from gradio_client import Client
from hugchat import hugchat
from hugchat.login import Login
from gtts import gTTS
import gradio as gr

client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/")
chat_client = Client("https://huggingfaceh4-falcon-chat.hf.space/")
retrieval = Client("https://warlord-k-iiti-similarity.hf.space/")

n_conv = 0

init_prompt ="## Instruction: You are an AI language model and must return truthful responses as per the information below\n ##Input: Information: My name is IITIGPT. I am a helpful and truthful chatbot. I can help answer any questions about the IIT Indore campus."
info="Information: \n"
q_prompt="\n ##Instruction: Please  provide an appropriate response to the following: \n"

def login_hf():
  email="jackrudolf584@gmail.com"
  passwd="Qwertypassword1"
  
  # login
  sign = Login(email, passwd)
  cookies = sign.login()
  
  # Create a ChatBot
  chatbot = hugchat.ChatBot(cookies=cookies.get_dict())  # or cookie_path="usercookies/<email>.json"
  chatbot.chat(init_prompt)
  
  print("Logged In")
  return chatbot

def change_conv():
    # Create a new conversation
    id = chatbot.new_conversation()
    chatbot.change_conversation(id)
    chatbot.chat(init_prompt)
    chatbot.cookies = {}
    
def answer_question(question):
    global n_conv
    if(n_conv > 3):
        n_conv = 0
        change_conv(chatbot)
    information = retrieval.predict(question, api_name = "/predict")
    answer = chatbot.chat(info+ information +q_prompt+" "+question+"""\n ##Response:""")
    n_conv+=1
    return answer

def answer(question):
    information = retrieval.predict(question, api_name = "/predict")
    answer=chat_client.predict(
				"Howdy!",
                "abc.json", 
                "You are an AI language model and must return truthful responses as per the information below\n ##Input: Information: Your name is IITIGPT. You are a helpful and truthful chatbot. You can help answer any questions about the IIT Indore campus." +information+question,	# str  in 'Type an input and press Enter' Textbox component
                0.8,
                0.9,
				fn_index=4
    )
    with open(answer, 'rb') as f:
        data = f.read()
    temp=json.load(data)
    print(temp)
    return temp

# chatbot = login_hf()

def file_to_text(audio_fpath):

    result = client.predict(
        audio_fpath,
        "transcribe", # str in 'Audio input' Radio component
        api_name="/predict"
    )
    return result

def text_file(text):
    tts = gTTS(text, lang = "en")
    tts.save("abc.mp3")
    return "abc.mp3"

def main(filename):
  text = file_to_text(filename)
  print(text)
  # answer = answer_question(text)
  answer = answer(text)
  print(answer)
  output = text_file(answer)
  return output

with gr.Blocks() as demo:
    with gr.Row():
        input = gr.Audio(source = "upload", type = "filepath", label = "Input")
        output = gr.Audio(type = "filepath", label = "Output")
    btn = gr.Button("Run")
    btn.click(main, [input], [output])

if __name__ == "__main__":
    demo.launch()