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A Coding Guide to Build a Multimodal Image Captioning App Using Salesforce BLIP Model, Streamlit, Ngrok, and Hugging Face
In this tutorial, we’ll learn how to build an interactive multimodal image-captioning application using Google’s Colab platform, Salesforce’s powerful BLIP model, and Streamlit for an intuitive web interface. Multimodal models, which combine image and text processing capabilities, have become increasingly important in AI applications, enabling tasks like image captioning, visual question answering, and more. This […] The post A Coding Guide to Build a Multimodal Image Captioning App Using Salesforce BLIP Model, Streamlit, Ngrok, and Hugging Face appeared first on MarkTechPost.
In this tutorial, we’ll learn how to build an interactive multimodal image-captioning application using Google’s Colab platform, Salesforce’s powerful BLIP model, and Streamlit for an intuitive web interface. Multimodal models, which combine image and text processing capabilities, have become increasingly important in AI applications, enabling tasks like image captioning, visual question answering, and more. This step-by-step guide ensures a smooth setup, clearly addresses common pitfalls, and demonstrates how to integrate and deploy advanced AI solutions, even without extensive experience.
!pip install transformers torch torchvision streamlit Pillow pyngrokFirst we install transformers, torch, torchvision, streamlit, Pillow, pyngrok, all necessary dependencies for building a multimodal image captioning app. It includes Transformers (for BLIP model), Torch & Torchvision (for deep learning and image processing), Streamlit (for creating the UI), Pillow (for handling image files), and pyngrok (for exposing the app online via Ngrok).
%%writefile app.py
import torch
from transformers import BlipProcessor, BlipForConditionalGeneration
import streamlit as st
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
@st.cache_resource
def load_model():
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
return processor, model
processor, model = load_model()
st.title("
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