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| import streamlit as st | |
| from transformers import pipeline | |
| from gtts import gTTS | |
| import os | |
| def generate_caption(image): | |
| # Load the image captioning model | |
| caption_model = pipeline("image-to-text", model="facebook/blip-image-captioning-base") | |
| # Generate the caption for the uploaded image | |
| caption = caption_model(image)[0]["generated_text"] | |
| return caption | |
| def generate_story(caption): | |
| # Load the text generation model | |
| text_generation_model = pipeline("text-generation", model="gpt2") | |
| # Generate the story based on the caption | |
| story = text_generation_model(caption, max_length=200, num_return_sequences=1)[0]["generated_text"] | |
| return story | |
| def convert_to_audio(story): | |
| # Convert the story to audio using gTTS | |
| tts = gTTS(text=story, lang="en") | |
| tts.save("story_audio.mp3") | |
| def main(): | |
| st.title("Storytelling Application") | |
| # File uploader for the image | |
| uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_image is not None: | |
| # Display the uploaded image | |
| st.image(uploaded_image, caption="Uploaded Image", use_column_width=True) | |
| # Generate the caption for the image | |
| caption = generate_caption(uploaded_image) | |
| st.subheader("Generated Caption:") | |
| st.write(caption) | |
| # Generate the story based on the caption | |
| story = generate_story(caption) | |
| st.subheader("Generated Story:") | |
| st.write(story) | |
| # Convert the story to audio | |
| convert_to_audio(story) | |
| # Display the audio player | |
| audio_file = open("story_audio.mp3", "rb") | |
| audio_bytes = audio_file.read() | |
| st.audio(audio_bytes, format="audio/mp3") | |
| if __name__ == "__main__": | |
| main() |