classagm / app.py
Leo Liu
Update app.py
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# import part
from transformers import pipeline
import streamlit as st
# function part
# img2text
def img2text(url):
image_to_text_model = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
text = image_to_text_model(url)[0]["generated_text"]
return text
# text2story
def text2story(text):
story_prompt = (
f"You are an expert in storytelling for children ages 3-10."
f"Based on the image description below:{text}\n"
f"Please create a funny, heartwarming, imaginative fairy tale of no less than 50 words"
f"Output only the body of the story, do not include other content"
)
story_text = pipeline("text-generation", model="distilbert/distilgpt2",
max_length=200,
do_sample=True,
top_p=0.95,
temperature=0.7)
generation = story_text(story_prompt)
story_text = generation[0]['generated_text']
return story_text
# text2audio
def text2audio(story_text):
audio_data = pipeline("text-to-audio", model="facebook/musicgen-medium")
return audio_data
# main part
st.set_page_config(page_title="Your Image to Audio Story",
page_icon="🦜")
st.header("Turn Your Image to Audio Story")
# Upload image here
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_container_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'])
st.audio("kids_playing_audio.wav")