# import part
import streamlit as st
from transformers import pipeline
import torch

# function part
# img2text
def img2text(url):
    image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
    text = image_to_text_model(url)[0]["generated_text"]
    # Make the caption simple and fun for kids
    fun_caption = f"Look what we found! 🎨 {text}"
    return fun_caption

# text2story
def text2story(text):
    story_generator = pipeline("text-generation", model="distilgpt2")
    # Generate a story with a maximum of 90 words
    story = story_generator(text, max_length=90, num_return_sequences=1)[0]["generated_text"]
    # Ensure the story does not exceed 90 words
    story = " ".join(story.split()[:90])  # Truncate to 90 words
    # Make the story simple and fun for kids
    fun_story = f"Once upon a time... 🌟 {story}"
    return fun_story

# text2audio
def text2audio(story_text):
    tts_pipeline = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech_vits")
    audio_data = tts_pipeline(story_text)
    return audio_data

# main part
st.set_page_config(page_title="Story Maker", page_icon="🦜")
st.header("Story Maker: Turn Your Picture into a Story!")
uploaded_file = st.file_uploader("Select an Image...")

if uploaded_file is not None:
    bytes_data = uploaded_file.getvalue()
    with open(uploaded_file.name, "wb") as file:
        file.write(bytes_data)

    st.image(uploaded_file, caption="Your Picture", use_container_width=True)

    # Stage 1: Image to Text
    st.text('✨ Discovering what’s in your picture...')
    scenario = img2text(uploaded_file.name)
    st.write(f"Here’s what we found: {scenario}")

    # Stage 2: Text to Story
    st.text('🎭 Creating a fun story for you...')
    story = text2story(scenario)
    st.write(story)

    # Stage 3: Story to Audio data
    st.text('🔊 Turning your story into audio...')
    audio_data = text2audio(story)

    # Play button
    if st.button("Play Audio"):
        st.audio(audio_data['audio'],
                 format="audio/wav",
                 start_time=0,
                 sample_rate=audio_data['sampling_rate'])