import streamlit as st
from transformers import T5ForConditionalGeneration, T5Tokenizer
import spacy
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from rake_nltk import Rake
import pandas as pd
from fpdf import FPDF
import wikipediaapi
from functools import lru_cache
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('brown')
from nltk.tokenize import sent_tokenize
nltk.download('wordnet')
from nltk.corpus import wordnet
import random
import sense2vec
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import json
import os
from sentence_transformers import SentenceTransformer, util
import textstat
from spellchecker import SpellChecker
from transformers import pipeline
import re
import pymupdf
import uuid
import time
import asyncio
import aiohttp
from datetime import datetime
import base64
from io import BytesIO
# '-----------------'
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText 
from email.mime.base import MIMEBase
from email.mime.application import MIMEApplication
from email import encoders
# '------------------'
print("***************************************************************")

st.set_page_config(
    page_icon='cyclone',
    page_title="Question Generator",
    initial_sidebar_state="auto",
    menu_items={
        "About" : "Hi this our project."
    }
)

st.set_option('deprecation.showPyplotGlobalUse',False)

class QuestionGenerationError(Exception):
    """Custom exception for question generation errors."""
    pass


# Initialize Wikipedia API with a user agent
user_agent = 'QGen/1.2'
wiki_wiki = wikipediaapi.Wikipedia(user_agent= user_agent,language='en')

def get_session_id():
    if 'session_id' not in st.session_state:
        st.session_state.session_id = str(uuid.uuid4())
    return st.session_state.session_id

def initialize_state(session_id):
    if 'session_states' not in st.session_state:
        st.session_state.session_states = {}

    if session_id not in st.session_state.session_states:
        st.session_state.session_states[session_id] = {
            'generated_questions': [],
            # add other state variables as needed
        }
    return st.session_state.session_states[session_id]

def get_state(session_id):
    return st.session_state.session_states[session_id]

def set_state(session_id, key, value):
    st.session_state.session_states[session_id][key] = value


@st.cache_resource
def load_model(modelname):
    model_name = modelname
    model = T5ForConditionalGeneration.from_pretrained(model_name)
    tokenizer = T5Tokenizer.from_pretrained(model_name)
    return model, tokenizer

# Load Spacy Model
@st.cache_resource
def load_nlp_models():
    nlp = spacy.load("en_core_web_md")
    s2v = sense2vec.Sense2Vec().from_disk('s2v_old')
    return nlp, s2v

# Load Quality Assurance Models
@st.cache_resource
def load_qa_models():
    # Initialize BERT model for sentence similarity
    similarity_model = SentenceTransformer('all-MiniLM-L6-v2')

    spell = SpellChecker()
    return similarity_model, spell

with st.sidebar:
    select_model = st.selectbox("Select Model", ("T5-large","T5-small"))
if select_model == "T5-large":
    modelname = "DevBM/t5-large-squad"
elif select_model == "T5-small":
    modelname = "AneriThakkar/flan-t5-small-finetuned"
nlp, s2v = load_nlp_models()
similarity_model, spell = load_qa_models()
context_model = similarity_model
model, tokenizer = load_model(modelname)


# Info Section
def display_info():
    st.sidebar.title("Information")
    st.sidebar.markdown("""
        ### Question Generator System
        This system is designed to generate questions based on the provided context. It uses various NLP techniques and models to:
        - Extract keywords from the text
        - Map keywords to sentences
        - Generate questions
        - Provide multiple choice options
        - Assess the quality of generated questions

        #### Key Features:
        - **Keyword Extraction:** Combines RAKE, TF-IDF, and spaCy for comprehensive keyword extraction.
        - **Question Generation:** Utilizes a pre-trained T5 model for generating questions.
        - **Options Generation:** Creates contextually relevant multiple-choice options.
        - **Question Assessment:** Scores questions based on relevance, complexity, and spelling correctness.
        - **Feedback Collection:** Allows users to rate the generated questions and provides statistics on feedback.

