from utils import cosineSim, googleSearch, getSentences, parallel_scrap, matchingScore
import gradio as gr
from urllib.request import urlopen, Request
from googleapiclient.discovery import build
import requests
import httpx
import re
from bs4 import BeautifulSoup
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import asyncio
from scipy.special import softmax
from evaluate import load
from datetime import date
import nltk
import fitz 
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
import nltk, spacy, subprocess, torch
import plotly.graph_objects as go
from writing_analysis import (
    normalize,
    preprocess_text1,
    preprocess_text2, 
    vocabulary_richness_ttr,
    calculate_gunning_fog,
    calculate_average_sentence_length,
    calculate_average_word_length,
    calculate_syntactic_tree_depth,
    calculate_perplexity,

    )

np.set_printoptions(suppress=True)


def plagiarism_check(
    plag_option,
    input,
    year_from,
    month_from,
    day_from,
    year_to,
    month_to,
    day_to,
    domains_to_skip,
):
    api_key = "AIzaSyCLyCCpOPLZWuptuPAPSg8cUIZhdEMVf6g"
    api_key = "AIzaSyCS1WQDMl1IMjaXtwSd_2rA195-Yc4psQE"
    api_key = "AIzaSyCB61O70B8AC3l5Kk3KMoLb6DN37B7nqIk"
    api_key = "AIzaSyCg1IbevcTAXAPYeYreps6wYWDbU0Kz8tg"
    # api_key = "AIzaSyBrx_pgb6A64wPFQXSGQRgGtukoxVV_0Fk"
    cse_id = "851813e81162b4ed4"

    sentences = getSentences(input)
    urlCount = {}
    ScoreArray = []
    urlList = []

    date_from = build_date(year_from, month_from, day_from)
    date_to = build_date(year_to, month_to, day_to)
    sort_date = f"date:r:{date_from}:{date_to}"

    # get list of URLS to check
    urlCount, ScoreArray = googleSearch(
        plag_option,
        sentences,
        urlCount,
        ScoreArray,
        urlList,
        sort_date,
        domains_to_skip,
        api_key,
        cse_id,
    )
    print("Number of URLs: ", len(urlCount))
    # print("Old Score Array:\n")
    # print2D(ScoreArray)

    # Scrape URLs in list
    formatted_tokens = []
    soups = asyncio.run(parallel_scrap(urlList))
    print(len(soups))
    print(
        "Successful scraping: "
        + str(len([x for x in soups if x is not None]))
        + "out of "
        + str(len(urlList))
    )

    # Populate matching scores for scrapped pages
    for i, soup in enumerate(soups):
        print(f"Analyzing {i+1} of {len(soups)} soups........................")
        if soup:
            page_content = soup.text
            for j, sent in enumerate(sentences):
                score = matchingScore(sent, page_content)
                ScoreArray[i][j] = score

    # ScoreArray = asyncio.run(parallel_analyze_2(soups, sentences, ScoreArray))
    # print("New Score Array:\n")
    # print2D(ScoreArray)


    # Gradio formatting section
    sentencePlag = [False] * len(sentences)
    sentenceToMaxURL = [-1] * len(sentences)
    for j in range(len(sentences)):
        if j > 0:
            maxScore = ScoreArray[sentenceToMaxURL[j - 1]][j]
            sentenceToMaxURL[j] = sentenceToMaxURL[j - 1]
        else:
            maxScore = -1
        for i in range(len(ScoreArray)):
            margin = (
                0.1
                if (j > 0 and sentenceToMaxURL[j] == sentenceToMaxURL[j - 1])
                else 0
            )
            if ScoreArray[i][j] - maxScore > margin:
                maxScore = ScoreArray[i][j]
                sentenceToMaxURL[j] = i
        if maxScore > 0.5:
            sentencePlag[j] = True
    
