app.py

# Must be FIRST import and FIRST Streamlit command
import streamlit as st
st.set_page_config(
    page_title="Magic Story Generator",
    layout="centered",
    page_icon="📖"
)

# Other imports AFTER Streamlit config
import re
import time
import tempfile
from PIL import Image
from gtts import gTTS
from transformers import pipeline

# --- Constants & Setup ---
st.title("📖✨ Turn Images into Children's Stories")

# --- Model Loading (Cached) ---
@st.cache_resource(show_spinner=False)
def load_models():
    # Image captioning model
    captioner = pipeline(
        "image-to-text",
        model="Salesforce/blip-image-captioning-base",
        device=-1  # Use -1 for CPU, 0 for GPU
    )
    
    # Story generation model (Qwen3-1.7B)
    storyteller = pipeline(
        "text-generation",
        model="Qwen/Qwen3-1.7B",
        device_map="auto",
        trust_remote_code=True,
        torch_dtype="auto",
        max_new_tokens=250,
        temperature=0.7,
        top_p=0.85,
        repetition_penalty=1.15,
        eos_token_id=151645
    )
    
    return captioner, storyteller

caption_pipe, story_pipe = load_models()

# --- Main Application Flow ---
uploaded_image = st.file_uploader(
    "Upload a children's book style image:",
    type=["jpg", "jpeg", "png"]
)

if uploaded_image:
    # Process image
    image = Image.open(uploaded_image).convert("RGB")
    st.image(image, use_container_width=True)

    # Generate caption
    with st.spinner("🔍 Analyzing image..."):
        caption_result = caption_pipe(image)
        image_caption = caption_result[0].get("generated_text", "").strip()
    
    if not image_caption:
        st.error("❌ Couldn't understand this image. Please try another!")
        st.stop()
    
    st.success(f"**Image Understanding:** {image_caption}")

    # Create story prompt
    story_prompt = (
        f"<|im_start|>system\n"
        f"You are a children's book author. Create a 100-150 word story based on: {image_caption}\n"
        "Use simple language, friendly characters, and a positive lesson.<|im_end|>\n"
        f"<|im_start|>user\n"
        f"Write a child-friendly story with a clear beginning, middle, and end.<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )

    # Generate story
    with st.spinner("📝 Crafting magical story..."):
        start_time = time.time()
        story_result = story_pipe(
            story_prompt,
            do_sample=True,
            num_return_sequences=1,
            pad_token_id=151645
        )
        generation_time = time.time() - start_time

    # Process output
    raw_story = story_result[0]['generated_text']
    
    # Clean up story text
    clean_story = raw_story.split("<|im_start|>assistant\n")[-1]
    clean_story = clean_story.split("<|im_start|>")[0]  # Remove any new turns
    clean_story = clean_story.replace("<|im_end|>", "").strip()
    
    # Remove assistant mentions using regex
    clean_story = re.sub(
        r'^(assistant[:>]?\s*)+', 
        '', 
        clean_story, 
        flags=re.IGNORECASE
    ).strip()

    # Format story punctuation
    final_story = []
    for sentence in clean_story.split(". "):
        sentence = sentence.strip()
        if not sentence:
            continue
        if not sentence.endswith('.'):
            sentence += '.'
        final_story.append(sentence[0].upper() + sentence[1:])
    
    final_story = " ".join(final_story).replace("..", ".")[:800]

    # Display story
    st.subheader("✨ Your Magical Story")
    st.write(final_story)

    # Audio conversion
    with st.spinner("🔊 Creating audio version..."):
        try:
            audio = gTTS(text=final_story, lang="en", slow=False)
            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
                audio.save(tmp_file.name)
                st.audio(tmp_file.name, format="audio/mp3")
        except Exception as e:
            st.error(f"❌ Audio conversion failed: {str(e)}")

# Footer
st.markdown("---")
st.markdown("📚 Made with ♥ by The Story Wizard • [Report Issues](https://example.com)")