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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() |