app.py

# app.py

import streamlit as st
from PIL import Image
from transformers import pipeline
from gtts import gTTS
import tempfile

# —––––––– Page config
st.set_page_config(page_title="Storyteller for Kids", layout="centered")
st.title("🖼️ ➡️ 📖 Interactive Storyteller")

# —––––––– Model loading + warm-up
@st.cache_resource
def load_pipelines():
    # 1) Original BLIP-base captioner
    captioner = pipeline(
        "image-to-text",
        model="Salesforce/blip-image-captioning-base",
        device=0  # set to -1 if CPU-only
    )
    # 2) Lightweight GPT-Neo for stories
    storyteller = pipeline(
        "text-generation",
        model="EleutherAI/gpt-neo-125M",
        device=0
    )

    # Warm-up so first real request is fast
    dummy = Image.new("RGB", (384, 384), color=(128, 128, 128))
    captioner(dummy)
    storyteller("Hello", max_new_tokens=1)

    return captioner, storyteller

captioner, storyteller = load_pipelines()

# —––––––– Image upload & processing
uploaded = st.file_uploader("Upload an image:", type=["jpg", "jpeg", "png"])
if uploaded:
    # 1) Load + downsize for faster vision encoding
    image = Image.open(uploaded).convert("RGB")
    image = image.resize((384, 384), Image.LANCZOS)
    st.image(image, caption="Your image", use_container_width=True)

    # 2) Caption step
    with st.spinner("🔍 Generating caption..."):
        cap = captioner(image)[0]["generated_text"].strip()
    st.markdown(f"**Caption:** {cap}")

    # 3) Story generation (greedy for speed)
    prompt = (
        f"Write an 80–100 word playful story for 3–10 year-olds "
        f"based on this description:\n\n“{cap}”\n\nStory:"
    )
    with st.spinner("✍️ Generating story..."):
        out = storyteller(
            prompt,
            max_new_tokens=120,
            do_sample=False
        )
        story = out[0]["generated_text"].strip()
    st.markdown("**Story:**")
    st.write(story)

    # 4) Text-to-Speech via gTTS (network-based)
    with st.spinner("🔊 Converting to speech..."):
        tts = gTTS(text=story, lang="en")
        tmp = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
        tts.write_to_fp(tmp)
        tmp.flush()
    st.audio(tmp.name, format="audio/mp3")