app.py

import os
import guidance
import json
import gradio as gr 
# import googleapiclient.discovery
import requests
from bs4 import BeautifulSoup
import re
from requests.exceptions import RequestException
from dotenv import load_dotenv

load_dotenv()
# my_cse_id = "02dc218e5cd174d7e"
# my_cse_engine = googleapiclient.discovery.build("customsearch", "v1", developerKey="AIzaSyAuktiM0lPtA2gqr4WJdULB3PrsblDejZY")
# # os.environ['OPENAI_API_KEY'] = os.environ.get('OPEN_API_KEY')
# def google_search(search_term):
#   res = my_cse_engine.cse().list(
#     q=search_term,
#     cx=my_cse_id,
#     num=6,
#   ).execute()
#   return res['items']

# results = google_search("Explain about Java Programming")
# def gen_ans(link, timeout=5):
#     try:
#         response = requests.get(link, timeout=timeout)
#         response.raise_for_status()  # Raise an exception for non-successful responses
#         soup = BeautifulSoup(response.content, 'html.parser')
#         answer = soup.find_all('p')
#         cut_ans = str(answer)
#         return cut_ans
#     except (RequestException, requests.Timeout, requests.ConnectionError) as e:
#         print(f"An error occurred while processing the link: {link}")
#         print(f"Error details: {str(e)}")
#         return "time out for extraction"
# def clean(text):
#         # makes text devoid of tags and encoded characters
#         # uses BeautifulSoup4 and Regular Expression
#         soup = BeautifulSoup(text, "html.parser").get_text()
#         encoded_bytes = soup.encode('utf-8')
#         cleaned_text = encoded_bytes.decode('unicode_escape', 'ignore')
#         return (re.sub(r'\W\s+',' ',soup)).strip('][')
# def top_10_ans(ques):
#         results = google_search(ques)
#         answers = []
#         k=0
#         for i in results: 
#             l=gen_ans(i['link'])
#             if l!='time out for extraction':
#               answers.append(clean(l))
#               k+=1
#             if k==3:
#               break
#         return answers
# def summarize(question,l):
#   model = guidance.llms.OpenAI('gpt-3.5-turbo')
  
#   system = guidance('''
#       {{#system~}}
#           You are a text-summarizer Expert System. You are given with multiple long paragraphs of text data and a question based on the given data.
#           You are required to summarize the content all user input long text paragraphs to a short essay.
#           The essay must have the complete information of all the input text paragraphs.
#       {{~/system}}
#       {{#user~}}
#           Now, I'll send multiple long text paragraphs, question based on the given paragraph's to you and you need to generate a summary of all the paragraphs to a
#           single essay of 190 to 300 words and the complete intent in my paragraphs must be reflected in the final generated summary, based on the question given.
#       {{~/user}}
#       {{#user~}}
#           {{question}}
#       {{~/user}}
#       {{#user~}}
#           {{para1}}
#       {{~/user}}
#       {{#user~}}
#           {{para2}}
#       {{~/user}}
#       {{#user~}}
#           {{para3}}
#       {{~/user}}
#       {{#user~}}
#           Now, based on the past five input paragraphic texts and question given, generate a summary essay of 190 to 300 words enveloping the complete intent
#           of the given paragraphs.
#       {{~/user}}
#       {{#assistant~}}
#       {{gen 'summary' temperature=0.5 max_tokens=1000}}
#       {{~/assistant}}
#   ''', llm = model)
#   output = system(question=question,para1=str(l[0][:2000]), para2=str(l[1][:2000]), para3=str(l[2][:2000]))
#   return output.get('summary')
def guideus(question, answer, essay):
   
    gptThreeFive = guidance.llms.OpenAI('gpt-3.5-turbo')
    mySys = guidance('''
        {{#system~}}
        You are an Interviewee answer grading System. You have highest precision in grading the answer. Grade the answers given by an interviewee.        
        Scenario : 
        1. The Interviewee is given the question to read for 3 minutes. 
        2. They were later provided with listening conceptual essay. He is allowed to take notes on the comphrehension. 
        3. For the next 20 minutes, they are instructed to complete their answer based on their notes and the question asked.
        4. The response is then graded according to appropriate graded measures and pattern scoring.
        {{~/system}}
        {{#user~}}
        Your task now is to assess an interviewee's response.
        The question provided to the interviewee is as follows :

        {{question}}

        Now consider the following grading measures and their definitions.
        Grading Measures:

            Clear Introduction: The answer should have a clear introduction that introduces the topic intended in the question.

