Update app.py

app.py

@@ -159,137 +159,41 @@ busi_chunks = text_to_chunks(busi_texts, start_page=start_page)
 
 
 
-#def struct_answer(openAI_key, engine, message, dataframe, user_question):
-#  
-#  def generate_text2(openAI_key, engine, message):
-#      openai.api_key = openAI_key
-#      
-#      completions = openai.ChatCompletion.create(
-#          model=engine,
-#          messages=message,
-#          max_tokens=512,
-#          n=1,
-#          stop=None,
-#          temperature=0.7,
-#      )
-#      message = completions.choices[0].message['content']
-#      return message
-#
-#  def filter_dataframe(openAI_key,engine, message,df=dataframe):
-#      out_state_year = generate_text2(openAI_key, engine, message)
-#      out_state_year = eval(out_state_year)
-#      state= [out_state_year[1]]
-#      year = [out_state_year[0]]
-#      tax_type = [out_state_year[2]]
-#      print(out_state_year)
-#      filter_df = df[(df['Year'].isin(year)) & (df['State'].isin(state)) & (df['SubSubCategory'].isin(tax_type))].reset_index().drop('index',axis=1)
-#      feeded_df = filter_df.loc[:,['Country','State','Govt_Type','Year','SubSubCategory',"Tax Amount",'Unit']]
-#      feeded_df = feeded_df.rename({'Govt_Type':'Government Type','SubSubCategory':'Type of Tax','Tax Amount':'Tax Amount Collected'},axis=1)
-#
-#      return feeded_df
-#
-#  feeded_df = filter_dataframe(openAI_key, engine, message)
-#  print(feeded_df.shape)
-#
-#  def dataframe_to_text(new_dataframe= feeded_df):
-#        new_dataframe.to_csv(r'nmdata.txt', index=None, sep='|', mode='a')
-#        text_file = open('nmdata.txt', 'r')
-#        lines = text_file.readlines()
-#        os.remove("nmdata.txt")
-#        return lines
-#
-#  def prompt_generation(x=dataframe_to_text(), y= user_question):
-#          prompt = ""
-#          for line in x:
-#            prompt+=line +'\n'
-#          prompt += '''Take above datapoint(s) as reference and answer the following question: {0}.\n Instructions while answering the question: 1. Do not generate any additional information on your own \n
-#          2. Do not do generate any number on your own. Just the number mentioned in the datapoint(s) \n
-#         3. Take your time to understand the question and than only answer the question \n
-#          4. Write answer in the form of sentence. '''.format(y)
-#          return prompt
-#
-#  def generate_text(openAI_key,prompt, engine="text-davinci-003"):
-#      openai.api_key = openAI_key
-#      completions = openai.Completion.create(
-#          engine=engine,
-#          prompt=prompt,
-#          max_tokens=512,
-#          n=1,
-#          stop=None,
-#          temperature=0
-#      )
-#      message = completions.choices[0].text
-#      return message
-#  
-#  prompt_ = prompt_generation()
-#  response_ = generate_text(openAI_key, prompt_)
-#  return response_
 
 def output_generation(typed_ques):
-    
-    type_of_query= classify_chatgpt(typed_ques, openAI_key)
-    global recommender
-    #print(type_of_query)
-    
-    if type_of_query in ['Individual Taxation Query','Individual Taxation Query.']:
+
+    type_of_query = 'dummy'
+
+    if typed_ques == "Hello" :
+        answe = "Do you have myGovID or wnat to continue as Guest."
+        return out_pu, type_of_query
         
