import random
import spacy
import requests
from bs4 import BeautifulSoup
import re
import spacy
import language_tool_python
import streamlit as st
from gradio_client import Client

class SubjectiveTest:

    def __init__(self, data, noOfQues):
        self.summary = data
        self.noOfQues = noOfQues
        self.nlp = spacy.load("en_core_web_sm")

    def adjust_question_pattern(self, entity_label, topic_placeholder=True):
        question_patterns = {
            "PERSON": ["Who is {entity}?", "Tell me about {entity}", "Explain {entity}", "What do you know about {entity}"],
            "ORG": ["What is {entity}?", "Tell me about {entity}", "Explain {entity}", "What do you know about {entity}"],
            "GPE": ["Tell me about {entity}", "Explain {entity}", "What do you know about {entity}", "Describe {entity}", "Where is {entity}"],
            "MONEY": ["How much is {entity}?", "Tell me the value of {entity}", "Explain the amount of {entity}"],
            "DATE": ["Why was {entity} important?", "Explain what happened on {entity}"],
            # Add more entity-label to question-pattern mappings as needed
        }

        if topic_placeholder:
            for key in question_patterns:
                question_patterns[key] = [pattern + " {topic}" for pattern in question_patterns[key]]

        return question_patterns.get(entity_label, ["Explain {entity} {topic}"])

    def generate_test(self, topic=None):
        doc = self.nlp(self.summary)
        question_answer_dict = dict()

        for sentence in doc.sents:
            for ent in sentence.ents:
                entity_label = ent.label_
                entity_text = ent.text
                question_patterns = self.adjust_question_pattern(entity_label, topic is not None)
                for pattern in question_patterns:
                    question = pattern.format(entity=entity_text, topic=topic)
                    if entity_label in question_answer_dict:
                        question_answer_dict[entity_label].append(question)
                    else:
                        question_answer_dict[entity_label] = [question]

        questions = []

        for entity_label, entity_questions in question_answer_dict.items():
            entity_questions = entity_questions[:self.noOfQues]
            questions.extend(entity_questions)

        return questions

# Initialize LanguageTool
tool = language_tool_python.LanguageToolPublicAPI('en-US')

# Helper function to check grammar and sense
def grammar_sense(sentence):
    sense = tool.correct(sentence)
    grammar = "Correct Grammar" if not tool.check(sentence) else "Incorrect Grammar"
    return "Make Sense" if "Not" not in sense and grammar == "Correct Grammar" else "Not Make Sense"

Quiz_Gen = st.form("Quiz Generation")
res = Quiz_Gen.text_input("What topic do you want to get quizzed on?")
sub = Quiz_Gen.form_submit_button("Submit")

if sub:
    entity = res
    prefix = "https://wiki.kidzsearch.com/wiki/"
    page = requests.get(f'{prefix}{entity}')
    res = BeautifulSoup(page.content, 'html.parser')
        
    text = [i.get_text() for i in res.find_all('p')]
        
    cleaned_text = ' '.join(text)
    cleaned_text = re.sub(r'[^a-zA-Z0-9.,]', ' ', cleaned_text)
    paragraphs = [p.strip() for p in re.split(r'\n', cleaned_text) if p.strip()]
        
    # Process text using SpaCy
    nlp = spacy.load("en_core_web_sm")
    doc = nlp(cleaned_text)
        
    sentences = [sent.text for sent in doc.sents]
        
    # Combine sentences into paragraphs
    paragraphs = [f"{sentences[i]} {sentences[i + 1]}" if i + 1 < len(sentences) else sentences[i] for i in range(0, len(sentences), 2)]
    
    # Example usage
    data = ' '.join(paragraphs)
    noOfQues = 5
    st.toast("Creating Questions", icon='✅')
    subjective_generator = SubjectiveTest(data, noOfQues)
    question_list = subjective_generator.generate_test("")
    questions = []

    st.session_state.questions = question_list  # Store the generated questions in session state

Quiz = st.form("Quiz")
st.toast("Filtering Questions", icon='✅')

# Check if questions are generated in session state
if 'questions' in st.session_state:
    question_index = 0

    while question_index < len(st.session_state.questions):
        current_question = st.session_state.questions[question_index]

        # Check if the current question meets your criteria
        if "Explain" not in current_question and len(tool.check(current_question)) == 0 and grammar_sense(current_question) == "Make Sense":
            # Get user input for the current question
            user_answer = Quiz.text_input(f'{current_question}')

            # Append the user answer to the list
            ans.append(user_answer)

            # Move to the next question
            question_index += 1

submit_button = Quiz.form_submit_button("Submit")

if submit_button:
    st.toast("Calculating grade", icon='✅')
    with st.spinner(text="Calculating Grade"):
        for i, q in enumerate(st.session_state.questions):
            st.toast(f'iteration {i} has begun', icon='✅')
            result = client.predict(
                f'What would you rate this answer to the question: "{q}" as a percentage? Here is the answer: {ans[i]}. Your percentage grade cannot be negative or over 100%. Additionally, you should also assume that the user is of a 5-7th grade level of intellect.',
                0.9,
                256,
                0.9,
                1.2,
                api_name="/chat"
            )

            pattern = r'(\d+)%'

            match = re.search(pattern, result)
            if match:
                score = match.group(1)
                user_scores.append(int(score))
            else:
                user_scores.append(85)  # You can set a default score if no score is found

        # Calculate the average score using the user_scores list
        average_score = sum(user_scores) / len(user_scores)

        st.info(f'Your average score for the answers is {average_score}%')
        st.write(f'Your average score for the answers is {average_score}%')

        st.balloons()