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from transformers import pipeline
import streamlit as st
from PIL import Image
import requests
from io import BytesIO
# Initialize the pipeline
image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
st.title('Image Captioning Application')
# Function to load images from URL
def load_image_from_url(url):
try:
response = requests.get(url)
img = Image.open(BytesIO(response.content))
return img
except Exception as e:
st.error(f"Error loading image from URL: {e}")
return None
# User option to select input type: Upload or URL
input_type = st.radio("Select input type:", ("Upload Image", "Image URL"))
if input_type == "Upload Image":
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
elif input_type == "Image URL":
image_url = st.text_input("Enter the image URL here:", "")
if image_url:
image = load_image_from_url(image_url)
if image:
st.image(image, caption='Image from URL', use_column_width=True)
# Generate caption button
if st.button('Generate Caption'):
if not image:
st.warning("Please upload an image or enter an image URL.")
else:
with st.spinner("Generating caption..."):
# Process the image and generate caption
if input_type == "Upload Image":
# Save the uploaded image to a temporary file to pass its path to the model
with open("temp_image.jpg", "wb") as f:
f.write(uploaded_file.getbuffer())
result = image_to_text("temp_image.jpg")
elif input_type == "Image URL" and image_url:
result = image_to_text(image_url)
if result:
generated_text = result[0]['generated_text']
st.success(f'Generated Caption: {generated_text}')
else:
st.error("Failed to generate caption.")
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