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| import streamlit as st | |
| from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| # 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"] | |
| return text | |
| # text2story | |
| def text2story(text): | |
| text_to_story = pipeline("text-generation", model="TheBloke/storytime-13B-GPTQ") | |
| # story_text = "" # to be completed | |
| story_text = text_to_story(text)[0]["generated_text"] | |
| return story_text | |
| # text2audio | |
| def text2audio(story_text): | |
| text_to_audio = pipeline("text-to-audio", model="facebook/musicgen-medium") | |
| audio_data = text_to_audio(story_text) | |
| return audio_data | |
| 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: | |
| 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_column_width=True) | |
| #Stage 1: Image to Text | |
| st.text('Processing img2text...') | |
| scenario = img2text(uploaded_file.name) | |
| st.write(scenario) | |
| #Stage 2: Text to Story | |
| st.text('Generating a story...') | |
| story = text2story(scenario) | |
| 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"): | |
| # st.audio(audio_data['audio'], | |
| # format="audio/wav", | |
| # start_time=0, | |
| # sample_rate = audio_data['sampling_rate']) | |
| # # st.audio("kids_playing_audio.wav") |