import urllib.request
import fitz
import re
import numpy as np
import tensorflow_hub as hub
import openai
import gradio as gr
import os
from sklearn.neighbors import NearestNeighbors

def download_pdf(url, output_path):
    urllib.request.urlretrieve(url, output_path)


def preprocess(text):
    text = text.replace('\n', ' ')
    text = re.sub('\s+', ' ', text)
    return text


def pdf_to_text(path, start_page=1, end_page=None):
    doc = fitz.open(path)
    total_pages = doc.page_count

    if end_page is None:
        end_page = total_pages

    text_list = []

    for i in range(start_page-1, end_page):
        text = doc.load_page(i).get_text("text")
        text = preprocess(text)
        text_list.append(text)

    doc.close()
    return text_list


def text_to_chunks(texts, word_length=150, start_page=1):
    text_toks = [t.split(' ') for t in texts]
    page_nums = []
    chunks = []
    
    for idx, words in enumerate(text_toks):
        for i in range(0, len(words), word_length):
            chunk = words[i:i+word_length]
            if (i+word_length) > len(words) and (len(chunk) < word_length) and (
                len(text_toks) != (idx+1)):
                text_toks[idx+1] = chunk + text_toks[idx+1]
                continue
            chunk = ' '.join(chunk).strip()
            chunk = f'[Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"'
            chunks.append(chunk)
    return chunks


class SemanticSearch:
    
    def __init__(self):
        self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
        self.fitted = False
    
    
    def fit(self, data, batch=1000, n_neighbors=5):
        self.data = data
        self.embeddings = self.get_text_embedding(data, batch=batch)
        n_neighbors = min(n_neighbors, len(self.embeddings))
        self.nn = NearestNeighbors(n_neighbors=n_neighbors)
        self.nn.fit(self.embeddings)
        self.fitted = True
    
    
    def __call__(self, text, return_data=True):
        inp_emb = self.use([text])
        neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
        
        if return_data:
            return [self.data[i] for i in neighbors]
        else:
            return neighbors
    
    
    def get_text_embedding(self, texts, batch=1000):
        embeddings = []
        for i in range(0, len(texts), batch):
            text_batch = texts[i:(i+batch)]
            emb_batch = self.use(text_batch)
            embeddings.append(emb_batch)
        embeddings = np.vstack(embeddings)
        return embeddings



def load_recommender(path, start_page=1):
    global recommender
    texts = pdf_to_text(path, start_page=start_page)
    chunks = text_to_chunks(texts, start_page=start_page)
    recommender.fit(chunks)
    return 'Corpus Loaded.'


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=0.7,
    )
    message = completions.choices[0].text
    return message


def generate_answer(question,openAI_key):
    topn_chunks = recommender(question)
    prompt = ""
    prompt += 'search results:\n\n'
    for c in topn_chunks:
        prompt += c + '\n\n'
        
    prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\
              "Cite each reference using [ Page Number] notation (every result has this number at the beginning). "\
              "Citation should be done at the end of each sentence. If the search results mention multiple subjects "\
              "with the same name, create separate answers for each. Only include information found in the results and "\
              "don't add any additional information. Make sure the answer is correct and don't output false content. "\
              "If the text does not relate to the query, simply state 'Found Nothing'. Ignore outlier "\
              "search results which has nothing to do with the question. Only answer what is asked. The "\
              "answer should be short and concise. \n\nQuery: {question}\nAnswer: "
    
    prompt += f"Query: {question}\nAnswer:"
    answer = generate_text(openAI_key, prompt,"text-davinci-003")
    return answer


def question_answer(url, file, question,openAI_key):
    if openAI_key.strip()=='':
        return '[ERROR]: Please enter you Open AI Key. Get your key here : https://platform.openai.com/account/api-keys'
    if url.strip() == '' and file == None:
        return '[ERROR]: Both URL and PDF is empty. Provide atleast one.'
    
    if url.strip() != '' and file != None:
        return '[ERROR]: Both URL and PDF is provided. Please provide only one (eiter URL or PDF).'

    if url.strip() != '':
        glob_url = url
        download_pdf(glob_url, 'corpus.pdf')
        load_recommender('corpus.pdf')

    else:
        old_file_name = file.name
        file_name = file.name
        file_name = file_name[:-12] + file_name[-4:]
        os.rename(old_file_name, file_name)
        load_recommender(file_name)

    if question.strip() == '':
        return '[ERROR]: Question field is empty'

    return generate_answer(question,openAI_key)


recommender = SemanticSearch()

title = 'PDF GPT'
description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination free response than other tools as the embeddings are better than OpenAI. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""

questions = [
    "what did the study investigate?",
    "what are the methods used in this study?",
    "what are the data intervals used in this study? Give me the start dates and end dates?",
    "what are the main limitations of this study?",
    "what are the main shortcomings of this study?",
    "what are the main findings of the study?",
    "what are the main results of the study?",
    "what are the input features used in this study?",
    "what is the dependent variable in this study?"
]
question = gr.Textbox(label='Enter your question here')

# Create a button for each question, which will call set_question_text when clicked
question_buttons = [gr.Button(f"{q}", fn=set_question_text) for q in questions]


# Create a function to set the text of the question Textbox
def set_question_text(q):
    question.set_value(q)
    
# Add the question buttons to the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown(f'<center><h1>{title}</h1></center>')
    gr.Markdown(description)

    with gr.Row():
        with gr.Group():
            gr.Markdown(f'<p style="text-align:center">Get your Open AI API key <a href="https://platform.openai.com/account/api-keys">here</a></p>')
            openAI_key=gr.Textbox(label='Enter your OpenAI API key here')
            url = gr.Textbox(label='Enter PDF URL here')
            gr.Markdown("<center><h4>OR<h4></center>")
            file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
            # Add the question buttons to the Gradio interface
            for btn in question_buttons:
                gr.Block(btn)
            question = gr.Textbox(label='Enter your question here')
            btn = gr.Button(value='Submit')
            btn.style(full_width=True)

        with gr.Group():
            answer = gr.Textbox(label='The answer to your question is :')

        btn.click(question_answer, inputs=[url, file, question, openAI_key], outputs=[answer])

#openai.api_key = os.getenv('Your_Key_Here') 
demo.launch()