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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 π€") | |
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() | |