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")

# —––––––– Load & warm models
@st.cache_resource
def load_pipelines():
    # 1) BLIP-base for image captions
    captioner = pipeline(
        "image-to-text",
        model="Salesforce/blip-image-captioning-base",
        device=0
    )
    # 2) Flan-T5-Large for instruction following
    storyteller = pipeline(
        "text2text-generation",
        model="google/flan-t5-large",
        device=0
    )

    # Warm up so first real call is faster
    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()

# —––––––– Main UI
uploaded = st.file_uploader("Upload an image:", type=["jpg","jpeg","png"])
if uploaded:
    # 1) Preprocess + display
    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
    with st.spinner("🔍 Generating caption..."):
        cap = captioner(image)[0]["generated_text"].strip()
    st.markdown(f"**Caption:** {cap}")

    # 3) Story — stronger, clearer prompt
    prompt = (
        f"Here’s an image description: “{cap}”.\n\n"
        "Write a playful, 80–100 word story for 3–10 year-old children.\n"
        "- Focus only on the panda and what it’s doing.\n"
        "- Do not introduce any other characters (no kids, no parents).\n"
        "- Be vivid: mention the panda’s feelings or the crunchy meat.\n\n"
        "Story:"
    )
    with st.spinner("✍️ Writing story..."):
        out = storyteller(
            prompt,
            max_new_tokens=130,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
            top_k=50,
            repetition_penalty=1.3,
            no_repeat_ngram_size=3
        )
        # strip prompt prefix — keep only the generated story
        raw = out[0]["generated_text"]
        story = raw.split("Story:")[-1].strip()

    st.markdown("**Story:**")
    st.write(story)

    # 4) Text-to-Speech (gTTS)
    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")