    if (
        (len(sentences) > 1)
        and (sentenceToMaxURL[1] != sentenceToMaxURL[0])
        and (
            ScoreArray[sentenceToMaxURL[0]][0]
            - ScoreArray[sentenceToMaxURL[1]][0]
            < 0.1
        )
    ):
        sentenceToMaxURL[0] = sentenceToMaxURL[1]

    index = np.unique(sentenceToMaxURL)

    urlMap = {}
    for count, i in enumerate(index):
        urlMap[i] = count + 1
    for i, sent in enumerate(sentences):
        formatted_tokens.append(
            (sent, "[" + str(urlMap[sentenceToMaxURL[i]]) + "]")
        )

    formatted_tokens.append(("\n", None))
    formatted_tokens.append(("\n", None))
    formatted_tokens.append(("\n", None))

    urlScore = {}
    for url in index:
        s = [
            ScoreArray[url][sen]
            for sen in range(len(sentences))
            if sentenceToMaxURL[sen] == url
        ]
        urlScore[url] = sum(s) / len(s)

    for ind in index:
        formatted_tokens.append(
            (
                urlList[ind] + " --- Matching Score: " + f"{str(round(urlScore[ind] * 100, 2))}%",
                "[" + str(urlMap[ind]) + "]",
            )
        )
        formatted_tokens.append(("\n", None))

    print(f"Formatted Tokens: {formatted_tokens}")

    return formatted_tokens


"""
AI DETECTION SECTION
"""

text_bc_model_path = "polygraf-ai/ai-text-bc-bert-2-7m"
text_bc_tokenizer = AutoTokenizer.from_pretrained(text_bc_model_path)
text_bc_model = AutoModelForSequenceClassification.from_pretrained(text_bc_model_path)

text_mc_model_path = "polygraf-ai/ai-text-mc-v5-lighter-spec"
text_mc_tokenizer = AutoTokenizer.from_pretrained(text_mc_model_path)
text_mc_model = AutoModelForSequenceClassification.from_pretrained(text_mc_model_path)

def remove_special_characters(text):
    cleaned_text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
    return cleaned_text

def update_character_count(text):
    return f"{len(text)} characters"

def predict_bc(model, tokenizer, text):
    tokens = tokenizer(
        text, padding=True, truncation=True, return_tensors="pt"
    )["input_ids"]
    output = model(tokens)
    output_norm = softmax(output.logits.detach().numpy(), 1)[0]
    print("BC Score: ", output_norm)
    bc_score = {"AI": output_norm[1].item(), "HUMAN": output_norm[0].item()}
    return bc_score


def predict_mc(model, tokenizer, text):
    tokens = tokenizer(
        text, padding=True, truncation=True, return_tensors="pt"
    )["input_ids"]
    output = model(tokens)
    output_norm = softmax(output.logits.detach().numpy(), 1)[0]
    print("MC Score: ", output_norm)
    mc_score = {}
    label_map = ["GPT 3.5", "GPT 4", "CLAUDE", "BARD", "LLAMA 2"]
    for score, label in zip(output_norm, label_map):
        mc_score[label.upper()] = score.item()
    return mc_score

def ai_generated_test(ai_option, input):

    cleaned_text = remove_special_characters(input)
    bc_score = predict_bc(text_bc_model, text_bc_tokenizer, cleaned_text)
    mc_score = predict_mc(text_mc_model, text_mc_tokenizer, cleaned_text)
    
    sum_prob = 1 - bc_score["HUMAN"]
    for key, value in mc_score.items():
        mc_score[key] = value * sum_prob
    
    if ai_option == "Human vs AI":
        mc_score = {}

    if sum_prob < 0.01  :
        mc_score = {}
        return bc_score, mc_score
    else:
        return bc_score, mc_score