        #### Customization Options:
        - Number of beams for question generation
        - Context window size for mapping keywords to sentences
        - Number of questions to generate
        - Additional display elements (context, answer, options, entity link, QA scores)

        #### Outputs:
        - Generated questions with multiple-choice options
        - Download options for CSV and PDF formats
        - Visualization of overall scores

    """)

def get_pdf_text(pdf_file):
    doc = pymupdf.open(stream=pdf_file.read(), filetype="pdf")
    text = ""
    for page_num in range(doc.page_count):
        page = doc.load_page(page_num)
        text += page.get_text()
    return text

def save_feedback_og(question, answer, rating, options, context):
    feedback_file = 'question_feedback.json'
    if os.path.exists(feedback_file):
        with open(feedback_file, 'r') as f:
            feedback_data = json.load(f)
    else:
        feedback_data = []
    tpl = {
        'question' : question,
        'answer' : answer,
        'context' : context,
        'options' : options,
        'rating' : rating,
    }
    # feedback_data[question] = rating
    feedback_data.append(tpl)
    print(feedback_data)
    with open(feedback_file, 'w') as f:
        json.dump(feedback_data, f)
    
    return feedback_file

# -----------------------------------------------------------------------------------------
def send_email_with_attachment(email_subject, email_body, recipient_emails, sender_email, sender_password, attachment):
    smtp_server = "smtp.gmail.com"  # Replace with your SMTP server
    smtp_port = 587  # Replace with your SMTP port

    # Create the email message
    message = MIMEMultipart()
    message['From'] = sender_email
    message['To'] = ", ".join(recipient_emails) 
    message['Subject'] = email_subject
    message.attach(MIMEText(email_body, 'plain'))

    # Attach the feedback data if available
    if attachment:
        attachment_part = MIMEApplication(attachment.getvalue(), Name="feedback_data.json")
        attachment_part['Content-Disposition'] = f'attachment; filename="feedback_data.json"'
        message.attach(attachment_part)

    # Send the email
    try:
        with smtplib.SMTP(smtp_server, smtp_port) as server:
            server.starttls()
            print(sender_email)
            print(sender_password)
            server.login(sender_email, sender_password)
            text = message.as_string()
            server.sendmail(sender_email, recipient_emails, text)
        return True
    except Exception as e:
        st.error(f"Failed to send email: {str(e)}")
        return False
# ----------------------------------------------------------------------------------

def collect_feedback(i,question, answer, context, options):
    st.write("Please provide feedback for this question:")
    edited_question = st.text_input("Enter improved question",value=question,key=f'fdx1{i}')
    clarity = st.slider("Clarity", 1, 5, 3, help="1 = Very unclear, 5 = Very clear",key=f'fdx2{i}')
    difficulty = st.slider("Difficulty", 1, 5, 3, help="1 = Very easy, 5 = Very difficult",key=f'fdx3{i}')
    relevance = st.slider("Relevance", 1, 5, 3, help="1 = Not relevant, 5 = Highly relevant",key=f'fdx4{i}')
    option_quality = st.slider("Quality of Options", 1, 5, 3, help="1 = Poor options, 5 = Excellent options",key=f'fdx5{i}')
    overall_rating = st.slider("Overall Rating", 1, 5, 3, help="1 = Poor, 5 = Excellent",key=f'fdx6{i}')
    comments = st.text_input("Additional Comments", "",key=f'fdx7{i}')

    if st.button("Submit Feedback",key=f'fdx8{i}'):
        feedback = {
            "question": question,
            'edited_question':edited_question,
            "answer": answer,
            "options": options,
            "clarity": clarity,
            "difficulty": difficulty,
            "relevance": relevance,
            "option_quality": option_quality,
            "overall_rating": overall_rating,
            "comments": comments
        }
        save_feedback(feedback)
        st.success("Thank you for your feedback!")

def save_feedback(feedback):
    st.session_state.feedback_data.append(feedback)

def analyze_feedback():
    if not st.session_state.feedback_data:
        st.warning("No feedback data available yet.")
        return

    df = pd.DataFrame(st.session_state.feedback_data)
    
    st.write("Feedback Analysis")
    st.write(f"Total feedback collected: {len(df)}")
    
    metrics = ['clarity', 'difficulty', 'relevance', 'option_quality', 'overall_rating']
    
    for metric in metrics:
        fig, ax = plt.subplots()
        df[metric].value_counts().sort_index().plot(kind='bar', ax=ax)
        plt.title(f"Distribution of {metric.capitalize()} Ratings")
        plt.xlabel("Rating")
        plt.ylabel("Count")
        st.pyplot(fig)

    st.write("Average Ratings:")
    st.write(df[metrics].mean())

    # Word cloud of comments
    comments = " ".join(df['comments'])
    if len(comments) > 1:
        wordcloud = WordCloud(width=800, height=400, background_color='white').generate(comments)
        fig, ax = plt.subplots()
        plt.imshow(wordcloud, interpolation='bilinear')
        plt.axis("off")
        st.pyplot(fig)


def export_feedback_data():
    if not st.session_state.feedback_data:
        st.warning("No feedback data available.")
        return None

