# import part import streamlit as st from transformers import pipeline # function part # img2text 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"] # Make the caption more fun and descriptive fun_caption = f"Wow! {text.capitalize()}! ๐ŸŒŸ" return fun_caption # text2story def text2story(text): # Use a text-generation model to create a fun and realistic story story_generator = pipeline("text-generation", model="gpt2") # Add a prompt to guide the story generation (avoid magical elements) prompt = f"Write a fun and realistic story for kids based on this: {text}. Keep it simple and under 95 words." story = story_generator(prompt, max_length=95, num_return_sequences=1)[0]["generated_text"] # Ensure the story is simple and fun for kids simple_story = story[:95] # Limit to 95 words return simple_story # text2audio def text2audio(story_text): # Use a TTS model to convert text to audio tts = pipeline("text-to-speech", model="facebook/fastspeech2-en-ljspeech") audio_data = tts(story_text) return audio_data # main part st.set_page_config(page_title="Story Explorer", page_icon="๐Ÿฆœ") st.header("Story Explorer: Turn Your Picture into a Fun Story! ๐ŸŽจ๐Ÿ“–") uploaded_file = st.file_uploader("Choose a picture...", type=["jpg", "png", "jpeg"]) if uploaded_file is not None: bytes_data = uploaded_file.getvalue() with open(uploaded_file.name, "wb") as file: file.write(bytes_data) st.image(uploaded_file, caption="Your Picture", use_container_width=True) # Stage 1: Image to Text st.text('Letโ€™s explore your picture! ๐Ÿงโœจ') scenario = img2text(uploaded_file.name) st.write(f"**Hereโ€™s what I see in your picture:** {scenario}") # Stage 2: Text to Story st.text('Creating a fun story for you! ๐Ÿ“–') story = text2story(scenario) st.write(f"**Hereโ€™s your story:** {story}") # Stage 3: Story to Audio data st.text('Turning your story into audio... ๐ŸŽง') audio_data = text2audio(story) # Play button if st.button("Play Audio"): st.audio(audio_data['audio'], format="audio/wav", start_time=0, sample_rate=audio_data['sampling_rate'])