import os
import csv
import json
import requests
import re as r
import gradio as gr
import pandas as pd
from transformers import pipeline
from huggingface_hub import Repository
from urllib.request import urlopen

HF_TOKEN = os.environ.get("HF_TOKEN")
DATASET_NAME = "huggingface_sentiment_analysis_dataset"
DATASET_REPO_URL = f"https://huggingface.co/datasets/pragnakalp/huggingface_sentiment_analysis_dataset"
DATA_FILENAME = "hf_sentiment_logs.csv"
DATA_FILE = os.path.join("hf_sentiment_logs", DATA_FILENAME)
DATASET_REPO_ID = "pragnakalp/huggingface_sentiment_analysis_dataset"
print("is none?", HF_TOKEN is None)

input_para = "I am happy\nI am sad\nI am not feeling well\nHe is a very good person\nHe is bad person\nI love pineapple\nI hate mangoes"

try:
    hf_hub_download(
        repo_id=DATASET_REPO_ID,
        filename=DATA_FILENAME,
        cache_dir=DATA_DIRNAME,
        force_filename=DATA_FILENAME
    )
    
except:
    print("file not found")

repo = Repository(
    local_dir="hf_sentiment_logs", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
)

def getIP():
	d = str(urlopen('http://checkip.dyndns.com/')
			.read())

	return r.compile(r'Address: (\d+\.\d+\.\d+\.\d+)').search(d).group(1)

def get_location(ip_addr):
    ip=ip_addr

    req_data={
        "ip":ip,
        "token":"pkml123"
    }
    url = "https://demos.pragnakalp.com/get-ip-location"

    # req_data=json.dumps(req_data)
    # print("req_data",req_data)
    headers = {'Content-Type': 'application/json'}

    response = requests.request("POST", url, headers=headers, data=json.dumps(req_data))
    response = response.json()
    print("response======>>",response)
    return response

def huggingface_result_page(paragraph):
    if paragraph.strip():
        model_base = pipeline('sentiment-analysis')
    
        sen_list = paragraph
        sen_list = sen_list.split('\n')
        sen_list_temp = sen_list[0:]
        results = []
        temp_result_dict = []
    
        for sen in sen_list_temp:
            sen = sen.strip()
    
            if sen:
                cur_result = model_base(sen)[0]
                temp_result_dict.append(sen)
                results.append(cur_result['label'])
    
        result = {
            'Input': sen_list, 'Sentiment': results
        }
        print("LENGTH of results ====> ",str(len(results)))
        print("LENGTH of sen_list ====> ",str(len(temp_result_dict)))
        
        return pd.DataFrame(result)
    else:
        raise gr.Error("Please enter text in inputbox!!!!")

def save_data_and_sendmail(sen_list,results,result,paragraph):
    try:
        print("welcome")
        ip_address = ''

        ip_address= getIP()
        print(ip_address)
        location = get_location(ip_address)
        print(location)
        add_csv = [paragraph,result,ip_address,location]
        with open(DATA_FILE, "a") as f:
            writer = csv.writer(f)
            # write the data
            writer.writerow(add_csv)
            commit_url = repo.push_to_hub()
            print("commit data   :",commit_url)
        
        # url = 'https://pragnakalpdev35.pythonanywhere.com/HF_space_que_gen'
        # # url = 'http://pragnakalpdev33.pythonanywhere.com/HF_space_question_generator'
        # myobj = {'article': article,'total_que': num_que,'gen_que':result,'ip_addr':hostname.get("ip_addr",""),'host':hostname.get("host","")}
        # x = requests.post(url, json = myobj) 
        
        url = 'https://pragnakalpdev33.pythonanywhere.com/HF_space_sentiment'
        myobj = {'para': sen_list,'result':results,'ip_addr':ip_address,"location":location}
        x = requests.post(url, json = myobj) 
        
        return "Successfully save data"
    
    except Exception as e:
        print("error")
        return "Error while sending mail" + str(e)

inputs = gr.Textbox(lines=3, label="Paragraph",value=input_para)
outputs = gr.Dataframe(row_count = (3, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Input","Sentiment"],wrap=True)

demo = gr.Interface(
    huggingface_result_page,
    inputs,
    outputs,
    title="Huggingface Sentiment Analysis",
    css=".gradio-container {background-color: lightgray}",
    article = """<p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> on this Question Generation using T5 demo.</p>
                                        <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>"""
)

demo.launch()