    # Convert feedback data to JSON
    json_data = json.dumps(st.session_state.feedback_data, indent=2)
    
    # Create a BytesIO object
    buffer = BytesIO()
    buffer.write(json_data.encode())
    buffer.seek(0)
    
    return buffer

# Function to clean text
def clean_text(text):
    text = re.sub(r"[^\x00-\x7F]", " ", text)
    text = re.sub(f"[\n]"," ", text)
    return text

# Function to create text chunks
def segment_text(text, max_segment_length=700, batch_size=7):
    sentences = sent_tokenize(text)
    segments = []
    current_segment = ""
    
    for sentence in sentences:
        if len(current_segment) + len(sentence) <= max_segment_length:
            current_segment += sentence + " "
        else:
            segments.append(current_segment.strip())
            current_segment = sentence + " "
    
    if current_segment:
        segments.append(current_segment.strip())
    
    # Create batches
    batches = [segments[i:i + batch_size] for i in range(0, len(segments), batch_size)]
    return batches


# Function to extract keywords using combined techniques
def extract_keywords(text, extract_all):
    try:
        doc = nlp(text)
        spacy_keywords = set([ent.text for ent in doc.ents])
        spacy_entities = spacy_keywords
        print(f"\n\nSpacy Entities: {spacy_entities} \n\n")  

        # Use Only Spacy Entities
        if extract_all is False:
            return list(spacy_entities) 
        
        # Use RAKE
        rake = Rake()
        rake.extract_keywords_from_text(text)
        rake_keywords = set(rake.get_ranked_phrases())
        print(f"\n\nRake Keywords: {rake_keywords} \n\n")
        # Use spaCy for NER and POS tagging
        spacy_keywords.update([token.text for token in doc if token.pos_ in ["NOUN", "PROPN", "VERB", "ADJ"]])
        print(f"\n\nSpacy Keywords: {spacy_keywords} \n\n")
        # Use TF-IDF
        vectorizer = TfidfVectorizer(stop_words='english')
        X = vectorizer.fit_transform([text])
        tfidf_keywords = set(vectorizer.get_feature_names_out())
        print(f"\n\nTFIDF Entities: {tfidf_keywords} \n\n")

        # Combine all keywords
        combined_keywords = rake_keywords.union(spacy_keywords).union(tfidf_keywords)
        
        return list(combined_keywords)
    except Exception as e:
        raise QuestionGenerationError(f"Error in keyword extraction: {str(e)}")

def get_similar_words_sense2vec(word, n=3):
    # Try to find the word with its most likely part-of-speech
    word_with_pos = word + "|NOUN"
    if word_with_pos in s2v:
        similar_words = s2v.most_similar(word_with_pos, n=n)
        return [word.split("|")[0] for word, _ in similar_words]
    
    # If not found, try without POS
    if word in s2v:
        similar_words = s2v.most_similar(word, n=n)
        return [word.split("|")[0] for word, _ in similar_words]
    
    return []

def get_synonyms(word, n=3):
    synonyms = []
    for syn in wordnet.synsets(word):
        for lemma in syn.lemmas():
            if lemma.name() != word and lemma.name() not in synonyms:
                synonyms.append(lemma.name())
                if len(synonyms) == n:
                    return synonyms
    return synonyms

def generate_options(answer, context, n=3):
    options = [answer]
    
    # Add contextually relevant words using a pre-trained model
    context_embedding = context_model.encode(context)
    answer_embedding = context_model.encode(answer)
    context_words = [token.text for token in nlp(context) if token.is_alpha and token.text.lower() != answer.lower()]

    # Compute similarity scores and sort context words
    similarity_scores = [util.pytorch_cos_sim(context_model.encode(word), answer_embedding).item() for word in context_words]
    sorted_context_words = [word for _, word in sorted(zip(similarity_scores, context_words), reverse=True)]
    options.extend(sorted_context_words[:n])

    # Try to get similar words based on sense2vec
    similar_words = get_similar_words_sense2vec(answer, n)
    options.extend(similar_words)
    
    # If we don't have enough options, try synonyms
    if len(options) < n + 1:
        synonyms = get_synonyms(answer, n - len(options) + 1)
        options.extend(synonyms)
    
    # If we still don't have enough options, extract other entities from the context
    if len(options) < n + 1:
        doc = nlp(context)
        entities = [ent.text for ent in doc.ents if ent.text.lower() != answer.lower()]
        options.extend(entities[:n - len(options) + 1])
    
