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"] return text # text2story def text2story(text): text_generation_model = pipeline("text-generation", model="openai-community/gpt2") story_text = text_generation_model(text, min_length=50, max_length=100, do_sample=True, early_stopping=True, top_p=0.4)[0]["generated_text"] return story_text # text2audio def text2audio(story_text): text2audio_model = pipeline("text-to-speech", model="facebook/mms-tts-eng") gen_audio = text2audio_model(story_text) return gen_audio def main(): 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_container_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'], sample_rate = audio_data['sampling_rate']) st.audio(audio_data['audio'], format="audio/wav", start_time=0, sample_rate = audio_data['sampling_rate']) # st.audio("kids_playing_audio.wav") if __name__ == "__main__": main()