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# 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 time
import os
def function(prompt):
url = os.environ.get("test7")
headers = {os.environ.get("test8")}
data = {
"input": prompt
}
response = requests.post(url, headers=headers, json=data)
response = response.json()
output = response["output"]
trigger1 = os.environ.get("test9")
trigger2 = os.environ.get("test10")
set1 = os.environ.get("test11")
set2 = os.environ.get("test12")
if trigger1 in output and trigger2 in output:
output = re.sub(rf"{trigger2}", ste1, output)
output = re.sub(rf"{trigger1}", set2, output)
print(output)
iface = gr.Interface(fn=function, inputs="text", outputs="text")
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() |