    # If we still need more options, add some random words from the context
    if len(options) < n + 1:
        context_words = [token.text for token in nlp(context) if token.is_alpha and token.text.lower() != answer.lower()]
        options.extend(random.sample(context_words, min(n - len(options) + 1, len(context_words))))
    print(f"\n\nAll Possible Options: {options}\n\n")    
    # Ensure we have the correct number of unique options
    options = list(dict.fromkeys(options))[:n+1]
    
    # Shuffle the options
    random.shuffle(options)
    
    return options

# Function to map keywords to sentences with customizable context window size
def map_keywords_to_sentences(text, keywords, context_window_size):
    sentences = sent_tokenize(text)
    keyword_sentence_mapping = {}
    print(f"\n\nSentences: {sentences}\n\n")
    for keyword in keywords:
        for i, sentence in enumerate(sentences):
            if keyword in sentence:
                # Combine current sentence with surrounding sentences for context
                start = max(0, i - context_window_size)
                end = min(len(sentences), i + context_window_size + 1)
                context = ' '.join(sentences[start:end])
                if keyword not in keyword_sentence_mapping:
                    keyword_sentence_mapping[keyword] = context
                else:
                    keyword_sentence_mapping[keyword] += ' ' + context
    return keyword_sentence_mapping


# Function to perform entity linking using Wikipedia API
@lru_cache(maxsize=128)
def entity_linking(keyword):
    page = wiki_wiki.page(keyword)
    if page.exists():
        return page.fullurl
    return None

async def generate_question_async(context, answer, num_beams):
    try:
        input_text = f"<context> {context} <answer> {answer}"
        print(f"\n{input_text}\n")
        input_ids = tokenizer.encode(input_text, return_tensors='pt')
        outputs = await asyncio.to_thread(model.generate, input_ids, num_beams=num_beams, early_stopping=True, max_length=250)
        question = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(f"\n{question}\n")
        return question
    except Exception as e:
        raise QuestionGenerationError(f"Error in question generation: {str(e)}")

async def generate_options_async(answer, context, n=3):
    try:
        options = [answer]
        
        # Add contextually relevant words using a pre-trained model
        context_embedding = await asyncio.to_thread(context_model.encode, context)
        answer_embedding = await asyncio.to_thread(context_model.encode, answer)
        context_words = [token.text for token in nlp(context) if token.is_alpha and token.text.lower() != answer.lower()]

        # Compute similarity scores and sort context words
        similarity_scores = [util.pytorch_cos_sim(await asyncio.to_thread(context_model.encode, word), answer_embedding).item() for word in context_words]
        sorted_context_words = [word for _, word in sorted(zip(similarity_scores, context_words), reverse=True)]
        options.extend(sorted_context_words[:n])

        # Try to get similar words based on sense2vec
        similar_words = await asyncio.to_thread(get_similar_words_sense2vec, answer, n)
        options.extend(similar_words)
        
        # If we don't have enough options, try synonyms
        if len(options) < n + 1:
            synonyms = await asyncio.to_thread(get_synonyms, answer, n - len(options) + 1)
            options.extend(synonyms)
        
        # Ensure we have the correct number of unique options
        options = list(dict.fromkeys(options))[:n+1]
        
        # Shuffle the options
        random.shuffle(options)
        
        return options
    except Exception as e:
        raise QuestionGenerationError(f"Error in generating options: {str(e)}")


# Function to generate questions using beam search
async def generate_questions_async(text, num_questions, context_window_size, num_beams, extract_all_keywords):
    try:
        batches = segment_text(text)
        keywords = extract_keywords(text, extract_all_keywords)
        all_questions = []
        
        progress_bar = st.progress(0)
        status_text = st.empty()

        for i, batch in enumerate(batches):
            status_text.text(f"Processing batch {i+1} of {len(batches)}...")
            batch_questions = await process_batch(batch, keywords, context_window_size, num_beams)
            all_questions.extend(batch_questions)
            progress_bar.progress((i + 1) / len(batches))
            
            if len(all_questions) >= num_questions:
                break
        
        progress_bar.empty()
        status_text.empty()
        
        return all_questions[:num_questions]
    except QuestionGenerationError as e:
        st.error(f"An error occurred during question generation: {str(e)}")
        return []
    except Exception as e:
        st.error(f"An unexpected error occurred: {str(e)}")
        return []

