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from dotenv import find_dotenv, load_dotenv
from transformers import pipeline
import streamlit as st
import os
# load env variables from .env file
load_dotenv(find_dotenv())
# img to text
def img_to_text(url):
image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
text = image_to_text(url)[0]["generated_text"]
return text
# llm
def generate_story(text):
generator = pipeline("text-generation", model="distilgpt2")
result = generator(text, max_length=20, num_return_sequences=1)
return result[0]['generated_text']
#
# text-to-speech
def text_to_speech(text):
import requests
API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
headers = {"Authorization": f"Bearer {os.environ.get('HUGGINGFACE_API_TOKEN')}"}
payload = {
"inputs": text
}
response = requests.post(API_URL, headers=headers, json=payload)
response.raise_for_status()
with open('audio.flac', 'wb') as file:
file.write(response.content)
def main():
st.set_page_config(page_title="img to audio story")
st.header("turn image to audio story")
uploaded_file = st.file_uploader("Choose an image ... ", 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)
text = img_to_text(uploaded_file.name)
story = generate_story(text)
text_to_speech(story)
with st.expander("text"):
st.write(text)
with st.expander("story"):
st.write(story)
st.audio("audio.flac")
main()
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