# COMBINED
def main(
    ai_option,
    plag_option,
    input,
    # models,
    year_from,
    month_from,
    day_from,
    year_to,
    month_to,
    day_to,
    domains_to_skip,
):

    formatted_tokens = plagiarism_check(
        plag_option,
        input,
        year_from,
        month_from,
        day_from,
        year_to,
        month_to,
        day_to,
        domains_to_skip,
    )
    depth_analysis_plot = depth_analysis(input)
    bc_score, mc_score = ai_generated_test(ai_option,input)
    
    return (
    bc_score,
    mc_score,
    formatted_tokens,
    depth_analysis_plot,
            )


def build_date(year, month, day):
    return f"{year}{months[month]}{day}"

def len_validator(text):
    min_chars = 350  
    if len(text) < min_chars:
        return f"Warning! Input length is {len(text)}. Please input a text that is greater than {min_chars} characters long. Recommended length {min_chars*2} characters." 
    else :
        return f"Input length is satisified." 

def extract_text_from_pdf(pdf_path):
    doc = fitz.open(pdf_path)
    text = ""
    for page in doc:
        text += page.get_text()
    return text


# DEPTH ANALYSIS
print("loading depth analysis")
nltk.download('stopwords')
nltk.download('punkt')
command = ['python3', '-m', 'spacy', 'download', 'en_core_web_sm']
# Execute the command
subprocess.run(command)
nlp = spacy.load("en_core_web_sm")

# for perplexity
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "gpt2"
gpt2_model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
gpt2_tokenizer = GPT2TokenizerFast.from_pretrained(model_id)

def depth_analysis(input_text):

    # vocanulary richness
    processed_words = preprocess_text1(input_text)
    ttr_value = vocabulary_richness_ttr(processed_words)

    # readability
    gunning_fog = calculate_gunning_fog(input_text)
    gunning_fog_norm = normalize(gunning_fog, min_value=0, max_value=20)
    
    # average sentence length and average word length
    words, sentences = preprocess_text2(input_text)
    average_sentence_length = calculate_average_sentence_length(sentences)
    average_word_length = calculate_average_word_length(words)
    average_sentence_length_norm = normalize(average_sentence_length, min_value=0, max_value=40)
    average_word_length_norm = normalize(average_word_length, min_value=0, max_value=8)

    # syntactic_tree_depth
    average_tree_depth = calculate_syntactic_tree_depth(nlp, input_text)
    average_tree_depth_norm = normalize(average_tree_depth, min_value=0, max_value=10)

    # perplexity
    perplexity = calculate_perplexity(input_text, gpt2_model, gpt2_tokenizer, device)
    perplexity_norm = normalize(perplexity, min_value=0, max_value=30)

    features = {
        "readability": gunning_fog_norm, 
        "syntactic tree depth": average_tree_depth_norm,
        "vocabulary richness": ttr_value,
        "perplexity": perplexity_norm,
        "average sentence length": average_sentence_length_norm,
        "average word length": average_word_length_norm, 
    }

    print(features)

    fig = go.Figure()

    fig.add_trace(go.Scatterpolar(
        r=list(features.values()),
        theta=list(features.keys()),
        fill='toself',
        name='Radar Plot'
    ))

    fig.update_layout(
        polar=dict(
            radialaxis=dict(
                visible=True,
                range=[0, 100],
            )),
        showlegend=False,
        # autosize=False,
        # width=600,
        # height=600,
        margin=dict(
            l=10,
            r=20,
            b=10,
            t=10,
            # pad=100
        ),
    )

    return fig


# START OF GRADIO

title = "Copyright Checker"
months = {
    "January": "01",
    "February": "02",
    "March": "03",
    "April": "04",
    "May": "05",
    "June": "06",
    "July": "07",
    "August": "08",
    "September": "09",
    "October": "10",
    "November": "11",
    "December": "12",
}


with gr.Blocks() as demo:
    today = date.today()
    # dd/mm/YY
    d1 = today.strftime("%d/%B/%Y")
    d1 = d1.split("/")

