import speech_recognition as sr
import gradio as gr
import time
import requests
import json
import time
from gtts import gTTS
import pandas as pd
import gradio as gr


# from transformers import pipeline
# p = pipeline("automatic-speech-recognition") 
r = sr.Recognizer() 
# def transcribe_old(audio):
#     text = p(audio)["text"]
#     return text

columns = ['FAQ', 'Answer']
NO_MATCH_RESPONSE = "Couldn't find relevant FAQ with enough confidence. Proceed to Agent?"

def get_faq_response_pd(text):
    # print("came here...")
    url = "https://nowrun.jina.ai/api/v1/text-to-text/search"

    payload = json.dumps({
      "host": "grpcs://nowapi-efb1aca434.wolf.jina.ai", # "grpcs://nowapi-3d81593c9f.wolf.jina.ai",
      "port": None,
      "jwt": {},
      "limit": 10,
      "uri": None,
      "text": text
    })
    headers = {
      'Content-Type': 'application/json'
    }
    
    try:
        response = requests.request("POST", url, headers=headers, data=payload)
        j = json.loads(response.text)
        # return f"Q. {j[0]['text']} \n2. {j[1]['text']}"
        if j[0]['scores']['cosine']['value']<0.65:
            FAQ_list = [j[i]['text'].split('?')[0]+'?' for i in range(2)]
            answers_list = [j[i]['text'].split('?')[1] for i in range(2)]
            return pd.DataFrame({columns[0]: FAQ_list,
                                 columns[1]: answers_list
                                })
        else:
            return pd.DataFrame({columns[0]: [NO_MATCH_RESPONSE], columns[1]:[""]}, columns=columns)
    except Exception as e:
        print(f"Exception occured: {e}")
        return pd.DataFrame(columns=columns)

# def get_faq_response(text):
#     # print("came here...")
#     url = "https://nowrun.jina.ai/api/v1/text-to-text/search"

#     payload = json.dumps({
#       "host": "grpcs://nowapi-efb1aca434.wolf.jina.ai", # "grpcs://nowapi-3d81593c9f.wolf.jina.ai",
#       "port": None,
#       "jwt": {},
#       "limit": 10,
#       "uri": None,
#       "text": text
#     })
#     headers = {
#       'Content-Type': 'application/json'
#     }
    
#     try:
#         response = requests.request("POST", url, headers=headers, data=payload)
#         j = json.loads(response.text)
#         return f"Q. {j[0]['text']} \n2. {j[1]['text']}"
#     except Exception as e:
#         print(f"Exception occured: {e}")
#         return ""

    
def transcribe(audio):
    with sr.AudioFile(audio) as source:
        audio_ = r.listen(source) 
        text = r.recognize_google(audio_)#, language = 'en-IN')# , show_all=True)
    return text

def tts(text):
    if len(text)>0:
        mytext = text
    else:
        mytext = NO_MATCH_RESPONSE
    language = 'en-IN'
    myobj = gTTS(text=mytext, lang=language, slow=False)

    filename = "abc.mp3"
    myobj.save(filename)
    # playsound.playsound(filename)
    # os.remove(filename)
    return filename

with gr.Blocks() as myapp:
    gr.Markdown("""
    # FAQs Automator
    ## _Ask me anything_
    FAQs Automator lets you automate your customer queries via chatbot interface or telemetry.<br />
    Current FAQ website used:<br />
    [Qatar Airways - Privilege club]<br />

    [//]: #
    [Qatar Airways - Privilege club]: <https://www.qatarairways.com/en/Privilege-Club/FAQ.html>
    """)
    inputs=gr.Audio(source="microphone", type="filepath")
    ask_btn = gr.Button("Ask FAQ")
    you_asked = gr.Textbox(label="You asked")
    ## best_response = gr.Textbox(label="best response")
    


    with gr.Row():
        # FAQs = gr.Textbox(label="Found FAQs")
        # answers = gr.Textbox(label="Found Answers")
        response_df_list = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(2,"dynamic"), label="Matching FAQs", headers=columns, wrap=True, interactive=False)]

    audio_bar = gr.Audio(label='Audio output')

    ask_btn.click(fn=transcribe, inputs=inputs, outputs=you_asked)
    you_asked.change(fn=get_faq_response_pd, inputs=you_asked, outputs=response_df_list)
    dummy_box = gr.Text(visible=False)


    def get_first_response(df):
        try:
            # print("came to get_first", df)
            # print(f"tdf.iloc[0,1] =  {df.iloc[0,1]}")
            # print(f"df[0] = {str(df[0])}")
            # print(f"df[1] = {str(df[1])}")
            return str(df.iloc[0,1])
        except:
            pass
        return ""

    response_df_list[0].change(fn=get_first_response, inputs=response_df_list, outputs=dummy_box)
    dummy_box.change(fn=tts, inputs=dummy_box, outputs=audio_bar)
    # dummy_box.change(fn=tts, inputs=dummy_box, outputs=audio_bar)
    # output.change(lambda x: "\n".join([k.split('?')[0]+"?\n\n" for k in get_faq_response(x).split('\n')]), output, FAQs)
    # output.change(lambda x: "\n".join([k.split('?')[1] for k in get_faq_response(x).split('\n')]), output, answers)
    # 


if __name__ == '__main__':
    # main()
    # test()
    # gr.Series(myapp, audioapp).launch()
    myapp.launch()