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import streamlit as st
from transformers import pipeline
import textwrap
import numpy as np
import soundfile as sf
import tempfile
import os
# Initialize pipelines
@st.cache_resource
def load_pipelines():
captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
storyer = pipeline("text-generation", model="aspis/gpt2-genre-story-generation")
tts = pipeline("text-to-speech", model="facebook/mms-tts-eng")
return captioner, storyer, tts
captioner, storyer, tts = load_pipelines()
# Main logic
def generate_content(image):
# Generate caption
caption = captioner(image)[0]["generated_text"]
st.write("**Caption:**", caption)
# Generate story
prompt = (
f"Write a funny, warm children's story for ages 3-10, 50–100 words, "
f"in third-person narrative, that describes this scene exactly: {caption} "
f"mention the exact place or venue within {caption}"
)
raw = storyer(
prompt,
max_new_tokens=150,
temperature=0.7,
top_p=0.9,
no_repeat_ngram_size=2,
return_full_text=False
)[0]["generated_text"].strip()
# Trim to max 100 words
words = raw.split()
story = " ".join(words[:100])
st.write("**Story:**", story)
# Convert story to speech
chunks = textwrap.wrap(story, width=200)
audio = np.concatenate([tts(chunk)["audio"].squeeze() for chunk in chunks])
# Save audio to temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
sf.write(temp_file.name, audio, tts.model.config.sampling_rate)
temp_file_path = temp_file.name
return caption, story, temp_file_path
# Streamlit UI
st.title("Image to Children's Story and Audio")
st.write("Upload an image to generate a caption, a children's story, and an audio narration.")
uploaded_image = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
if uploaded_image is not None:
st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
if st.button("Generate Story and Audio"):
with st.spinner("Generating content..."):
caption, story, audio_path = generate_content(uploaded_image)
st.audio(audio_path, format="audio/wav")
# Clean up temporary file
os.remove(audio_path) |