import streamlit as st
from transformers import pipeline
from gtts import gTTS
import os

# Function: Image to Text
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

# Function: Text to Story (Placeholder)
def text2story(text):
    story_text = text  # Placeholder for now
    return story_text

# Function: Text to Audio
def text2audio(story_text):
    # Convert text to audio using gTTS
    tts = gTTS(story_text, lang="en")
    audio_file = "story_audio.wav"
    tts.save(audio_file)
    return audio_file

# Streamlit App
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_column_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
    st.text('Generating audio data...')
    audio_file = text2audio(story)

    # Play button
    if st.button("Play Audio"):
        st.audio(audio_file, format="audio/wav")