app.py

# ======================
# Import Section
# ======================

# Core Libraries
import io # Input/output operations for byte streams

# AI/ML Frameworks
from transformers import pipeline  # Hugging Face transformers pipeline
import torch  # PyTorch tensor operations

# Audio Processing
import soundfile as sf  # Audio file I/O operations

# Image Processing
from PIL import Image  # Image manipulation library

# Data Handling
from datasets import load_dataset  # Hugging Face datasets loader

# Web Interface
import streamlit as st  # Web app framework


# ======================
# Model Loading Functions
# ======================

@st.cache_resource
def load_caption_pipeline():
    """Initialize and cache the image captioning pipeline.
    
    Returns:
        Pipeline: BLIP model for image-to-text generation
    """
    return pipeline("image-to-text", model="Salesforce/blip-image-captioning-large",use_fast=True)

@st.cache_resource
def load_story_pipeline():
    """Initialize and cache the story generation pipeline.
    
    Returns:
        Pipeline: Fine-tuned LLaMA model for children's story generation
    """
    return pipeline("text-generation", model="wy2001/storygenratorllama3.21b",use_fast=True)

@st.cache_resource
def load_tts_pipeline():
    """Initialize and cache the text-to-speech pipeline.
    
    Returns:
        Pipeline: Microsoft's SpeechT5 for high-quality speech synthesis
    """
    return pipeline("text-to-speech", model="microsoft/speecht5_tts",use_fast=True)


# ======================
# Core Processing Functions
# ======================

@st.cache_data(show_spinner=False, max_entries=3)
def generate_image_caption(image: Image.Image) -> str:
    """Generate descriptive caption for uploaded image.
    
    Args:
        image (PIL.Image): RGB formatted input image
        
    Returns:
        str: Generated image caption
        
    Raises:
        StreamlitError: If caption generation fails
    """
    try:
        img2caption = load_caption_pipeline()
        # Generate caption
        caption = img2caption(image)[0]['generated_text']
        return caption
    except Exception as e:
        st.error(f"🔍 The caption fairy is confused about the picture!  says: {str(e)}")
        st.stop()

@st.cache_data(show_spinner=False, max_entries=3)
def generate_story(caption: str) -> str:
    """Generate child-friendly story from image caption.
    
    Args:
        caption (str): Image description from previous step
        
    Returns:
        str: Generated story (60-80 words) with happy ending
        
    Raises:
        StreamlitError: If story generation fails
    """
    try:
        messages = [{"role": "user", "content": f"Creating a story for 3-10 years old kids about {caption} between 60 to 80 words with friendly words and happy ending. present the story itself only."},]
        cap2story = load_story_pipeline()
        output = cap2story(messages,max_new_tokens=200,num_return_sequences=1)
        story = output[0]['generated_text'][1]['content']
        return story
    except Exception as e:
        st.error(f"🧚 The writing fairy is sleeping! says: {str(e)}")
        st.stop()



@st.cache_resource
def load_speaker_embeddings():
    """loading the embedding dataset that model required"""
    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
    speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
    return speaker_embeddings

@st.cache_data(show_spinner=False, max_entries=3)
def read_story(story):
    """Convert generated story to speech audio.
    
    Args:
        story (str): Generated story text
        
    Returns:
        io.BytesIO: Audio buffer in WAV format
        
    Raises:
        StreamlitError: If audio generation fails
    """
    try:
        text2speech = load_tts_pipeline()
        audio_data = text2speech(story,forward_params={"speaker_embeddings": load_speaker_embeddings()})
        audio_buffer = io.BytesIO()
        sf.write(audio_buffer, audio_data["audio"], samplerate=audio_data["sampling_rate"],format='WAV')
        audio_buffer.seek(0)
        return audio_buffer
    except Exception as e:
        st.error(f"🔊 The reading fairy is sneezing! says: {str(e)}")
        st.stop()  




# ======================
# Main Application
# ======================

def main():
    """Main application flow and UI configuration."""
    