-        recommender = SemanticSearch(data=indi_chunks,loaded_array = indi_loaded_array)
-        out_pu = question_answer(question= typed_ques, openAI_key= openAI_key)
-        #print(out_pu)
+    elif typed_ques == "I have myGovID":
+        answe = "Hi User, Please Enter Username"
         return out_pu, type_of_query
-    elif type_of_query in ['Business Taxation Query','Business Taxation Query.']:
-        recommender = SemanticSearch(data=busi_chunks, loaded_array=busi_loaded_array)
-        out_pu = question_answer(question= typed_ques, openAI_key= openAI_key)
-        #print(out_pu)
+        
+    elif typed_ques:
+        answe = "Hi "+ typed_ques + ", Please Enter Password" 
         return out_pu, type_of_query
-    #elif type_of_query in ['Data Related Query', 'Data Related Query.']:
-    #    
-    #    engine="gpt-3.5-turbo-0301"
-    #    message = [{'role': 'system', 'content': '''From the provided sentence extract out the year mentioned in the sentence, State and type of Tax for which user is asking the question about.
-    #
-    #                    Instructions:  
-    #                    1. Provide your output just a list in which first element is Year and second element is the State for example: [2018, "Queensland", "Land Tax"]
-    #                    2. If multiple states and year are mentioned than provide both as the result. for example: [[2018,2020], ["New South Wales","Queensland"], ["Land Tax","Gift Tax"]]
-    #                    3. Extract the exact name of the tax from the mentioned list of type of taxes below. For example, if in query motor tax is present but in the given below categories there is no category as 'motor tax' than classify into its most nearest category which has information about total tax present.
-    #                    4. Do not explain your answer. Just write the answer as mentioned in the first instruction.
-    #
-    #                    There are total 7 states in country Australia and they are as follows:
-    #                    1.  Northern Territory
-    #                    2.  Tasmania
-    #                    3.  South Australia
-    #                    4.  Western Australia
-    #                    5.  Queensland
-    #                    6.  Victoria
-    #                    7.  New South Wales
-    #
-    #                    Classify the State from above 7 names but in case, if in the sentence none of the states are mentioned than classify from the below special cases:
-    #                    1.  Give "Australia" as the result if nothing is mentioned at all.
-    #                    2.  Give "All States" as the result if the sentence is talking about all the states of Australia or all the state governments of Australia. 
-    #                    3.  Give "Australian Capital Territory" as the result if the sentence is talking about the Australian Capitals territory.
-    #
-    # 
-    #                    Also from the sentence classify the type of tax for which user is talking about from the below list of Tax Type:
-    #                    1. Taxes on employers payroll and labour force, 2. Land taxes, 3. Municipal rates, 4. Other taxes on property, 5. Total taxes on immovable property, 6. Estate, inheritance and gift taxes,
-    #                    7. Total taxes on property, 8. Excises and levies,  9. Taxes on government lotteries, 10. Taxes on private lotteries, 11. Taxes on gambling machines, 12. Casino taxes,
-    #                    13. Race and other sports betting taxes,  14. Other taxes on gambling,  15. Total taxes on gambling,  16. Insurance companies contributions to fire brigades,
-    #                    17. Third party insurance taxes,  18. Other taxes on insurance, 19. Total taxes on insurance, 20. Government borrowing guarantee levies,  21. Stamp duties on conveyances,
-    #                    22. Other taxes on financial and capital transactions, 23. Total taxes on financial and capital transactions, 24. Total taxes on the provision of goods and services,
-    #                    25. Stamp duty on vehicle registration, 26. Other motor vehicle taxes,  27. Total motor vehicle taxes, 28. Franchise taxes, 29. Other taxes on use of goods and performance of activities,
-    #                    30. Total taxes on use of goods and performance of activities, 31. Total Taxation Northern Territory State and Local Government, 32. Taxes received from public corporations,
-    #                    33. Taxes received from other levels of government, 34. Total Taxation Tasmania State and Local Government, 35. Total Taxation South Australia State and Local Government,
-    #                    36. Total Taxation Western Australia State and Local Government, 37. Total Taxation Queensland State and Local Government, 38. Total Taxation Victoria State and Local Government,
-    #                    39. Total Taxation New South Wales State and Local Government, 40. Total Taxation Australian Capital Territory State Government, 41. Personal income tax, 42. Government health insurance levy,
-    #                    43. Fringe benefits tax, 44. Other income tax levied on individuals, 45. Total income taxes levied on individuals, 46. Company income tax,  47. Income tax paid by superannuation funds,
-    #                    48. Other income tax levied on enterprises, 49. Total income taxes levied on enterprises, 50. Dividend withholding tax, 51. Interest withholding tax, 52. Other income taxes levied on non-residents,
-    #                    53. Total income taxes levied on non-residents, 54. Total taxes on income, 55. General taxes (sales taxes), 56. Goods and services tax (GST),
-    #                    57. Crude oil and LPG, 58. Other excises, 59. Agricultural production taxes, 60. Levies on statutory corporations, 61. Total excises and levies, 62. Taxes on international trade,
-    #                    63. Taxes on financial and capital transactions, 64. Taxes on the use of goods and performance of activities, 65. Total Taxation on Commonwealth Government'''},
-    #                                    {'role': 'user', 'content': typed_ques}]
-    #    output_ = struct_answer(openAI_key, engine, message, ndf, typed_ques).lstrip()
-    #    #print(output_)
-    #    return output_, type_of_query
-
 
+    else:
+    
+        type_of_query= classify_chatgpt(typed_ques, openAI_key)
+        global recommender
+        #print(type_of_query)
+        
+        if type_of_query in ['Individual Taxation Query','Individual Taxation Query.']:
+            
+            recommender = SemanticSearch(data=indi_chunks,loaded_array = indi_loaded_array)
+            out_pu = question_answer(question= typed_ques, openAI_key= openAI_key)
+            #print(out_pu)
+            return out_pu, type_of_query
+        elif type_of_query in ['Business Taxation Query','Business Taxation Query.']:
+            recommender = SemanticSearch(data=busi_chunks, loaded_array=busi_loaded_array)
+            out_pu = question_answer(question= typed_ques, openAI_key= openAI_key)
+            #print(out_pu)
+            return out_pu, type_of_query
+    
 
 title = 'CDI Citizen Intelligence 360 Tool: Tax Advisory'
 #description = """ Citizen Intelligence 360 sources data from various government agencies and makes it accessible to citizens in a user-friendly format. This can help citizens better understand how their local government works and stay informed about important initiatives and changes in their communities. Citizen Intelligence 360 is a citizen intelligence tool that uses mapping technology to provide citizens with access to government data, performance report and other civic information. It helps to increase transparency and accountability in local government, and empowers citizens to make informed decisions and participate in the democratic process."""
@@ -306,29 +210,14 @@ def gradio_chatbox(input,history):
     history = history or []
     
     typed_ques = input
-    
-
-    def output(answe):
-        ccc = "{0}".format(answe)
-        output = ccc
-        history.append((input,output))
-        #print(history)
-        return history, history
-
-    if typed_ques == "Hello" :
-        answe = "Do you have myGovID or wnat to continue as Guest."
-        output(answe)
-        
-    elif typed_ques == "I have myGovID":
-            answe = "Hi User, Please Enter Username"
-            output(answe)
-    else:
-        answe = "Hi "+ typed_ques + ", Please Enter Password" 
-        output(answe)
-                 
+                    
     answe, query_type = output_generation(typed_ques)
 
-    return output(answe)
+    ccc = "{0}".format(answe)
+    output = ccc
+    history.append((input,output))
+    #print(history)
+    return history, history