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import streamlit as st
from transformers import pipeline
from gtts import gTTS
import os
from PIL import Image
# Load models
def load_models():
image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
storyteller = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B", max_new_tokens=200)
return image_to_text, storyteller
# Process image to text
def generate_caption(image, image_to_text):
result = image_to_text(image)
return result[0]["generated_text"] if result else "No caption generated."
# Generate a narrative story
def generate_story(text, storyteller):
prompt = f"Write a short, engaging story based on this image description: {text}"
story = storyteller(prompt, do_sample=True, temperature=0.7, max_new_tokens=200)
return story[0]["generated_text"] if story else "No story generated."
# Convert text to speech
def text_to_speech(text, filename="output.mp3"):
tts = gTTS(text)
tts.save(filename)
return filename
# Main Streamlit app
def main():
st.title("AI-Powered Image Captioning and Storytelling")
image_to_text, storyteller = load_models()
uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
# Convert uploaded file to a PIL image
image = Image.open(uploaded_file)
# Display the uploaded image
st.image(image, caption="Uploaded Image", use_column_width=True)
with st.spinner("Generating caption..."):
caption = generate_caption(image, image_to_text)
st.write("### Image Caption:")
st.write(caption)
with st.spinner("Generating story..."):
story = generate_story(caption, storyteller)
st.write("### Generated Story:")
st.write(story)
with st.spinner("Generating speech..."):
audio_file = text_to_speech(story)
st.audio(audio_file, format="audio/mp3")
if __name__ == "__main__":
main()
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