt, doc_bu, md])
-    doc_bu.click(doc_emb, [txt], [doc_text_state, doc_emb_state, msg_txt, chat_bu, md, chat_bot])
-    chat_bu.click(get_response, [open_ai_key, msg_txt, chat_bot, doc_text_state, doc_emb_state], [chat_bot])
+    file.change(up_file, [file], [txt, doc_bu, md])  # 上传文件
+    doc_bu.click(doc_emb, [txt], [doc_text_state, doc_emb_state, msg_txt, chat_bu, md, chat_bot])  # 提交解析结果
+    chat_bu.click(get_response, [open_ai_key, msg_txt, chat_bot, doc_text_state, doc_emb_state], [chat_bot])  # 发送消息
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue().launch()