    model_list = ["GPT 3.5", "GPT 4", "CLAUDE", "BARD", "LLAMA2"]
    domain_list = ["com", "org", "net", "int", "edu", "gov", "mil"]
    gr.Markdown(
        """
    # Copyright Checker 
    """
    )
    with gr.Row():
        input_text = gr.Textbox(label="Input text", lines=6, placeholder="")
        file_input = gr.File(label="Upload PDF")
        file_input.change(fn=extract_text_from_pdf, inputs=file_input, outputs=input_text)

    char_count = gr.Textbox(label="Minumum Character Limit Check") 
    input_text.change(fn=len_validator, inputs=input_text, outputs=char_count)

    with gr.Row():
        with gr.Column():
            ai_option = gr.Radio(["Human vs AI", "Human vs AI Source Models"], label="Choose an option please.")
        with gr.Column():
            plag_option = gr.Radio(["Standard", "Advanced"], label="Choose an option please.")

    with gr.Row():
        with gr.Column():
            only_ai_btn = gr.Button("AI Check")

        with gr.Column():
            only_plagiarism_btn = gr.Button("Plagiarism Check")
    
    with gr.Row():
        depth_analysis_btn = gr.Button("Detailed Writing Analysis")

    with gr.Row():
        full_check_btn = gr.Button("Full Check")

    gr.Markdown(
        """
        ## Output
        """
    )
    
    # models = gr.Dropdown(
            # model_list,
            # value=model_list,
            # multiselect=True,
            # label="Models to test against",
        # )
    
    with gr.Row():
        with gr.Column():
            bcLabel = gr.Label(label="Source")
        with gr.Column():
            mcLabel = gr.Label(label="Creator")

    with gr.Group():
        with gr.Row():
            month_from = gr.Dropdown(
                choices=months,
                label="From Month",
                value="January",
                interactive=True,
            )
            day_from = gr.Textbox(label="From Day", value="01")
            year_from = gr.Textbox(label="From Year", value="2000")
            # from_date_button = gr.Button("Submit")

        with gr.Row():
            month_to = gr.Dropdown(
                choices=months,
                label="To Month",
                value=d1[1],
                interactive=True,
            )
            day_to = gr.Textbox(label="To Day", value=d1[0])
            year_to = gr.Textbox(label="To Year", value=d1[2])
            # to_date_button = gr.Button("Submit")
        with gr.Row():
            domains_to_skip = gr.Dropdown(
                domain_list,
                multiselect=True,
                label="Domain To Skip",
            )

    with gr.Row():
        with gr.Column():
            sentenceBreakdown = gr.HighlightedText(
                label="Plagiarism Sentence Breakdown",
                combine_adjacent=True,
                color_map={
                    "[1]": "red",
                    "[2]": "orange",
                    "[3]": "yellow",
                    "[4]": "green",
                },
            )

    with gr.Row():
        with gr.Column():
            writing_analysis_plot = gr.Plot(
                label="Writing Analysis Plot"
            )

    full_check_btn.click(
        fn=main,
        inputs=[
            ai_option,
            plag_option,
            input_text,
            # models,
            year_from,
            month_from,
            day_from,
            year_to,
            month_to,
            day_to,
            domains_to_skip,
        ],
        outputs=[
            bcLabel,
            mcLabel,
            sentenceBreakdown,
            writing_analysis_plot,
        ],
        api_name="main",
    )

    only_ai_btn.click(
        fn=ai_generated_test,
        inputs=[ai_option, input_text],
        outputs=[
            bcLabel,
            mcLabel,
        ],
        api_name="ai_check",
    )

    only_plagiarism_btn.click(
        fn=plagiarism_check,
        inputs=[
            plag_option,
            input_text,
            year_from,
            month_from,
            day_from,
            year_to,
            month_to,
            day_to,
            domains_to_skip,
        ],
        outputs=[
            sentenceBreakdown,
        ],
        api_name="plagiarism_check",
    )

    depth_analysis_btn.click(
        fn=depth_analysis,
        inputs=[input_text],
        outputs=[writing_analysis_plot],
        api_name="depth_analysis",
    )

    date_from = ""
    date_to = ""

demo.launch()