Update app.py

app.py

@@ -1,46 +1,31 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer,BlenderbotForConditionalGeneration
-import torch
-chat_tkn = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-mdl = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-#chat_tkn = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-#mdl = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
-def converse(user_input, chat_history=[]):
-    user_input_ids = chat_tkn(user_input + chat_tkn.eos_token, return_tensors='pt').input_ids
-    # keep history in the tensor
-    bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1)
-    # get response
-    chat_history = mdl.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tkn.eos_token_id).tolist()
-    print (chat_history)
-    response = chat_tkn.decode(chat_history[0]).split("<|endoftext|>")
-    print("starting to print response")
-    print(response)
-    # html for display
-    html = "<div class='mybot'>"
-    for x, mesg in enumerate(response):
-        if x%2!=0 :
-           mesg="Alicia:"+mesg
-           clazz="alicia"
-        else :
-           clazz="user"
-        print("value of x")
-        print(x)
-        print("message")
-        print (mesg)
-        html += "<div class='mesg {}'> {}</div>".format(clazz, mesg)
-    html += "</div>"
-    print(html)
-    return html, chat_history
-import gradio as grad
-css = """
-.mychat {display:flex;flex-direction:column}
-.mesg {padding:5px;margin-bottom:5px;border-radius:5px;width:75%}
-.mesg.user {background-color:lightblue;color:white}
-.mesg.alicia {background-color:orange;color:white,align-self:self-end}
-.footer {display:none !important}
-"""
-text=grad.inputs.Textbox(placeholder="Lets chat")
-grad.Interface(fn=converse,
-             theme="default",
-             inputs=[text, "state"],
-             outputs=["html", "state"],
-             css=css).launch()
+import gradio as gr
+import 期刊论文投稿推荐系统
+textbox_keyword_inputs = gr.inputs.Textbox(lines=6, placeholder='关键词', default='', label=None, optional=False)
+textbox_abstract = gr.inputs.Textbox(lines=1, placeholder='请输入文章摘要', default='', label='摘要', optional=False)
+
+
+def greet(textbox_keyword_inputs, history=[]):
+    outputs = 期刊论文投稿推荐系统.extract_tags(textbox_keyword_inputs)
+    label = outputs[0]
+    values_1 = outputs[1]
+    history.append(textbox_keyword_inputs)
+    print([(f"{history[len(history)-1]}", f"{label[0]}")])
+    print('----------------------------------')
+    print(textbox_keyword_inputs)
+    # return [(f"{history[0]}",f"{gr.HTML('根据您的论文所推荐的期刊.html')}")], history
+    return [(f"{history[len(history)-1][:50]+'.....'}", f"{label[0]+'综合影响因子：'+str(values_1[0])}")], history
+
+
+with gr.Blocks() as Interface:
+    #   实例化一个机器人聊天组件
+    chatbot = gr.Chatbot()
+    #   状态码
+    state = gr.State([])
+    #   水平居中排列
+    with gr.Row():
+        #   实例化输入文本框
+        txt = gr.Textbox(show_label=False, placeholder="请输入文章摘要").style(container=False)
+    #   提交文本框后执行分析并输出
+    txt.submit(greet, [txt, state], [chatbot, state])
+if __name__ == "__main__":
+    Interface.launch()