app.py

import streamlit as st
from PIL import Image
from io import BytesIO
from huggingface_hub import InferenceApi
from transformers import pipeline
from gtts import gTTS
import tempfile

# —––––––– Page Config —–––––––
st.set_page_config(page_title="Magic Story Generator", layout="centered")
st.title("📖✨ Turn Images into Children's Stories")

# —––––––– Clients (cached) —–––––––
@st.cache_resource
def load_clients():
    hf_token = st.secrets["HF_TOKEN"]
    # image captioning client as before
    caption_client = InferenceApi("Salesforce/blip-image-captioning-base", token=hf_token)
    # text-generation pipeline for story
    story_generator = pipeline(
        "text-generation",
        model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
        tokenizer="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
        use_auth_token=hf_token,
        device=0  # or -1 for CPU
    )
    return caption_client, story_generator

caption_client, story_generator = load_clients()

# —––––––– Helper: Generate Caption —–––––––
def generate_caption(img):
    buf = BytesIO()
    img.save(buf, format="JPEG")
    try:
        out = caption_client(data=buf.getvalue())
        return out[0].get("generated_text", "").strip()
    except Exception as e:
        st.error(f"Caption error: {e}")
        return ""

# —––––––– Helper: Generate Story via pipeline —–––––––
def generate_story(prompt: str) -> str:
    # generate up to ~200 tokens to cover 100 words margin
    outputs = story_generator(
        prompt,
        max_new_tokens=200,
        temperature=0.8,
        top_p=0.95,
        repetition_penalty=1.15,
        no_repeat_ngram_size=2,
        do_sample=True
    )
    text = outputs[0]["generated_text"].strip()
    # everything after "Story:" (if you kept that in your prompt)
    if "Story:" in text:
        text = text.split("Story:", 1)[1].strip()
    # truncate to 100 words
    words = text.split()
    if len(words) > 100:
        text = " ".join(words[:100])
        if not text.endswith("."):
            text += "."
    return text

# —––––––– Main App Flow —–––––––
uploaded = st.file_uploader("Upload an image:", type=["jpg", "png", "jpeg"])
if uploaded:
    img = Image.open(uploaded).convert("RGB")
    if max(img.size) > 2048:
        img.thumbnail((2048, 2048))
    st.image(img, use_container_width=True)

    caption = generate_caption(img)
    if not caption:
        st.error("😢 Couldn't understand this image. Try another one!")
        st.stop()
    st.success(f"**Caption:** {caption}")

    story_prompt = (
        f"Image description: {caption}\n\n"
        "Write a 50-100 word children's story that:\n"
        "1. Features the main subject as a friendly character\n"
        "2. Includes a simple adventure or discovery\n"
        "3. Ends with a happy or funny conclusion\n"
        "4. Uses simple language for ages 3-8\n\n"
        "Story:\n"
    )

    with st.spinner("📝 Writing magical story..."):
        story = generate_story(story_prompt)
    st.subheader("📚 Your Magical Story")
    st.write(story)

    # Audio Conversion
    with st.spinner("🔊 Adding story voice..."):
        try:
            tts = gTTS(text=story, lang="en", slow=False)
            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
                tts.save(fp.name)
                st.audio(fp.name, format="audio/mp3")
        except Exception as e:
            st.warning(f"⚠️ Couldn't make audio version: {e}")

# Footer
st.markdown("---\n*Made with ❤️ by your friendly story wizard*")