from watson_developer_cloud import SpeechToTextV1
import json, base64
from nltk.tokenize import word_tokenize
import re
import matplotlib.pyplot as plt
from watson_developer_cloud import NaturalLanguageUnderstandingV1
import watson_developer_cloud.natural_language_understanding.features.v1 as \
    features
from wordcloud import WordCloud
import os
import subprocess
##removes non non-alphanumeric characters based on re
def re_nalpha(str):
    pattern = re.compile(r'[^\w\s]', re.U)
    return re.sub(r'_', '', re.sub(pattern, '', str))

def wfreq (text):
    word_dic = {}
    if isinstance(text,str):
        #preprocessing module is used to tokenize the text
        text = word_tokenize(re_nalpha(text))
        for word in text:
            if word not in word_dic:
                word_dic[word] = 1
            else:
                word_dic[word] +=1
    else:
        for sent in text:
            sent_str = word_tokenize(re_nalpha(sent[0]))
            for word in sent_str:
                if word not in word_dic:
                    word_dic[word] = 1
                else:
                    word_dic[word] +=1
    sorted_list = sorted(word_dic.items(), key=lambda d: d[1],reverse=True)
    return sorted_list

def create_pic(input_text):
    sorted_list=wfreq(input_text)
    print (sorted_list)
    x_list = []
    y_list = []
    for item in sorted_list:
        x_list.append(item[0])
        y_list.append(item[1])
    x=range(len(x_list[:5]))
    plt.bar(x, y_list[:5],width=0.9)
    plt.xticks((x),x_list)
    plt.savefig('Interviewer/static/Interviewer/userMedia/freq.png')
    plt.close()

def create_emo(input_text):
    natural_language_understanding = NaturalLanguageUnderstandingV1(
        version='2017-02-27',
        username='6a28fa30-2bd7-4981-9037-09e3e8a3a691',
        password='ZtUrPvaSmxKL')

    response = natural_language_understanding.analyze(
        text=input_text,
        features=[features.Emotion()])
    result=json.loads(json.dumps(response, indent=2))
    print(result)
    emo_rate=result["emotion"]["document"]["emotion"]
    labels = ['sadness', 'joy', 'fear', 'disgust','anger']
    sizes = [emo_rate['sadness'], emo_rate['joy'], emo_rate['fear'], emo_rate['disgust'],emo_rate['anger']]
    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral','black']
    patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90)
    plt.legend(patches, labels, loc="best")
    plt.tight_layout()
    plt.savefig('Interviewer/static/Interviewer/userMedia/emo.png')
    plt.close()
def word_could(text):
# Generate a word cloud image
    wordcloud = WordCloud().generate(text)
    wordcloud = WordCloud(max_font_size=40).generate(text)
    plt.figure()
    plt.imshow(wordcloud, interpolation="bilinear")
    plt.axis("off")
    plt.savefig('Interviewer/static/Interviewer/userMedia/wordcloud.png')
    plt.close()

def pic_anly():
    p = subprocess.Popen('curl -X POST "https://v1-api.visioncloudapi.com/face/detection?api_id=5a0f0ffc899a4db88241a0e7bd4ed306&api_secret=f66a525a98e54c3e97bb1d6ee9d6d4c7&attributes=true" \
      -F file=@user_photo.png', stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd='C:\\Users\\rabbani\\PycharmProjects\\Interview-AI')
    out, error = p.communicate()
    result=json.loads(out.decode("utf-8"))
    desc = ''
    if(len(result['faces'])==1):
        att=result['faces'][0]['attributes']
        emo=result['faces'][0]['emotions']
        desc+="Description for the appearance" + '\n'
        desc+="age: "+str(att['age'])+'\n'
        d_smile='No'
        if(att['smile']==1):
            d_smile='yes'
        desc+="smile: "+d_smile+'\n'
        gender = att['gender']
        d_gender=''
        if(gender>50):
            d_gender='Male'
        else:
            d_gender='Female'
        desc+="Gender: " + d_gender + '\n'

        glass = att['eyeglass']
        d_glass = ''

        if(glass>50):
            d_glass="with glass"
        else:
            d_glass="without glass"
        desc+="Eyeglass: " + d_glass + '\n'
        desc+="\nDescription for the emotion:" + '\n'
        d_emo=''
        emo_list = ["angry","calm","confused","disgust","happy","sad","scared","surprised","screaming"]
        for sub_emo in emo_list:
            if(emo[sub_emo]==1):
                d_emo+=sub_emo
        desc+=d_emo+'\n'
    else:
        desc="Invalid picture - mutiple faces detected or nofaces detected"
    return (desc)

def speech_to_text(b64code):

    stt = SpeechToTextV1(username="5ff4851b-de60-45c5-9fdb-e0d7d9b866c2", password="3siYdkUyzVoj")

    audio_file = base64.b64decode(b64code)

    result = json.dumps(stt.recognize(audio_file, content_type="audio/wav",continuous="true",timestamps="true",word_confidence="True",model="en-UK_NarrowbandModel"), indent=2)

    # print result
    wjdata = json.loads(result)

    temp_str = ''
    for subs in wjdata["results"]:
        # print "transcript"
        temp_str+=subs["alternatives"][0]["transcript"]
    print(temp_str)
    timestamps = subs["alternatives"][0]["timestamps"]
    average_velocity = round(60/((timestamps[-1][2]-timestamps[0][1])/len(timestamps)))
    print(average_velocity)
    create_pic(temp_str)
    create_emo(temp_str)
    word_could(temp_str)
    result = {'speech_str': temp_str, 'average_velocity': average_velocity}
    print(pic_anly())
    return result