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| import streamlit as st | |
| from transformers import pipeline | |
| from PIL import Image | |
| import os | |
| # function part | |
| # img2text | |
| def img2text(image_path): | |
| try: | |
| # Load the image-to-text model | |
| image_to_text_model = pipeline("image-to-text", model="naver-clova-ix/donut-base") | |
| # Open the image file | |
| image = Image.open(image_path) | |
| # Extract text from the image | |
| result = image_to_text_model(image) | |
| # Get the generated text | |
| text = result[0]["generated_text"] if result else "No text detected" | |
| return text | |
| except Exception as e: | |
| st.error(f"Error processing image: {str(e)}") | |
| return f"Error: {str(e)}" | |
| # text2story | |
| def text2story(text): | |
| # For now, just return the extracted text as the story | |
| # This function can be expanded later with more sophisticated story generation | |
| story_text = f"Here's a story based on the text: {text}" | |
| return story_text | |
| # text2audio | |
| def text2audio(story_text): | |
| try: | |
| # Load the text-to-speech model (using a common TTS pipeline) | |
| # Note: You may need to install additional dependencies depending on the model used | |
| tts_model = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech_vits") | |
| # Generate audio from the story text | |
| audio_data = tts_model(story_text) | |
| return audio_data | |
| except Exception as e: | |
| st.error(f"Error generating audio: {str(e)}") | |
| return None | |
| # main part | |
| st.set_page_config(page_title="Your Image to Audio Story", | |
| page_icon="🦜") | |
| st.header("Turn Your Image to Audio Story") | |
| st.subheader("Using Donut model for text extraction") | |
| uploaded_file = st.file_uploader("Select an Image...", type=['png', 'jpg', 'jpeg', 'gif', 'bmp', 'webp']) | |
| if uploaded_file is not None: | |
| # Save the uploaded file temporarily | |
| bytes_data = uploaded_file.getvalue() | |
| with open(uploaded_file.name, "wb") as file: | |
| file.write(bytes_data) | |
| # Display the uploaded image | |
| st.image(uploaded_file, caption="Uploaded Image", | |
| use_column_width=True) | |
| # Stage 1: Image to Text | |
| with st.spinner('Processing img2text...'): | |
| extracted_text = img2text(uploaded_file.name) | |
| st.subheader("Extracted Text:") | |
| st.write(extracted_text) | |
| # Stage 2: Text to Story | |
| with st.spinner('Generating a story...'): | |
| story = text2story(extracted_text) | |
| st.subheader("Generated Story:") | |
| st.write(story) | |
| # Stage 3: Story to Audio data | |
| with st.spinner('Generating audio data...'): | |
| audio_data = text2audio(story) | |
| # Remove the temporary file | |
| if os.path.exists(uploaded_file.name): | |
| os.remove(uploaded_file.name) | |
| # 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.warning("Audio generation failed. Playing a placeholder audio.") | |
| try: | |
| st.audio("kids_playing_audio.wav") | |
| except FileNotFoundError: | |
| st.error("Placeholder audio file not found. Audio playback is unavailable.") |