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| # import part | |
| import streamlit as st | |
| from transformers import pipeline | |
| # function part | |
| # image2text | |
| def img2text(img): | |
| image_to_text_model = pipeline("image-to-text", | |
| model="nlpconnect/vit-gpt2-image-captioning") | |
| text = image_to_text_model(img)[0]["generated_text"] | |
| return text | |
| # text2story | |
| def text2story(text): | |
| text_generation_model = pipeline("text-generation", | |
| model="openai-community/gpt2") | |
| story_text = f"Once upon a time in a land far, far away, {text}" | |
| generated_story = text_generation_model(story_text, | |
| max_length=100, | |
| num_return_sequences=1) | |
| return generated_story[0]['generated_text'] | |
| # text2audio | |
| def text2audio(story_text): | |
| text_to_speech_model = pipeline("text-to-speech", model="facebook/mms-tts-eng") | |
| speech_output = text_to_speech_model(story_text) | |
| return speech_output | |
| # main part | |
| st.title("Storytelling Application") | |
| st.write("🎉Transfer an image to a short story with audio.🎉---From ✨LING Yunhan 21010943 ISOM5240 (L2)✨") | |
| uploaded_file = st.file_uploader("Choose an image📷...", type=["jpg", "png", "jpeg"]) | |
| 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_container_width=True) | |
| # stage 1 | |
| st.text('Generating caption✍...') | |
| scenario = img2text(uploaded_file.name) | |
| st.write(scenario) | |
| # stage 2 | |
| st.text('Generating story📚...') | |
| generated_story = text2story(scenario) | |
| # Use the scenario from img2text | |
| st.write(generated_story) | |
| # stage 3 | |
| st.text('Generating audio💽...') | |
| audio_data = text2audio(generated_story) | |
| if st.button("Play Audio🍩"): | |
| st.audio(audio_data['audio'], | |
| format="audio/wav", | |
| start_time=0, | |
| sample_rate=audio_data['sampling_rate']) |