Update app.py

app.py

@@ -113,12 +113,13 @@ def extract_chapter(file_object):
 
 class Reviewer:
 
-    def __init__(self, api, api_base, paper_pdf, aspect, model_name, enable_rag):
+    def __init__(self, api, api_base, paper_pdf, aspect, model_name, limit_num, enable_rag):
         self.api = api
         self.api_base = api_base
         self.aspect = aspect
         self.paper_pdf = paper_pdf
         self.model_name = model_name
+        self.limit_num = int(limit_num)
         self.enable_rag = enable_rag
         # self.max_token_num = 50000
         # self.encoding = tiktoken.get_encoding("gpt2")
@@ -325,12 +326,12 @@ Organize the result in JSON format as follows:
 
         if self.enable_rag:
             messages=[
-                    {"role": "system", "content": f"Read the following content from several papers to gain knowledge in the relevant field. Using this knowledge, review a new scientific paper in this field. Based on existing research, identify the limitations of the 'Paper to Review'. Generate the major limitations related to its {self.aspect} in this paper. Do not include any limitation explicitly mentioned in the paper itself and return only the list of limitations. Return only the limitations in the following JSON format: {{\"limitations\": <a list of limitations>"} ,
+                    {"role": "system", "content": f"Read the following content from several papers to gain knowledge in the relevant field. Using this knowledge, review a new scientific paper in this field. Based on existing research, identify the limitations of the 'Paper to Review'. Generate {str(self.limit_num)} major limitations related to its {self.aspect} in this paper. Do not include any limitation explicitly mentioned in the paper itself. Return only the limitations in the following JSON format: {{\"limitations\": <a list of limitations>"} ,
                     {"role": "user", "content": text},
                 ]
         else:
             messages=[
-                    {"role": "system", "content": f"Read the following scientific paper and generate major limitations in this paper about its {self.aspect}. Do not include any limitation explicitly mentioned in the paper itself and return only the limitations. Return only the limitations in the following JSON format: {{\"limitations\": <a list of limitations>"} ,
+                    {"role": "system", "content": f"Read the following scientific paper and generate {str(self.limit_num)} major limitations in this paper about its {self.aspect}. Do not include any limitation explicitly mentioned in the paper itself. Return only the limitations in the following JSON format: {{\"limitations\": <a list of limitations>"} ,
                     {"role": "user", "content": text},
                 ]
         try:
@@ -345,7 +346,7 @@ Organize the result in JSON format as follows:
                 )
             )            
             try:
-                limitations = json.loads(responses[0])["limitations"]
+                limitations = json.loads(responses[0])["limitations"][:self.limit_num]
                 result = ""
                 limit_cnt = 1
                 for limitation in limitations:
@@ -367,7 +368,7 @@ Organize the result in JSON format as follows:
                         response_format={"type":"json_object"},
                     )
                 )  
-                limitations = json.loads(responses[0])["limitations"]
+                limitations = json.loads(responses[0])["limitations"][:self.limit_num]
                 result = ""
                 limit_cnt = 1
                 for limitation in limitations:
@@ -486,7 +487,7 @@ Organize the result in JSON format as follows:
                     break
         return extracted_text, title, abstract
 
-def main(api,api_base, paper_pdf, aspect, model_name, enable_rag):  
+def main(api,api_base, paper_pdf, aspect, model_name, limit_num, enable_rag):  
     start_time = time.time()
     # print("key: ", PRIVATE_API_KEY, "\nbase: ", PRIVATE_API_BASE)
     comments = ''
@@ -497,7 +498,7 @@ def main(api,api_base, paper_pdf, aspect, model_name, enable_rag):
         output2 =  "It looks like there's a missing API key or PDF input. Make sure you've provided the necessary information or uploaded the required file."
     else:
         try:
-            reviewer1 = Reviewer(api,api_base, paper_pdf, aspect, model_name, enable_rag)   
+            reviewer1 = Reviewer(api,api_base, paper_pdf, aspect, model_name, limit_num, enable_rag)   
             comments, retrieved_content = reviewer1.review_by_chatgpt(paper_list=paper_pdf)
             time_used = time.time() - start_time
             output2 ="Processing Time："+ str(round(time_used, 2)) +"seconds"
@@ -520,10 +521,10 @@ description = '''<div align='left'>
 </div>
 '''
 
-inp = [gradio.Textbox(label="Input your API-key",
+inp = [gradio.Textbox(label="Enter your API-key",
                           value="",
                           type='password'),
-       gradio.Textbox(label="Input the base URL (ending with /v1). Skip this step if using the original OpenAI API.",
+       gradio.Textbox(label="Enter the base URL (ending with /v1). Skip this step if using the original OpenAI API.",
                           value="https://api.openai.com/v1"),
 
        gradio.File(label="Upload the PDF file of your paper (Make sure the PDF is fully uploaded before clicking Submit)",type="binary"),
@@ -531,10 +532,11 @@ inp = [gradio.Textbox(label="Input your API-key",
                         value="Methodology",
                         label="Select the aspect"),
        gradio.Dropdown(["gpt-4o-mini","gpt-4o","Qwen/Qwen2.5-7B-Instruct-Turbo", "meta-llama/Meta-Llama-3-70B-Instruct-Turbo", "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo"], 
-                          label="Input the model name",
+                          label="Select the model name",
                           value="gpt-4o-mini"), 
-       gradio.Checkbox(label="Enable RAG", value=False)
-    
+       gradio.Textbox(label="Enter the number of limitations to generate.",
+                          value="3"),
+       gradio.Checkbox(label="Enable RAG", value=False),
 ]
 
 chat_reviewer_gui = gradio.Interface(fn=main,