app.py

import io
import wave
import streamlit as st
from transformers import pipeline
from PIL import Image
import numpy as np
import time
import threading

# ——— 1) MODEL LOADING (cached) ————————————————
# Cache the model loading to avoid reloading on every rerun, improving performance
@st.cache_resource
def get_image_captioner(model_name="Salesforce/blip-image-captioning-base"):
    # Load the image-to-text model for generating captions from images
    return pipeline("image-to-text", model=model_name, device="cpu")

@st.cache_resource
def get_story_pipe(model_name="google/flan-t5-base"):
    # Load the text-to-text model for generating stories from captions
    return pipeline("text2text-generation", model=model_name, device="cpu")

@st.cache_resource
def get_tts_pipe(model_name="facebook/mms-tts-eng"):
    # Load the text-to-speech model for converting stories to audio
    return pipeline("text-to-speech", model=model_name, device="cpu")

# ——— 2) TRANSFORM FUNCTIONS ————————————————
def part1_image_to_text(pil_img, captioner):
    # Generate a caption for the input image using the captioner model
    results = captioner(pil_img)
    # Extract the generated caption, return empty string if no result
    return results[0].get("generated_text", "") if results else ""

def part2_text_to_story(
    caption: str,
    story_pipe,
    target_words: int = 100,
    max_length: int = 100,
    min_length: int = 80,
    do_sample: bool = True,
    top_k: int = 100,
    top_p: float= 0.9,
    temperature: float= 0.7,
    repetition_penalty: float = 1.1,
    no_repeat_ngram_size: int = 4
) -> str:
    # Create a prompt instructing the model to write a story based on the caption
    prompt = (
        f"Write a vivid, imaginative short story of about {target_words} words "
        f"describing this scene: {caption}"
    )
    # Generate the story using the text-to-text model with specified parameters
    out = story_pipe(
        prompt,
        max_length=max_length,  # Maximum length of generated text
        min_length=min_length,  # Minimum length to ensure sufficient content
        do_sample=do_sample,    # Enable sampling for creative output
        top_k=top_k,            # Consider top-k tokens for sampling
        top_p=top_p,            # Use nucleus sampling for diversity
        temperature=temperature, # Control randomness of output
        repetition_penalty=repetition_penalty, # Penalize repeated phrases
        no_repeat_ngram_size=no_repeat_ngram_size, # Prevent repeating n-grams
        early_stopping=False    # Continue until max_length is reached
    )
    # Extract the generated text and clean it
    raw = out[0].get("generated_text", "").strip()
    if not raw:
        return ""
    # Remove the prompt if it appears in the output
    if raw.lower().startswith(prompt.lower()):
        story = raw[len(prompt):].strip()
    else:
        story = raw
    # Truncate at the last full stop for a natural ending
    idx = story.rfind(".")
    if idx != -1:
        story = story[:idx+1]
    return story

def part3_text_to_speech_bytes(text: str, tts_pipe) -> bytes:
    # Convert the input text to audio using the text-to-speech model
    out = tts_pipe(text)
    if isinstance(out, list):
        out = out[0]
    # Extract audio data (numpy array) and sampling rate
    audio_array = out["audio"] # np.ndarray (channels, samples)
    rate = out["sampling_rate"] # int
    # Transpose audio array if it has multiple channels
    data = audio_array.T if audio_array.ndim == 2 else audio_array
    # Convert audio to 16-bit PCM format for WAV compatibility
    pcm = (data * 32767).astype(np.int16)

    # Create a WAV file in memory
    buffer = io.BytesIO()
    wf = wave.open(buffer, "wb")
    channels = 1 if data.ndim == 1 else data.shape[1] # Set mono or stereo
    wf.setnchannels(channels)
    wf.setsampwidth(2) # 2 bytes for 16-bit audio
    wf.setframerate(rate) # Set sampling rate
    wf.writeframes(pcm.tobytes()) # Write audio data
    wf.close()
    buffer.seek(0) # Reset buffer to start for reading
    return buffer.read() # Return WAV bytes

# ——— 3) STREAMLIT UI ————————————————————————————
# Configure the Streamlit page for a kid-friendly, centered layout
st.set_page_config(
    page_title="Picture to Story Magic",
    page_icon="✨",
    layout="centered"
)

