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| import streamlit as st # Streamlit for building the web application | |
| from transformers import pipeline # Hugging Face Transformers pipeline for models | |
| from PIL import Image # PIL for handling image files | |
| # Function to convert image to text | |
| def img2text(image): | |
| # Load the image-to-text model | |
| image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base") | |
| # Generate a caption for the image | |
| text = image_to_text_model(image)[0]["generated_text"] | |
| return text # Return the generated caption | |
| # Function to generate a story based on the caption | |
| def text2story(text): | |
| # Load the text generation model | |
| story_model = pipeline("text-generation", model="gpt2") | |
| # Generate a story based on the input text | |
| story_text = story_model(f"Once upon a time, {text}.", max_length=100, num_return_sequences=1) | |
| return story_text[0]["generated_text"] # Return the generated story | |
| # Function to convert text to audio | |
| def text2audio(story_text): | |
| # Load the text-to-speech model | |
| text_to_audio_model = pipeline("text-to-speech", model="facebook/mms-tts-eng") | |
| # Generate audio data from the story text | |
| audio_data = text_to_audio_model(story_text) | |
| return audio_data # Return the audio data | |
| # Main part of the application | |
| st.set_page_config(page_title="Your Image to Audio Story", page_icon="🦜") | |
| st.header("Turn Your Image into an Audio Story") | |
| uploaded_file = st.file_uploader("Select an Image...", type=["jpg", "jpeg", "png"]) # File uploader for images | |
| if uploaded_file is not None: | |
| # Open and read the uploaded image | |
| image = Image.open(uploaded_file) # Use PIL to open the uploaded image | |
| st.image(image, caption="Uploaded Image", use_container_width=True) # Display the uploaded image | |
| # Stage 1: Image to Text | |
| st.text('Processing image to text...') | |
| scenario = img2text(image) # Get the caption for the uploaded image | |
| st.write("Caption:", scenario) # Display the generated caption | |
| # Stage 2: Text to Story | |
| st.text('Generating a story...') | |
| story = text2story(scenario) # Generate a story based on the caption | |
| st.write("Story:", story) # Display the generated story | |
| # Stage 3: Story to Audio data | |
| st.text('Generating audio data...') # Inform the user about the audio generation stage | |
| audio_data = text2audio(story) # Convert the generated story into audio | |
| # Play button for the audio | |
| if st.button("Play Audio"): # Create a button to play the audio | |
| st.audio(audio_data['audio'], format="audio/wav", start_time=0, sample_rate=audio_data['sampling_rate']) # Play the audio |