import gc
import os
import csv
import socket
import huggingface_hub

import gradio as gr
import pandas as pd

from huggingface_hub import Repository
from transformers import AutoTokenizer, AutoModelWithLMHead 


## connection with HF datasets
HF_TOKEN = os.environ.get("HF_TOKEN")
DATASET_NAME = "emotion_detection"
DATASET_REPO_URL = f"https://huggingface.co/datasets/pragnakalp/{DATASET_NAME}"
DATA_FILENAME = "emotion_detection_logs.csv"
DATA_FILE = os.path.join("emotion_detection_logs", DATA_FILENAME)
DATASET_REPO_ID = "pragnakalp/emotion_detection"
print("is none?", HF_TOKEN is None)
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="emotion_detection_logs", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
)

SENTENCES_VALUE = """Raj loves Simran.\nLast year I lost my Dog.\nI bought a new phone!\nShe is scared of cockroaches.\nWow! I was not expecting that.\nShe got mad at him."""
## load model
cwd = os.getcwd()
model_path = os.path.join(cwd)
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
model_base = AutoModelWithLMHead.from_pretrained(model_path)

"""
get ip address
"""
def get_device_ip_address():
    result = {}
    if os.name == "nt":
        result = "Running on Windows"
        hostname = socket.gethostname()
        ip_address = socket.gethostbyname(hostname)
        result['ip_addr'] = ip_address
        result['host'] = hostname
        print(result)
        return result
    elif os.name == "posix":
        gw = os.popen("ip -4 route show default").read().split()
        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
        s.connect((gw[2], 0))
        ipaddr = s.getsockname()[0]
        gateway = gw[2]
        host = socket.gethostname()
        result['ip_addr'] = ipaddr
        result['host'] = host
        print(result)
        return result
    else:
        result['id'] = os.name + " not supported yet."
        print(result)
        return result

"""
generate emotions of the sentences
"""
def get_emotion(text):

    # input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
    input_ids = tokenizer.encode(text, return_tensors='pt')
    output = model_base.generate(input_ids=input_ids,
                                 max_length=2)

    dec = [tokenizer.decode(ids) for ids in output]
    label = dec[0]
    gc.collect()
    return label

def generate_emotion(article):
    sen_list = article
    sen_list = sen_list.split('\n')
    sen_list_temp = sen_list[0:]
    print(sen_list_temp)
    results_dict = []
    results = []

    for sen in sen_list_temp:
        if(sen.strip()):
            cur_result = get_emotion(sen)

            results.append(cur_result)
            results_dict.append(
                {
                    'sentence': sen,
                    'emotion': cur_result
                }
            )
            
    result = {'Input':sen_list_temp, 'Detected Emotion':results}
    gc.collect()
    save_data_and_sendmail(results_dict,sen_list, results)
    return pd.DataFrame(result)
    
"""
Save generated details
"""
def save_data_and_sendmail(results_dict,sen_list,results):
    try:
        hostname = {}
        
        add_csv = [results_dict]
        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)
        
        hostname = get_device_ip_address()
        print("hostname    ",hostname)
        url = 'https://pragnakalpdev35.pythonanywhere.com/hf_space_emotion_detection'
        # url = 'http://pragnakalpdev33.pythonanywhere.com/HF_space_question_generator'
        myobj = {'sen_list': sen_list,'gen_results': results,'ip_addr':hostname.get("ip_addr",""),'host':hostname.get("host","")}
        print("myobj    ",myobj)
        x = requests.post(url, json = myobj) 
            
    except Exception as e:
        return "Error while sending mail" + str(e)
        
    return "Successfully save data"
    
"""
UI design for demo using gradio app
"""
inputs = gr.Textbox(value=SENTENCES_VALUE,lines=10, label="Sentences",elem_id="inp_div")
outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Input","Detected Emotion"])]

demo = gr.Interface(
    generate_emotion,
    inputs,
    outputs,
    title="Emotion Detection",
    description="Feel free to give your feedback", 
    css=".gradio-container {background-color: lightgray} #inp_div {background-color: #FB3D5;}"
)
demo.launch()