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import streamlit as st
from PIL import Image
import tempfile
from transformers import pipeline
# --- Stage 1: Image → Caption ---
def generate_caption(image):
caption_pipeline = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
caption = caption_pipeline(image)[0]['generated_text']
return caption
# --- Stage 2: Caption → Story ---
def generate_story(caption):
story_pipeline = pipeline("text-generation", model="gpt2")
prompt = f"Write a fun, short story (50-100 words) for a child based on: {caption}"
story = story_pipeline(prompt, max_length=100, do_sample=True)[0]['generated_text']
return story
# --- Stage 3: Story → Audio ---
def generate_audio(story_text):
tts_pipeline = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech_vits")
speech = tts_pipeline(story_text)
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
f.write(speech["audio"])
return f.name
# --- Streamlit UI ---
def main():
st.title("📖 AI Storyteller for Kids (3 Stages)")
st.write("Upload a child-friendly image and let the app create a story and read it out loud!")
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_image:
image = Image.open(uploaded_image)
st.image(image, caption="Your uploaded image", use_column_width=True)
with st.spinner("🔍 Generating caption..."):
caption = generate_caption(image)
st.success(f"🖼️ Caption: {caption}")
with st.spinner("📝 Generating story..."):
story = generate_story(caption)
st.markdown("### 📚 Generated Story:")
st.write(story)
with st.spinner("🔊 Generating audio..."):
audio_path = generate_audio(story)
st.audio(audio_path, format="audio/wav")
if __name__ == "__main__":
main()
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