# import gradio as gr
# import requests
# import os

# def function1(prompt):
#     response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={
#       "data": [
#         prompt,
#     ]}).json()
#     message = response["data"][0]
#     url = 'https://api.elevenlabs.io/v1/text-to-speech/pNInz6obpgDQGcFmaJgB'

#     headers = {
#         'accept': 'audio/mpeg',
#         'xi-api-key': os.environ.get("test2"),
#         'Content-Type': 'application/json'
#     }

#     data = {
#         "text": message,
#         "voice_settings": {
#             "stability": 0,
#             "similarity_boost": 0
#         }
#     }

#     response = requests.post(url, headers=headers, json=data)
#     if response.status_code == 200:
#         file_path = 'test.mp3'
#         if os.path.isfile(file_path):
#             os.remove(file_path)
#         with open(file_path, 'wb') as f:
#             f.write(response.content)
#     return "test.mp3"

# iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")])
# iface.launch()


# import gradio as gr
# import requests
# import urllib.request
# from pydub import AudioSegment
# import numpy as np
# import os

# def function1(prompt):
#     response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={
#       "data": [
#         prompt,
#     ]}).json()
#     data = response["data"][0]
#     response = requests.post("https://matthijs-speecht5-tts-demo.hf.space/run/predict", json={
#         "data": [
#             data,
#             "KSP (male)",
#         ]
#     }).json()
#     data = response["data"][0]["name"]
#     data = "https://matthijs-speecht5-tts-demo.hf.space/file="+data
#     file_name, headers = urllib.request.urlretrieve(data, "speech.mp3")
#     # code = random.randint(1,1000)
#     # generated_file = f"output{code}"
#     filename = "output.mp3"
    
#     if os.path.exists(filename):
#         os.remove(filename)
#     else:
#         pass
#     command = f"ffmpeg -i {file_name} -vn -ar 44100 -ac 2 -b:a 192k output.mp3"
#     os.system(command)
#     return "output.mp3"

# iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")])
# iface.launch()

import gradio as gr
import requests
import urllib.request
from pydub import AudioSegment
import numpy as np
import os
import sys
import wave
import io
import base64
import azure.cognitiveservices.speech as speechsdk

speech_key = os.environ.get("test3")
service_region = os.environ.get("test4")

speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region)
# Note: the voice setting will not overwrite the voice element in input SSML.
speech_config.speech_synthesis_voice_name = os.environ.get("test5")

def function1(prompt):
    response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={
      "data": [
        prompt,
    ]}).json()
    message = response["data"][0]
    speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config)
    result = speech_synthesizer.speak_text_async(message).get()
    if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
        audio_stream = io.BytesIO(result.audio_data)
    
        # Create a wave file object and write the audio data to it
        with wave.open("audio.wav", 'wb') as wave_file:
            wave_file.setnchannels(1)
            wave_file.setsampwidth(2)
            wave_file.setframerate(16000)
            wave_file.writeframesraw(audio_stream.getvalue())
        
        # Use ffmpeg to convert the wave file to an mp3 file
        filename = "output.mp3"
        
        if os.path.exists(filename):
            os.remove(filename)
        else:
            pass
        command = f"ffmpeg -i audio.wav -y -codec:a libmp3lame -qscale:a 2 {filename}"
        os.system(command)
        return "output.mp3"

iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")])
iface.launch()

# import gradio as gr
# import requests
# import json
# import os

# def function2(prompt):
#     response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={
#       "data": [
#         prompt,
#     ]}).json()
#     message = response["data"][0]

#     url = "https://api.dynapictures.com/designs/7c4aba1d73"
#     test6 = os.environ.get("test6")
#     headers = {
#         "Authorization": f"Bearer {test6}",
#         "Content-Type": "application/json"
#     }

#     payload = {
#       "format": "jpeg",
#       "metadata": "some text",
#       "params": [
#         {
#           "name": "bubble",
#           "imageUrl": "https://dynapictures.com/b/rest/public/media/0ceb636a01/images/568b337221.png"
#         },
#         {
#           "name": "quotes",
#           "imageUrl": "https://dynapictures.com/b/rest/public/media/0ceb636a01/images/779f8b9041.png"
#         },
#         {
#           "name": "text",
#           "text": message
#         },
#         {
#           "name": "avatar",
#           "imageUrl": "https://dynapictures.com/b/rest/public/media/0ceb636a01/images/2f7ddd7b55.jpg"
#         },
#         {
#           "name": "name",
#           "text": "JohnAI"
#         },
#         {
#           "name": "title",
#           "text": "Automated"
#         }
#       ]
#     }

#     response = requests.post(url, headers=headers, data=json.dumps(payload))
#     response = response.json()
#     response = response["imageUrl"]
#     return response

# iface = gr.Interface(fn=function2, inputs="text", outputs="text")
# iface.launch()