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import streamlit as st
from transformers import pipeline
# function part
# img2text
def img2text(url):
image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
text = image_to_text_model(url)[0]["generated_text"]
return text
# text2story
def text2story(text):
story_generator = pipeline("text-generation", model="distilgpt2") # Corrected pipeline initialization
story_text = story_generator(text, max_length=150, num_return_sequences=1) # Pass parameters here
return story_text[0]["generated_text"] # Extract generated text
# text2audio
def text2audio(story_text):
tts_model = pipeline("text-to-speech", model="facebook/mms-tts-eng") # Initialize pipeline
audio_data = tts_model(story_text) # Generate audio
return audio_data # Return generated audio
#main part
def main():
st.set_page_config(page_title="Your Image to Audio Story",
page_icon="🦜")
st.header("Turn Your Image to Audio Story")
uploaded_file = st.file_uploader("Select an Image...")
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)
#Stage 1: Image to Text
st.text('Processing img2text...')
scenario = img2text(uploaded_file.name)
st.write(scenario)
#Stage 2: Text to Story
st.text('Generating a story...')
story = text2story(scenario)
st.write(story)
#Stage 3: Story to Audio data
st.text('Generating audio data...')
audio_data =text2audio(story)
# Play button
if st.button("Play Audio"):
st.audio(audio_data['audio'],
format="audio/wav",
start_time=0,
sample_rate = audio_data['sampling_rate'])
if __name__ == "__main__":
main() |