app.py

# import part
import streamlit as st
from transformers import pipeline
import os
import numpy as np
import io
import scipy.io.wavfile as wavfile

# function part
# img2text
def img2text(image_path):
    image_to_text = pipeline("image-to-text", model="sooh-j/blip-image-captioning-base")
    text = image_to_text(image_path)[0]["generated_text"]
    return text

# text2story
def text2story(text):
    # Using a smaller text generation model
    generator = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
    
    # Create a prompt for the story generation
    prompt = f"Write a fun children's story based on this: {text}. Once upon a time, "
    
    # Generate the story
    story_result = generator(
        prompt,
        max_length=150,
        num_return_sequences=1,
        temperature=0.7,
        top_k=50,
        top_p=0.95,
        do_sample=True
    )
   
    # Extract the generated text
    story_text = story_result[0]['generated_text']
    story_text = story_text.replace(prompt, "Once upon a time, ")
    
    # Make sure the story is at least 100 words
    words = story_text.split()
    if len(words) > 100:
        # Simply truncate to 100 words
        story_text = " ".join(words[:100])
    
    return story_text

# text2audio - REVISED to use facebook/mms-tts-eng model
def text2audio(story_text):
    try:
        # Use a smaller and more reliable TTS model
        synthesizer = pipeline("text-to-speech", model="facebook/mms-tts-eng")
        
        # Break the text into smaller chunks if needed (prevent timeout)
        max_chunk_size = 200  # characters
        chunks = []
        
        for i in range(0, len(story_text), max_chunk_size):
            chunk = story_text[i:i+max_chunk_size]
            # Make sure we break at word boundaries
            if i+max_chunk_size < len(story_text) and story_text[i+max_chunk_size] != ' ':
                # Find the last space in this chunk
                last_space = chunk.rfind(' ')
                if last_space != -1:
                    chunk = chunk[:last_space]
            
            chunks.append(chunk)
        
        # Process each chunk
        audio_arrays = []
        sampling_rate = None
        
        for chunk in chunks:
            if not chunk.strip():  # Skip empty chunks
                continue
                
            speech = synthesizer(chunk)
            if sampling_rate is None:
                sampling_rate = speech["sampling_rate"]
            
            audio_arrays.append(speech["audio"])
        
        # Combine all audio chunks
        combined_audio = np.concatenate(audio_arrays)
        
        # Create a BytesIO object to store the wave file
        wav_buffer = io.BytesIO()
        wavfile.write(wav_buffer, sampling_rate, combined_audio)
        wav_buffer.seek(0)  # Rewind the buffer
        
        return {
            "audio": wav_buffer.getvalue(),
            "sampling_rate": sampling_rate
        }
        
    except Exception as e:
        st.error(f"Error generating audio: {str(e)}")
        # Fallback to a pre-recorded audio file if available
        try:
            with open("fallback_audio.wav", "rb") as f:
                return {
                    "audio": f.read(),
                    "sampling_rate": 22050  # Common sample rate
                }
        except:
            return None

# Function to save temporary image file
def save_uploaded_image(uploaded_file):
    if not os.path.exists("temp"):
        os.makedirs("temp")
    
    image_path = os.path.join("temp", uploaded_file.name)
    
    with open(image_path, "wb") as f:
        f.write(uploaded_file.getvalue())
    
    return image_path

# 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:
    # Display the uploaded image
    st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)
    
    # Save the image temporarily
    image_path = save_uploaded_image(uploaded_file)
    
    # Stage 1: Image to Text
    st.text('Processing img2text...')
    caption = img2text(image_path)
    st.write(caption)
    
    # Stage 2: Text to Story
    st.text('Generating a story...')
    story = text2story(caption)
    st.write(story)
    
    # Stage 3: Story to Audio data
    st.text('Generating audio data...')
    audio_data = text2audio(story)
    
    # Play button
    if st.button("Play Audio"):
        if audio_data:
            st.audio(
                audio_data["audio"],
                format="audio/wav",
                start_time=0,
                sample_rate=audio_data["sampling_rate"]
            )
        else:
            st.error("Failed to generate audio. Please try again.")
    
    # Clean up the temporary file
    try:
        os.remove(image_path)
    except:
        pass