# 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 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()