    # Configure page settings
    st.set_page_config(
    page_title="Magic Story Time",
    page_icon="🧚",
    layout="centered",
    initial_sidebar_state="expanded"
    )

    # Custom CSS styling
    st.markdown("""
    <style>
    .story-box {
    background: linear-gradient(145deg, #fff1eb 0%, #ace0f9 100%);
    border-radius: 15px;
    padding: 25px;
    font-size: 1.1em;
    line-height: 1.8;
    color: #2c3e50;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    margin: 20px 0;
    }
    .upload-section {
    border: 2px dashed #4CAF50;
    border-radius: 10px;
    padding: 20px;
    background: rgba(76, 175, 80, 0.05);
    }
    </style>
    """, unsafe_allow_html=True)

    
    # Sidebar - Image Upload
    with st.sidebar:
        st.header("🖼️ Upload Your Magic Drawing Paper")
        uploaded_image = st.file_uploader(
            label="Upload an image",
            type=["jpg", "jpeg", "png"],
            help="Format in JPEG/JPG/PNG, max 1MB",
            key="image_uploader",
            accept_multiple_files=False
        )

        if uploaded_image:
          st.success(f"🔍 The caption fairy received your image: {uploaded_image.name}")
  
    # Main Content Area
    # App title
    st.title("🧚 Magic Story Camp")
    st.markdown("---")


    # input validation
    if uploaded_image:
        
        # Validate file specifications
        if uploaded_image.size > 1024* 1024:
            st.error("🔍 The caption fairy says the image is too big! please give me image under 1MB")
            st.stop()
        if uploaded_image.type not in ["image/jpeg", "image/png"]:
            st.error("🔍 The caption fairy says only JPG/PNG allowed!")
            st.stop()

        # Processing pipeline
        with st.spinner("🧙 The fairies are casting magic spells, it may take some time⏳..."):
            try:
                # Convert to RGB format for model compatibility
                image = Image.open(uploaded_image).convert("RGB") 

                # Display processing UI elements
                status_display = st.empty()
                progress_bar = st.progress(0)

                # Image preview expander
                with st.expander("view the image", expanded=True):
                    st.image(image, use_container_width=True)
        
                
                # Processing stages
                # Stage 1: Image Captioning
                status_display.markdown("🔍 **The caption fairy is viewing the image...**")
                progress_bar.progress(25)
                caption = generate_image_caption(image)
                
        
                # Stage 2: Story Generation
                status_display.markdown("🧚 **The writing fairy is writing the story...**")
                progress_bar.progress(50)
                story = generate_story(caption)
                
        
                # Stage 3: Audio Synthesis
                status_display.markdown("🔊 **The reading fairy is preparing audio magic...**")
                progress_bar.progress(75)
                speech = read_story(story)
                
        
                # Finish
                progress_bar.progress(100)
                status_display.markdown("🧚 **The Story is ready!**")

                # Display formatted story
                st.markdown("### 📖 Your Magic story")
                st.markdown(f'<div class="story-box">{story}</div>', unsafe_allow_html=True)
        
                # Audio playback and download
                st.audio(speech, format="audio/wav")
                st.download_button(
                      "🎵 Download Story",
                      data=speech,
                      file_name="magic_story.wav",
                      mime="audio/wav",
                      help="click to download your story"
                      )
            except Exception as e:
                st.error(f"💥 The magic spell broke! Please try again. {str(e)}")
                st.stop()
    else:
        # Page instructions
        st.markdown("""
        <div class="upload-section">
          <h3 style="color:#4CAF50; text-align:center;">❓ guidance</h3>
          1. 🖼️ Upload Your Picture in the sidebar<br>
          2. Wait for the magic sparkles may take 10 min ✨）<br>
          3. Read/listen to your story and download with 🎵 button!<br>
          <br>
          Note: First-time model loading may take longer.<br> 
          Please have a glass of juice and be patient for a few moments<br>
        </div>
        """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()