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# import part
import streamlit as st
from transformers import pipeline
# function part
# function part
def generate_image_caption(image_path):
"""Generates a caption for the given image using a pre-trained model."""
img2caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
result = img2caption(image_path)
return result[0]['generated_text']
# text2story
def text2story(text):
pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
story_text = pipe(text)[0]['generated_text']
return story_text
def main():
# App title
st.title("Streamlit Demo on Hugging Face")
# Write some text
st.write("Welcome to a demo app showcasing basic Streamlit components!")
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
#Stage 1: Image to Text
st.text('Processing img2text...')
image_caption = generate_image_caption(uploaded_image.name)
st.write(image_caption)
if __name__ == "__main__":
main() |