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import streamlit as st
from transformers import pipeline
from responsivevoice import ResponsiveVoice
from PIL import Image
from diffusers import StableDiffusionPipeline
# Text-to-speech model
tts_model = ResponsiveVoice()
# Text-to-image pipeline
sd_pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
# Story generation model
generator = pipeline("text-generation", model="gpt2")
def generate_story(prompt):
"""
Generates a story based on user input.
Args:
prompt: A string containing the starting prompt for the story.
Returns:
A string containing the generated story.
"""
story = generator(prompt, max_length=1024)[0]["generated_text"]
return story
def generate_image(prompt):
"""
Generates an image based on user input.
Args:
prompt: A string containing the description for the image.
Returns:
A PIL Image object.
"""
image = sd_pipeline(prompt).images[0]
return image
# Streamlit app
st.title("Hugging Face Storytelling App")
# Prompt input
prompt = st.text_input("Start your story with...")
# Text-to-speech button
if st.button("Speak the Story"):
with st.spinner("Speaking the story..."):
tts_model.speak(generate_story(prompt))
# Text-to-text button
if st.button("Generate Text from Prompt"):
with st.spinner("Generating text..."):
new_text = generate_story(prompt)
st.write(new_text)
# Text-to-image button
if st.button("Generate Image from Prompt"):
with st.spinner("Generating image..."):
image = generate_image(prompt)
st.image(image)
# Questions to answer
st.write("**Think about your story and answer these questions:**")
st.text_input("What is the main character's name?")
st.text_input("Where does the story take place?")
st.text_input("What is the problem the character faces?")
st.text_input("How does the character solve the problem?")
# Disclaimer
st.write("* This app is still under development and may not always generate accurate or coherent results.")
st.write("* Please be mindful of the content generated by the AI models.")
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