            Coherent Body Paragraphs: The answer should have well-developed body paragraphs that support the question with relevant examples, details, and explanations.

            Logical Organization: The answer should demonstrate a logical progression of ideas and use appropriate transitions between paragraphs and sentences.\

            Cohesion: The answer should have coherence in terms of ideas, with connections between sentences and paragraphs clearly established.

            Grammar and Sentence Structure: The answer should demonstrate a good command of grammar, with accurate sentence structures and appropriate use of verb tenses, pronouns, and modifiers.

            Vocabulary: The answer should use a wide range of vocabulary appropriately and effectively to convey meaning and enhance the quality of the writing.

            Idiomatic Language: The answer should demonstrate the ability to use idiomatic expressions and phrases in a natural and accurate way.

            Word Choice: The answer should show precision and accuracy in word choice, avoiding repetition and using words that convey the intended meaning.

            Relevance to the Topic: The answer should address the given topic directly and provide a focused and relevant response.

            Content Accuracy: The answer should demonstrate a good understanding of the topic and present accurate information and examples.

            Use of Examples and Details: The answer should provide specific examples and details to support ideas and arguments effectively.

            Critical Thinking: The answer should show the candidate's ability to analyze and evaluate the topic, presenting a well-reasoned argument or position.
        
        The above are the available grading measures. 
        """Now, you need to pick/generate the suitable subset of above grading measures for the given question."""
        {{~/user}}
        {{#assistant~}}
        {{gen 'grading_measures' temperature=0.7 max_tokens=100}}
        {{~/assistant}}
        {{#user~}}
        Consider the following inputs, to perform the task.

        The answer he had given to the questions is as follows : 

        """{{answer}}"""
        if the above ans is empty then  evaluate to zero in all blocks of grading measures 

        The candidate's answer is to be evaluated as per below Conceptual Essay : 

