# import part
from transformers import pipeline
import streamlit as st


#function part
# imgtext
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"]
    return text

# text2story
def text2story(text):
    modified_text = f"This is a children story about {text}"
    pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
    story_text = pipe(modified_text, max_length=100, truncation=True)[0]['generated_text']  # Limit story length
    return story_text

# text2audio
def text2audio(story_text):
    pipe = pipeline("text-to-audio", model="Matthijs/mms-tts-eng")
    audio_data = pipe(story_text)
    return audio_data

# main part

st.set_page_config(page_title="Your Image to Audio Story",
                   page_icon="🦜")
st.header("Turn Your Image to Audio Story")
uploaded_file = st.file_uploader("Select an Image...")

if uploaded_file is not None:
    print(uploaded_file)
    bytes_data = uploaded_file.getvalue()
    with open(uploaded_file.name, "wb") as file:
        file.write(bytes_data)
    st.image(uploaded_file, caption="Uploaded Image",
             use_container_width=True)

    #Stage 1: Image to Text
    st.text('Processing img2text...')
    scenario = img2text(uploaded_file.name)
    st.write(scenario)

    #Stage 2: Text to Story
    st.text('Generating a story...')
    story = text2story(scenario)
    st.write(story)

    #Stage 3: Story to Audio data
    st.text('Generating audio data...')
    audio_data =text2audio(story)

    #Play audio after generation
    st.audio(audio_data['audio'],
                    format="audio/wav",
                    start_time=0,
                    sample_rate = audio_data['sampling_rate'])