import streamlit as st
from PIL import Image
from io import BytesIO
from huggingface_hub import InferenceApi
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"]
    return (
        InferenceApi("Salesforce/blip-image-captioning-base", token=hf_token),
        InferenceApi("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", token=hf_token)
    )

caption_client, story_client = load_clients()

# —––––––– Helper: Generate Caption —–––––––
def generate_caption(img):
    """
    Runs the BLIP caption model on a PIL.Image and returns the generated text.
    """
    img_bytes = BytesIO()
    img.save(img_bytes, format="JPEG")
    try:
        result = caption_client(data=img_bytes.getvalue())
        if isinstance(result, list) and result:
            return result[0].get("generated_text", "").strip()
        return ""
    except Exception as e:
        st.error(f"Caption generation error: {e}")
        return ""

# —––––––– Helper: Process Image —–––––––
def process_image(uploaded_file):
    try:
        img = Image.open(uploaded_file).convert("RGB")
        if max(img.size) > 2048:
            img.thumbnail((2048, 2048))
        return img
    except Exception as e:
        st.error(f"Image processing error: {e}")
        st.stop()

# —––––––– Main App Flow —–––––––
uploaded = st.file_uploader("Upload an image:", type=["jpg", "png", "jpeg"])
if uploaded:
    img = process_image(uploaded)
    st.image(img, use_container_width=True)

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

    # Prepare Story Prompt
    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"
    )

    # Generate Story
    with st.spinner("📝 Writing magical story..."):
        try:
            story_response = story_client(
                story_prompt,
                parameters={
                    "max_new_tokens":       200,
                    "temperature":          0.8,
                    "top_p":                0.95,
                    "repetition_penalty":   1.15,
                    "do_sample":            True,
                    "no_repeat_ngram_size": 2
                }
            )
            full_text = story_response[0].get("generated_text", "")
            story = full_text.split("Story:")[-1].strip()
            if "." in story:
                story = story.rsplit(".", 1)[0] + "."
        except Exception as e:
            st.error(f"🚨 Story magic failed: {str(e)}")
            st.stop()

    # Display Story
    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("⚠️ Couldn't make audio version: " + str(e))

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