        {{essay}}
        
        {{~/user}}
        {{#user~}}
        Now consider the pattern of scoring as follows : 
        """The evaluation score is awared 5 when the answer contains important information of the conceptual is found in it.
        The evaluation score is awared 4 when the answer has the important information but with minor omissions, inaccuracy, vagueness, imprecision and language errors.
        The evaluation score is awared 3 when the answer conveys only a global, unclear and inaccurate information. It is also done when key points 
        of the conceptual essay are not found in the interviewee's answer. Frequent Grammatical Usage Errors are also seen.
        The evaluation score is given 2 when the answer is relevant to the conceptual essay but has major omission, inaccuracy and language difficulties.
        The evaluation score is presented with 1 if the answer has little to no meaning or relevant information from the conceptual essay. The language sophistication is too low to comphrehend.
        The evaluation score is determined to be 0 if the answer is a copy from the conceptual essay or has no relevance to the question or written in Non-English Language or is left as blank."""
        {{~/user}}
        {{#user~}}
        Consider the following example of grading.
        The answer provided : 
        And lastly, paragraph says that everyone feels responsible for their own part, and all together they
        are all more effective as a team. In the lecture, doubt was expressed concerning the advantages of
        the recent trend of forming teams to tackle projects, which was mentioned in the reading.
        to begin with, the lecturer agrues that although a group tends to have a greater resource of skills
        and expertises, these resources may not necessarily be effefectively used. according to a recent
        company project, it was found that one or two members dominated over the whole group, when
        the dominant members asserted or banned an idea, most of the other group members would
        follow their ideas and “suppress” the other ideas that were suggested, even if the other ideas were
        more creative and innovative.
        secondly, it was proved that, on the contrary of the reading, progress in the project was very slow.
        this was the result of long debates over reaching a compromise as ideas were diverted and
        consenus took a lenghty period of time.
        thirdly, as a group would be credited collectively, quite a number of unfair situations appeared. in
        the group. it was found that some members did not work hard at all and got a “free ride”.
        however, those worked harder were not rewarded for their extra efforts as their individual efforts
        would not be recognized.
        concluding, via the results of a recent company that adopted the “group method” of tackling
        projects, the lecturer projected doubts that contradicted with the central standpoint of the reading.
        the lecturer believes that skills and expertise cannot be maximized in a group, progress is slow 
        and the overall results of the team is not a fair assessment of the individual members of the group-
        which contradicts with the central standpoint of the reading.
        Grade Evaluated : 5
        {{~/user}}
        {{#user~}}
        Now, based on the given values of question, answer and the Conceptual Essay,
        perform the Grading Evaluation on the Answer text based on generated grading Measures.
        The answer evaluation must totally depend on the conceptual essay and pattern of scoring provided above.
        {{~/user}}
        {{#user~}}
        Generate the Evaulation in a JSON Format. The JSON must consist of Grading Measure as the key and a dictionary of two key-value pairs. 
        The dict must have the first pair with key as 'numerical_score' and value as grade out of 5. The other element must have 'reason' as the key and value as a string containing reason to why setting such grade.
        The final JSON must also a Key Value of 'Overall Score' and dictionary as value with 'numerical_score' key having the mean value of all grading measure numerical scores.
        {{~/user}}
        {{#assistant~}}
        {{gen 'evaluation' temperature=0.5 max_tokens=1000}}
        {{~/assistant}}
    ''', llm=gptThreeFive)
    text = mySys(question= question, answer = answer, essay = essay)
    text=text['evaluation']


    start_index = text.find('{')
    end_index = text.rfind('}') + 1
    json_part = text[start_index:end_index]


    data = json.loads(json_part)
    # final = json.loads(output['evaluation'])
    # for i in final.keys():
    #     print("The Rubric : ",i,"\n\tThe Numerical Score Assigned : ", final[i]['numerical_score'],"\n\tThe Justification : ", final[i]['reason'])
    return data
def k(question, answer, essay):
    if  question=='' or answer=='' or essay=='':
        return {'error':'fill all the  required fields'}
    # l=top_10_ans(question)
    return guideus(question,answer,essay)
# css = """
# body {
#   background-color: #F2F2F2;
#   font-family: Arial, sans-serif;
# }

# .container {
#   padding: 20px;
#   border: 1px solid #DDD;
#   border-radius: 5px;
#   background-color: #FFFFFF;
# }

# h1 {
#   font-size: 24px;
#   color: #333333;
#   margin-bottom: 20px;
# }

# .input-container {
#   margin-bottom: 20px;
# }

# input[type="text"] {
#   width: 100%;
#   padding: 10px;
#   border: 1px solid #CCCCCC;
#   border-radius: 5px;
# }

# .button-container {
#   text-align: center;
# }

# button {
#   background-color: #4CAF50;
#   color: white;
#   padding: 10px 20px;
#   border: none;
#   border-radius: 5px;
#   cursor: pointer;
# }

# button:hover {
#   background-color: #45a049;
# }

# .output-container {
#   margin-top: 20px;
# }

# .result {
#   background-color: #E9E9E9;
#   padding: 10px;
#   border-radius: 5px;
# }
# """
textbox = gr.inputs.Textbox(label="Question")
textbox1 = gr.inputs.Textbox(label="Answer")
textbox2 = gr.inputs.Textbox(label="Essay")
inputs = [
    textbox,textbox1,textbox2
]



gr.Interface(fn=k, inputs=inputs, outputs=['json']).launch()