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import streamlit as st
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import torch
# Title and description
st.title("Image Captioning App")
st.write("This app converts an uploaded image into a text description using the BLIP model.")
# Load model and processor
@st.cache_resource
def load_model():
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
return processor, model
processor, model = load_model()
# Upload image
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption="Uploaded Image", use_column_width=True)
# Preprocess the image
inputs = processor(image, return_tensors="pt")
# Generate the caption (inference)
generated_ids = model.generate(**inputs)
# Decode the generated caption
generated_text = processor.decode(generated_ids[0], skip_special_tokens=True)
# Display the generated caption
st.write("Generated Caption:")
st.success(generated_text)
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