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from dotenv import find_dotenv, load_dotenv
from transformers import pipeline
import requests
import os
import streamlit as st
load_dotenv(find_dotenv())
api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
#img2text
def img2text(url):
image_to_text = pipeline("image-to-text",model='Salesforce/blip-image-captioning-large')
text = image_to_text(url)[0]["generated_text"]
#print(text)
return text
#
#text2speech
def text2speech(message):
API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
#API_URL = "https://api-inference.huggingface.co/models/microsoft/speecht5_tts"
headers = {"Authorization": f"Bearer {api_token}"}
payloads = {
"inputs":message
}
response = requests.post(API_URL, headers=headers, json=payloads)
with open('audio.flac','wb') as file:
file.write(response.content)
def main():
st.title("Image to text to audio by 🤖")
st.header("Turn image to audio podcast !!!")
st.caption("Sample picture...")
st.image("beachboat.jpg")
img2text("beachboat.jpg")
uploaded_file = st.file_uploader("Choose your image or simpley drag sample image given above",type="jpg")
if uploaded_file is not None:
print(uploaded_file)
bytes_data = uploaded_file.getvalue()
with open(uploaded_file.name,"wb")as file:
file.write(bytes_data)
st.image(uploaded_file,caption='Uploaded image.',
use_column_width=True)
scenario = img2text(uploaded_file.name)
text2speech(scenario)
with st.expander("Scenario"):
st.write(scenario)
st.audio("audio.flac")
if __name__ == '__main__':
main()
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