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'[{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.'

# The modified function generates embeddings based on PDF file name and page number and checks if the embeddings file exists before loading or generating it.	
def load_recommender(path, start_page=1):
    global recommender
    pdf_file = os.path.basename(path)
    embeddings_file = f"{pdf_file}_{start_page}.npy"
    
    if os.path.isfile(embeddings_file):
        embeddings = np.load(embeddings_file)
        recommender.embeddings = embeddings
        recommender.fitted = True
        return "Embeddings loaded from file"
    
    texts = pdf_to_text(path, start_page=start_page)
    chunks = text_to_chunks(texts, start_page=start_page)
    recommender.fit(chunks)
    np.save(embeddings_file, recommender.embeddings)
    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_text2(openAI_key, prompt, engine="gpt-3.5-turbo-0301"):
    openai.api_key = openAI_key
    messages = [{'role': 'system', 'content': 'You are a helpful assistant.'},
                {'role': 'user', 'content': prompt}]
    
    completions = openai.ChatCompletion.create(
        model=engine,
        messages=messages,
        max_tokens=512,
        n=1,
        stop=None,
        temperature=0.7,
    )
    message = completions.choices[0].message['content']
    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 'Text Not Found in PDF'. Ignore outlier "\
              "search results which has nothing to do with the question. Only answer what is asked. The "\
              "answer should be short and concise. Answer step-by-step. \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 = 'Chat with Your PDFs'
description = """ Instructions
1. Input your API Key
2. Upload PDF"""

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'])
            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='Answer :')

        btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
#openai.api_key = os.getenv('Your_Key_Here') 
demo.launch()


# import streamlit as st

# #Define the app layout
# st.markdown(f'<center><h1>{title}</h1></center>', unsafe_allow_html=True)
# st.markdown(description)

# col1, col2 = st.columns(2)

# # Define the inputs in the first column
# with col1:
#     url = st.text_input('URL')
#     st.markdown("<center><h6>or<h6></center>", unsafe_allow_html=True)
#     file = st.file_uploader('PDF', type='pdf')
#     question = st.text_input('question')
#     btn = st.button('Submit')

# # Define the output in the second column
# with col2:
#     answer = st.text_input('answer')

# # Define the button action
# if btn:
#     answer_value = question_answer(url, file, question)
#     answer.value = answer_value