import streamlit as st
from PIL import Image
from io import BytesIO
from huggingface_hub import InferenceApi
from gtts import gTTS
import requests
import tempfile
import time
import threading

# —––––––– 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"]
    caption_client = InferenceApi("Salesforce/blip-image-captioning-base", token=hf_token)

    # Start background keep-alive for story model to avoid cold starts
    api_url = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
    headers = {"Authorization": f"Bearer {hf_token}"}
    keep_alive_payload = {
        "inputs": "Hello!",
        "parameters": {"max_new_tokens": 1}
    }
    def keep_model_warm():
        # Initial warm-up
        try:
            requests.post(api_url, headers=headers, json=keep_alive_payload, timeout=10)
        except:
            pass
        # Periodic keep-alive every 10 minutes
        while True:
            time.sleep(600)
            try:
                requests.post(api_url, headers=headers, json=keep_alive_payload, timeout=10)
            except:
                pass
    threading.Thread(target=keep_model_warm, daemon=True).start()

    return caption_client, hf_token

caption_client, hf_token = load_clients()

# —––––––– Helper: Generate Caption —–––––––
def generate_caption(img):
    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()
    except Exception as e:
        st.error(f"Caption generation error: {type(e).__name__}: {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: {type(e).__name__}: {e}")
        st.stop()

# —––––––– Helper: Generate Story —–––––––
def generate_story(prompt: str) -> str:
    api_url = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
    headers = {"Authorization": f"Bearer {hf_token}"}
    payload = {
        "inputs": 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
        }
    }
    max_retries = 5
    retries = 0
    while True:
        try:
            resp = requests.post(api_url, headers=headers, json=payload, timeout=30)
        except Exception as e:
            st.error(f"🚨 Story magic failed: {type(e).__name__}: {e}")
            st.stop()

        if resp.status_code == 200:
            data = resp.json()
            if isinstance(data, list) and data:
                text = data[0].get("generated_text", "").strip()
                story = text.split("Story:")[-1].strip()
                if "." in story:
                    story = story.rsplit(".", 1)[0] + "."
                return story
            else:
                st.error("🚨 Story magic failed: invalid response format")
                st.stop()

        if resp.status_code == 503 and retries < max_retries:
            try:
                wait = int(resp.json().get("estimated_time", 5))
            except:
                wait = 5 * (2 ** retries)
            st.info(f"Model loading; retrying in {wait}s (attempt {retries+1}/{max_retries})")
            time.sleep(wait)
            retries += 1
            continue

        st.error(f"🚨 Story magic failed: HTTP {resp.status_code} - {resp.text}")
        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 and Display Story
    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: {type(e).__name__}: {e}")

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