async def process_batch(batch, keywords, context_window_size, num_beams):
    questions = []
    for text in batch:
        keyword_sentence_mapping = map_keywords_to_sentences(text, keywords, context_window_size)
        for keyword, context in keyword_sentence_mapping.items():
            question = await generate_question_async(context, keyword, num_beams)
            options = await generate_options_async(keyword, context)
            overall_score, relevance_score, complexity_score, spelling_correctness = assess_question_quality(context, question, keyword)
            if overall_score >= 0.5:
                questions.append({
                    "question": question,
                    "context": context,
                    "answer": keyword,
                    "options": options,
                    "overall_score": overall_score,
                    "relevance_score": relevance_score,
                    "complexity_score": complexity_score,
                    "spelling_correctness": spelling_correctness,
                })
    return questions

# Function to export questions to CSV
def export_to_csv(data):
    # df = pd.DataFrame(data, columns=["Context", "Answer", "Question", "Options"])
    df = pd.DataFrame(data)
    # csv = df.to_csv(index=False,encoding='utf-8')
    csv = df.to_csv(index=False)
    return csv

# Function to export questions to PDF
def export_to_pdf(data):
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", size=12)
    
    for item in data:
        pdf.multi_cell(0, 10, f"Context: {item['context']}")
        pdf.multi_cell(0, 10, f"Question: {item['question']}")
        pdf.multi_cell(0, 10, f"Answer: {item['answer']}")
        pdf.multi_cell(0, 10, f"Options: {', '.join(item['options'])}")
        pdf.multi_cell(0, 10, f"Overall Score: {item['overall_score']:.2f}")
        pdf.ln(10)
    
    return pdf.output(dest='S').encode('latin-1')

def display_word_cloud(generated_questions):
    word_frequency = {}
    for question in generated_questions:
        words = question.split()
        for word in words:
            word_frequency[word] = word_frequency.get(word, 0) + 1

    wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(word_frequency)
    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    st.pyplot()


def assess_question_quality(context, question, answer):
    # Assess relevance using cosine similarity
    context_doc = nlp(context)
    question_doc = nlp(question)
    relevance_score = context_doc.similarity(question_doc)

    # Assess complexity using token length (as a simple metric)
    complexity_score = min(len(question_doc) / 20, 1)  # Normalize to 0-1

    # Assess Spelling correctness
    misspelled = spell.unknown(question.split())
    spelling_correctness = 1 - (len(misspelled) / len(question.split()))  # Normalize to 0-1

    # Calculate overall score (you can adjust weights as needed)
    overall_score = (
        0.4 * relevance_score +
        0.4 * complexity_score +
        0.2 * spelling_correctness
    )

    return overall_score, relevance_score, complexity_score, spelling_correctness

def main():
    # Streamlit interface
    st.title(":blue[Question Generator System]")
    session_id = get_session_id()
    state = initialize_state(session_id)
    if 'feedback_data' not in st.session_state:
        st.session_state.feedback_data = []

    with st.sidebar:
        show_info = st.toggle('Show Info',True)
        if show_info:
            display_info()
        st.subheader("Customization Options")
        # Customization options
        input_type = st.radio("Select Input Preference", ("Text Input","Upload PDF"))
        with st.expander("Choose the Additional Elements to show"):
            show_context = st.checkbox("Context",True)
            show_answer = st.checkbox("Answer",True)
            show_options = st.checkbox("Options",False)
            show_entity_link = st.checkbox("Entity Link For Wikipedia",True)
            show_qa_scores = st.checkbox("QA Score",False)
        num_beams = st.slider("Select number of beams for question generation", min_value=2, max_value=10, value=2)
        context_window_size = st.slider("Select context window size (number of sentences before and after)", min_value=1, max_value=5, value=1)
        num_questions = st.slider("Select number of questions to generate", min_value=1, max_value=1000, value=5)
        col1, col2 = st.columns(2)
        with col1:
            extract_all_keywords = st.toggle("Extract Max Keywords",value=False)
        with col2:
            enable_feedback_mode = st.toggle("Enable Feedback Mode",False)

    text = None
    if input_type == "Text Input":
        text = st.text_area("Enter text here:", value="Joe Biden, the current US president is on a weak wicket going in for his reelection later this November against former President Donald Trump.", help="Enter or paste your text here")
    elif input_type == "Upload PDF":
        file = st.file_uploader("Upload PDF Files")
        if file is not None:
            try:
                text = get_pdf_text(file)
            except Exception as e:
                st.error(f"Error reading PDF file: {str(e)}")
                text = None
    if text:
        text = clean_text(text)
    with st.expander("Show text"):
        st.write(text)
    generate_questions_button = st.button("Generate Questions")
    st.markdown('<span aria-label="Generate questions button">Above is the generate questions button</span>', unsafe_allow_html=True)

