Bugs fixed. Updated Readme.md

README.md

@@ -11,3 +11,42 @@ license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+## Instructions
+
+### Generate Results
+* There are mainly two way of using this demo: The Online way and the Offline way
+
+#### The Online way
+
+* You need to have the OpenAI's API key (DM me or email me if you can't find it) and the paper to be processed. 
+
+* Once you enter the API key and update the file, you should click `Submit`. 
+
+* Wait about 1-2 minute, you will be able to see the results on Output part.
+
+#### The Offline way
+
+* You can browser the generated result before head by ChatGPT without waiting just by clicking `Show Answers`
+
+### Correct Results
+
+* You need to judge whether the Generated Answer and Reference Correct.
+
+* If correct, you just need to click the `Correct` button. 
+
+* If incorrect, you need to first click the `Incorrect` button then provide the correct answer in the text box below.
+
+* You can click `Last Question` or `Next Question` in the Output area to change questions of a passage.
+
+* Or click `Last passage` or `Next passage` to change the passages.
+
+### Download Results
+
+* Onece finish judging all questions of all passage. You can download the answers and judgements
+
+* If you just want to download the answer generated by LLMs, you can click `Download original LLM answers` then click on the right side to download the excel file.
+
+* If you want to download the answer and the judgements, you can click `Download corrected LLM answers` then click on the right side to download the excel file.
+
+#### If you have further questions, please email me: oliverwang15@outlook.com

app.py

@@ -7,8 +7,6 @@ import pandas as pd
 
 from backend import Backend_offline
 
-
-
 QUESTIONS = [
     "Animal Type",
     "Exposure Age",
@@ -20,8 +18,13 @@ QUESTIONS = [
 
 with gr.Blocks(theme="dark") as demo:
     backend = Backend_offline()
+    with gr.Row():
+            gr.Markdown(f'<center> <h1> <b> DAN_AI </b> </h1> </center>\
+                        <center> <h4> Please follow the Instruction <a href="https://huggingface.co/spaces/oliverwang15/DAN_AI/blob/main/README.md">HERE</a> </h4> </center>')
+    
     with gr.Row():
         with gr.Row():
+            
             # Update
             with gr.Group():
                 gr.Markdown(f'<center><h1>Input</h1></center>')
@@ -93,8 +96,7 @@ with gr.Blocks(theme="dark") as demo:
                 corrected_file = gr.File(label='Download corrected data', file_types=['.xlsx'])
                 btn_download_corrected = gr.Button(value='Download corrected LLM answers')
                 # btn_download_corrected.style(full_width=True)
-            
-        
+                   
     with gr.Row():
         highlighted_text = gr.HTML(label="Highlighted Text")
         # reset = gr.Button(value='Reset')

backend.py

@@ -269,7 +269,10 @@ class Backend:
         os.makedirs(path, exist_ok = True)
         path = os.path.join(path, name)
         # self.ori_answer_df['questions'] = self.questions
-        self.ori_answer_df.to_excel(path, index = False)
+        if not hasattr(self, 'ori_answer_df'):
+            raise gr.Error("You need to submit the document first")
+        else:
+            self.ori_answer_df.to_excel(path, index = False)
 
         return path
     
@@ -277,7 +280,10 @@ class Backend:
         os.makedirs(path, exist_ok = True)
         path = os.path.join(path, name)
         # self.answer_df['questions'] = self.questions
-        self.answer_df.to_excel(path, index = False)
+        if not hasattr(self, 'answer_df'):
+            raise gr.Error("You need to submit the document first")
+        else:
+            self.answer_df.to_excel(path, index = False)
 
         return path
     
@@ -302,9 +308,7 @@ class Backend:
                 return self.highlighted_out
             else:
                 return "No answer yet, you need to submit the document first"
-            
 
-class Backend_offline(Backend):
     def phase_df(self, df):
         df = json.loads(df.T.to_json())
         res_list = []
@@ -397,4 +401,4 @@ class Backend_offline(Backend):
 
         file_name = self.filename_list[self.current_passage]
         
-        return file_name, question, self.answer, self.highlighted_out, highlighted_out_html, self.answer, self.highlighted_out
+        return file_name, question, self.answer, self.highlighted_out, highlighted_out_html, self.answer, self.highlighted_out