Delete app.py

app.py

@@ -1,145 +0,0 @@
-import streamlit as st
-import time
-from datetime import datetime
-from transformers import SpeechT5Processor, SpeechT5ForSpeechToSpeech, SpeechT5HifiGan,SpeechT5ForTextToSpeech
-import numpy as np
-import torch
-from io import StringIO
-# from streamlit_chat import message as st_message
-
-
-html_temp= """
-    <div style="background-color:tomato;padding:10px">
-    <h2 style="color:white;text-align:centre;"> Text-to-Speech </h2>
-    </div>
-    """
-st.markdown(html_temp,unsafe_allow_html=True)
-
-st.markdown(
-   
-    """
-    This is an AI tool. This tool will convert your text into audio. You can also drop you text file here and download the audio file.
-"""
-)
-model = SpeechT5ForTextToSpeech.from_pretrained("speecht5_tts")
-processor = SpeechT5Processor.from_pretrained("speecht5_tts")
-vocoder = SpeechT5HifiGan.from_pretrained("speecht5_hifigan")
-
-speaker_embeddings = np.load("cmu_us_slt_arctic-wav-arctic_a0499.npy")
-speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0)
-
-text = st.text_area("Type your text..")
-st.button("Convert")
-inputs = processor(text=text, return_tensors="pt")
-spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
-with torch.no_grad():
-    speech = vocoder(spectrogram)
-    import soundfile as sf
-    sf.write("speech.wav", speech.numpy(), samplerate=16000)
-    
-audio_file = open('speech.wav', 'rb')
-audio_bytes = audio_file.read()
-st.audio(audio_bytes, format='audio/wav')
-
-
-uploaded_file=st.file_uploader("Upload your text file here",type=['txt'] )
-if uploaded_file is not None:
-    stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
-    #To read file as string:
-    text = stringio.read()
-    st.write(text)
-    
-    st.button("Convert",key=1)
-    inputs = processor(text=text, return_tensors="pt")
-    spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
-    with torch.no_grad():
-        speech = vocoder(spectrogram)
-        import soundfile as sf
-        sf.write("speech.wav", speech.numpy(), samplerate=16000)
-    audio_file = open('speech.wav', 'rb')
-    audio_bytes = audio_file.read()
-    st.audio(audio_bytes, format='audio/wav')
-    
-    
-    
-    
-    
-st.text("Thanks for using")
-            
-if st.button("About"):
-        st.text("Created by Surendra Kumar")
-## footer
-from htbuilder import HtmlElement, div, ul, li, br, hr, a, p, img, styles, classes, fonts
-from htbuilder.units import percent, px
-from htbuilder.funcs import rgba, rgb
-
-
-def image(src_as_string, **style):
-    return img(src=src_as_string, style=styles(**style))
-
-
-def link(link, text, **style):
-    return a(_href=link, _target="_blank", style=styles(**style))(text)
-
-
-def layout(*args):
-    style = """
-    <style>
-      # MainMenu {visibility: hidden;}
-      footer {visibility: hidden;}
-     .stApp { bottom: 105px; }
-    </style>
-    """
-
-    style_div = styles(
-        position="fixed",
-        left=0,
-        bottom=0,
-        margin=px(0, 0, 0, 0),
-        width=percent(100),
-        color="black",
-        text_align="center",
-        height="auto",
-        opacity=1
-    )
-
-    style_hr = styles(
-        display="block",
-        margin=px(8, 8, "auto", "auto"),
-        border_style="solid",
-        border_width=px(0.5)
-    )
-
-    body = p()
-    foot = div(
-        style=style_div
-    )(
-        hr(
-            style=style_hr
-        ),
-        body
-    )
-    st.markdown(style,unsafe_allow_html=True)
-
-    for arg in args:
-        if isinstance(arg, str):
-            body(arg)
-
-        elif isinstance(arg, HtmlElement):
-            body(arg)
-
-    st.markdown(str(foot), unsafe_allow_html=True)
-
-
-def footer():
-    myargs = [
-        "©️ surendraKumar",
-        br(),
-        link("https://www.linkedin.com/in/surendra-kumar-51802022b", image('https://icons.getbootstrap.com/assets/icons/linkedin.svg') ),
-        br(),
-        link("https://www.instagram.com/im_surendra_dhaka/",image('https://icons.getbootstrap.com/assets/icons/instagram.svg')),
-    ]
-    layout(*myargs)
-
-if __name__ == "__main__":
-    footer()