    # if generate_questions_button:
    if generate_questions_button and text:
        start_time = time.time()
        with st.spinner("Generating questions..."):
            try:
                state['generated_questions'] = asyncio.run(generate_questions_async(text, num_questions, context_window_size, num_beams, extract_all_keywords))
                if not state['generated_questions']:
                    st.warning("No questions were generated. The text might be too short or lack suitable content.")
                else:
                    st.success(f"Successfully generated {len(state['generated_questions'])} questions!")
            except QuestionGenerationError as e:
                st.error(f"An error occurred during question generation: {str(e)}")
            except Exception as e:
                st.error(f"An unexpected error occurred: {str(e)}")

        print("\n\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n")
        data = get_state(session_id)
        print(data)
        end_time = time.time()
        print(f"Time Taken to generate: {end_time-start_time}")
        set_state(session_id, 'generated_questions', state['generated_questions'])
    
    # sort question based on their quality score
    state['generated_questions'] = sorted(state['generated_questions'],key = lambda x: x['overall_score'], reverse=True)
    # Display generated questions
    if state['generated_questions']:
        st.header("Generated Questions:",divider='blue')
        for i, q in enumerate(state['generated_questions']):
            st.subheader(body=f":orange[Q{i+1}:] {q['question']}")

            if show_context is True:
                st.write(f"**Context:** {q['context']}")
            if show_answer is True:
                st.write(f"**Answer:** {q['answer']}")
            if show_options is True:
                st.write(f"**Options:**")
                for j, option in enumerate(q['options']):
                    st.write(f"{chr(65+j)}. {option}")
            if show_entity_link is True:
                linked_entity = entity_linking(q['answer'])
                if linked_entity:
                    st.write(f"**Entity Link:** {linked_entity}")
            if show_qa_scores is True:
                m1,m2,m3,m4 = st.columns([1.7,1,1,1])
                m1.metric("Overall Quality Score", value=f"{q['overall_score']:,.2f}")
                m2.metric("Relevance Score", value=f"{q['relevance_score']:,.2f}")
                m3.metric("Complexity Score", value=f"{q['complexity_score']:,.2f}")
                m4.metric("Spelling Correctness", value=f"{q['spelling_correctness']:,.2f}")

                # q['context'] = st.text_area(f"Edit Context {i+1}:", value=q['context'], key=f"context_{i}")
            if enable_feedback_mode:
                collect_feedback(
                    i,
                    question = q['question'],
                    answer = q['answer'],
                    context = q['context'],
                    options = q['options'],
                )
            st.write("---")
        
            
        # Export buttons
        # if st.session_state.generated_questions:
        if state['generated_questions']:
            with st.sidebar:            
                csv_data = export_to_csv(state['generated_questions'])
                st.download_button(label="Download CSV", data=csv_data, file_name='questions.csv', mime='text/csv')

                pdf_data = export_to_pdf(state['generated_questions'])
                st.download_button(label="Download PDF", data=pdf_data, file_name='questions.pdf', mime='application/pdf')

            with st.expander("View Visualizations"):
                questions = [tpl['question'] for tpl in state['generated_questions']]
                overall_scores = [tpl['overall_score'] for tpl in state['generated_questions']]
                st.subheader('WordCloud of Questions',divider='rainbow')
                display_word_cloud(questions)
                st.subheader('Overall Scores',divider='violet')
                overall_scores = pd.DataFrame(overall_scores,columns=['Overall Scores'])
                st.line_chart(overall_scores)


    # View Feedback Statistics
    with st.expander("View Feedback Statistics"):
        analyze_feedback()
        if st.button("Export Feedback"):
            feedback_data = export_feedback_data()
            pswd = st.secrets['EMAIL_PASSWORD']
            send_email_with_attachment(
                email_subject='feedback from QGen',
                email_body='Please find the attached feedback JSON file.',
                recipient_emails=['apjc01unique@gmail.com', 'channingfisher7@gmail.com'],
                sender_email='apjc01unique@gmail.com',
                sender_password=pswd,
                attachment=feedback_data
            ) 

    print("********************************************************************************")

if __name__ == '__main__':
    try:
        main()
    except Exception as e:
        st.error(f"An unexpected error occurred: {str(e)}")
        st.error("Please try refreshing the page. If the problem persists, contact support.")