import os, sys
# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, MBartForConditionalGeneration
# import torch
import gradio as gr
import requests
import json
# from huggingface_hub import login


class LTRC_Translation_API():
    def __init__(self, url = 'https://ssmt.iiit.ac.in/onemt', src_lang = 'en', tgt_lang = 'te'):
        self.lang_map = {'te': 'tel', 'en': 'eng', 'ta': 'tam', 'ml': 'mal', 'mr': 'mar', 'kn': 'kan', 'hi': 'hin'}
        self.url = url

        self.headers = {
            'Content-Type': 'application/json',
            'Accept': 'application/json'
        }

        tgt_lang = self.lang_map.get(tgt_lang, 'te')
        src_lang = self.lang_map.get(src_lang, 'en')

        self.src_lang = src_lang
        self.tgt_lang = tgt_lang

    def translate(self, text):
        try:
            data = {'text': text, 'source_language': self.src_lang, 'target_language': self.tgt_lang}

            response = requests.post(self.url, headers = self.headers, json = data)
            translated_text = json.loads(response.text).get('data', '')
            
            return translated_text
            
        except Exception as e:
            print("Exception: ", e)
            
            return ''
            

# class Headline_Generation():
# 	def __init__(self, model_name = "lokeshmadasu42/sample"):
# 		self.model_name = model_name

# 		self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 		self.tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False, use_fast=False, keep_accents=True)
# 		self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# 		self.model.to(self.device)
# 		self.model.eval()

# 		self.bos_id = self.tokenizer._convert_token_to_id_with_added_voc("<s>")
# 		self.eos_id = self.tokenizer._convert_token_to_id_with_added_voc("</s>")
# 		self.pad_id = self.tokenizer._convert_token_to_id_with_added_voc("<pad>")

# 		self.lang_map = {'as': '<2as>', 'bn': '<2bn>', 'en': '<2en>', 'gu': '<2gu>', 'hi': '<2hi>', 'kn': '<2kn>', 'ml': '<2ml>', 'mr': '<2mr>', 'or': '<2or>', 'pa': '<2pa>', 'ta': '<2ta>', 'te': '<2te>'}

# 		print("Headline Generation model loaded...!")


# 	def get_headline(self, text, lang_id):

# 		inp = self.tokenizer(text, add_special_tokens=False, return_tensors="pt", padding=True).to(self.device)
# 		inp = inp['input_ids']

# 		lang_code = self.lang_map.get(lang_id, '')

# 		text = text + "</s> " + lang_code
# 		# print("Text: ", text)

# 		model_output = self.model.generate(
# 			inp,
# 			use_cache=True,
# 			num_beams=5,
# 			max_length=32,
# 			min_length=1,
# 			early_stopping=True,
# 			pad_token_id = self.pad_id,
# 			bos_token_id = self.bos_id,
# 			eos_token_id = self.eos_id,
# 			decoder_start_token_id = self.tokenizer._convert_token_to_id_with_added_voc(lang_code)
# 		)

# 		decoded_output = self.tokenizer.decode(
# 			model_output[0],
# 			skip_special_tokens=True,
# 			clean_up_tokenization_spaces=False
# 		)

# 		return decoded_output


# class Summarization():
# 	def __init__(self, model_name = "ashokurlana/mBART-TeSum"):
# 		self.model_name = model_name

# 		self.device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# 		self.tokenizer = AutoTokenizer.from_pretrained(model_name)
# 		self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# 		self.model.to(self.device)
# 		self.model.eval()

# 		self.lang_map = {'te': 'te_IN', 'en': 'en_XX'}

# 		print("Summarization model loaded...!")


# 	def get_summary(self, text, lang_id):

# 		inp = self.tokenizer([text], add_special_tokens=False, return_tensors="pt", max_length = 1024).to(self.device)
# 		inp = inp['input_ids']

# 		lang_code = self.lang_map.get(lang_id, '')

# 		model_output = self.model.generate(
# 			inp,
# 			use_cache=True,
# 			num_beams=5,
# 			max_length=256,
# 			early_stopping=True
# 		)

# 		decoded_output = [self.tokenizer.decode(
# 			summ_id,
# 			skip_special_tokens=True,
# 			clean_up_tokenization_spaces=False
# 		) for summ_id in model_output]

# 		return " ".join(decoded_output)


def get_prediction(text, src_lang_id, tgt_lang_id, translate = False):
    # if len(sys.argv)<3:
    # 	print("Usage: python app.py <text_file_path> <lang_id>")
    # 	print("Text file should contain the article news")
    # 	exit()
    
    # txt_path = sys.argv[1]
    # lang_id = sys.argv[2]
    
    # if not os.path.exists(txt_path):
    # 	print("Path: {} do not exists".format(txt_path))
    # 	exit()
    
    # text = ''
    # with open(txt_path, 'r', encoding='utf-8') as fp:
    # 	text = fp.read().strip()

    ### Login to huggingface token
    # access_token = "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"
    # login(token = access_token)
    
    # headline_generator = Headline_Generation()
    # summarizer = Summarization()
    # if translate == True:
    #     translator = LTRC_Translation_API(tgt_lang = lang_id)
    #     text = translator.translate(text)
    
    # headline = headline_generator.get_headline(text, lang_id)
    # summary = summarizer.get_summary(text, lang_id)
        
    
    # print("Article: ", text)
    # print("Summary: ", summary)
    # print("Headline: ", headline)
    
    # return "Headline: " + headline + "\nSummary: " + summary
    # return [text, summary, headline]

    translator = LTRC_Translation_API(src_lang = src_lang_id, tgt_lang = tgt_lang_id)
    text = translator.translate(text)
    
    return text

interface = gr.Interface(
    get_prediction,
    inputs=[
        gr.Textbox(lines = 8, label = "Source Text", info = "Provide the news article text here"),
        # gr.Textbox(lines = 8, label = "News Article Text", info = "Provide the news article text here. Check the `Translate` if the source language is english."),
        # gr.Dropdown(
        #     ['as', 'bn', 'en', 'gu', 'hi', 'kn', 'ml', 'mr', 'or', 'pa', 'ta', 'te'], label="Source Language code", info="select the source language code"
        # ),
        # gr.Dropdown(
        #     ['as', 'bn', 'en', 'gu', 'hi', 'kn', 'ml', 'mr', 'or', 'pa', 'ta', 'te'], label="Target Language code", info="select the target language code"
        # ),
        gr.Dropdown(
            ['en', 'hi', 'kn', 'ml', 'mr', 'ta', 'te'], label="Source Language code", info="select the source language code"
        ),
        gr.Dropdown(
            ['en', 'hi', 'kn', 'ml', 'mr', 'ta', 'te'], label="Target Language code", info="select the target language code"
        ),
        # gr.Checkbox(label="Translate", info="Is translation required?")
    ],
    outputs=[
        gr.Textbox(lines = 8, label = "Translation", info = "Translated text"),
        # gr.Textbox(lines = 8, label = "Source Article Text", info = "Source article text (if `Translate` is enabled then the source will be translated to target language)"),
        # gr.Textbox(lines = 4, label = "Summary", info = "Summary of the given article (translated if `Translate` is enabled)"),
        # gr.Textbox(lines = 2, label = "Headline", info = "Generated headline of the given article (translated if `Translate` is enabled)")
    ]
)
interface.launch(share=True)