# Apply custom CSS for a colorful, engaging, and readable interface
st.markdown("""
<style>
.main {
    background-color: #e6f3ff; /* Light blue background for main area */
    padding: 20px;
    border-radius: 15px;
}
.stButton>button {
    background-color: #ffcccb; /* Pink button background */
    button-color: #000000;
    border-radius: 10px;
    border: 2px solid #ff9999; /* Red border */
    font-size: 18px;
    font-weight: bold;
    padding: 10px 20px;
    transition: all 0.3s; /* Smooth hover effect */
}
.stButton>button:hover {
    background-color: #ff9999; /* Darker pink on hover */
    color: #ffffff;
    transform: scale(1.05); /* Slight zoom on hover */
}
.stFileUploader {
    background-color: #ffb300; /* Orange uploader background */
    border: 2px dashed #ff8c00; /* Dashed orange border */
    border-radius: 10px;
    padding: 10px;
}
.stFileUploader div[role="button"] {
    background-color: #f0f0f0; /* Light gray button */
    border-radius: 10px;
    padding: 10px;
}
.stFileUploader div[role="button"] > div {
    color: #000000 !important; /* Black text for readability */
    font-size: 16px;
}
.stFileUploader button {
    background-color: #ffca28 !important; /* Yellow button */
    color: #000000 !important;
    border-radius: 8px !important;
    border: 2px solid #ffb300 !important; /* Orange border */
    padding: 5px 15px !important;
    font-weight: bold !important;
    box-shadow: 0 2px 4px rgba(0,0,0,0.2) !important; /* Subtle shadow */
}
.stFileUploader button:hover {
    background-color: #ff8c00 !important; /* Orange on hover */
    color: #000000 !important;
}
.stImage {
    border: 3px solid #81c784; /* Green border for images */
    border-radius: 10px;
    box-shadow: 0 4px 8px rgba(0,0,0,0.1); /* Soft shadow */
}
.section-header {
    background-color: #b3e5fc; /* Light blue header background */
    padding: 10px;
    border-radius: 10px;
    text-align: center;
    font-size: 24px;
    font-weight: bold;
    color: #000000;
    margin-bottom: 10px;
}
.caption-box, .story-box {
    background-color: #f0f4c3; /* Light yellow for text boxes */
    padding: 15px;
    border-radius: 10px;
    border: 2px solid #d4e157; /* Green-yellow border */
    margin-bottom: 20px;
    color: #000000;
}
.caption-box b, .story-box b {
    color: #000000; /* Black for bold text */
}
.stProgress > div > div {
    background-color: #81c784; /* Green progress bar */
}
</style>
""", unsafe_allow_html=True)

# Display the main title with a fun, magical theme
st.markdown("<div class='section-header'>Picture to Story Magic! ✨</div>", unsafe_allow_html=True)

# Image upload section
with st.container():
    # Prompt user to upload an image
    st.markdown("<div class='section-header'>1️⃣ Pick a Fun Picture! 🖼️</div>", unsafe_allow_html=True)
    uploaded = st.file_uploader("Choose a picture to start the magic! 😊", type=["jpg","jpeg","png"])
    if not uploaded:
        # Stop execution if no image is uploaded, with a friendly message
        st.info("Upload a picture, and let's make a story! 🎉")
        st.stop()

# Display the uploaded image
with st.spinner("Looking at your picture..."):
    pil_img = Image.open(uploaded)
    st.image(pil_img, use_container_width=True) # Show image scaled to container

# Caption generation section
with st.container():
    st.markdown("<div class='section-header'>2️⃣ What's in the Picture? 🧐</div>", unsafe_allow_html=True)
    captioner = get_image_captioner() # Load captioning model
    progress_bar = st.progress(0) # Initialize progress bar
    result = [None] # Store caption result
    def run_caption():
        # Run captioning in a separate thread to avoid blocking UI
        result[0] = part1_image_to_text(pil_img, captioner)
    with st.spinner("Figuring out what's in your picture..."):
        thread = threading.Thread(target=run_caption)
        thread.start()
        # Simulate progress for ~5 seconds
        for i in range(100):
            progress_bar.progress(i + 1)
            time.sleep(0.05)
        thread.join() # Wait for captioning to complete
    progress_bar.empty() # Clear progress bar
    caption = result[0]
    # Display the generated caption in a styled box
    st.markdown(f"<div class='caption-box'><b>Picture Description:</b><br>{caption}</div>", unsafe_allow_html=True)

# Story and audio generation section
with st.container():
    st.markdown("<div class='section-header'>3️⃣ Your Story and Audio! 🎵</div>", unsafe_allow_html=True)
    # Story generation
    story_pipe = get_story_pipe() # Load story model
    progress_bar = st.progress(0)
    result = [None] # Store story result
    def run_story():
        # Generate story in a separate thread
        result[0] = part2_text_to_story(caption, story_pipe)
    with st.spinner("Writing a super cool story..."):
        thread = threading.Thread(target=run_story)
        thread.start()
        # Simulate progress for ~7 seconds
        for i in range(100):
            progress_bar.progress(i + 1)
            time.sleep(0.07)
        thread.join()
    progress_bar.empty()
    story = result[0]
    # Display the generated story in a styled box
    st.markdown(f"<div class='story-box'><b>Your Cool Story! 📚</b><br>{story}</div>", unsafe_allow_html=True)

    # Text-to-speech conversion
    tts_pipe = get_tts_pipe() # Load TTS model
    progress_bar = st.progress(0)
    result = [None] # Store audio result
    def run_tts():
        # Generate audio in a separate thread
        result[0] = part3_text_to_speech_bytes(story, tts_pipe)
    with st.spinner("Turning your story into sound..."):
        thread = threading.Thread(target=run_tts)
        thread.start()
        # Simulate progress for ~10 seconds
        for i in range(100):
            progress_bar.progress(i + 1)
            time.sleep(0.10)
        thread.join()
    progress_bar.empty()
    audio_bytes = result[0]
    # Play the generated audio in the UI
    st.audio(audio_bytes, format="audio/wav")