Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import torch | |
| import bitsandbytes | |
| import accelerate | |
| import scipy | |
| from PIL import Image | |
| from transformers import Blip2Processor, Blip2ForConditionalGeneration | |
| def load_caption_model(blip2=false, instructblip=True): | |
| if blip2: | |
| processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
| model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True,torch_dtype=torch.float16, device_map="auto") | |
| #model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="auto") | |
| if instructblip: | |
| model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b", load_in_8bit=True,torch_dtype=torch.float16, device_map="auto") | |
| processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b") | |
| return model, processor | |
| def answer_question(image, question, model, processor): | |
| image = Image.open(image).convert('RGB') | |
| inputs = processor(image, question, return_tensors="pt").to("cuda", torch.float16) | |
| out = model.generate(**inputs, max_length=200, min_length=20, num_beams=1) | |
| answer = processor.decode(out[0], skip_special_tokens=True).strip() | |
| return answer | |
| st.title("Image Question Answering") | |
| # File uploader for the image | |
| image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"]) | |
| # Text input for the question | |
| question = st.text_input("Enter your question about the image:") | |
| if st.button("Get Answer"): | |
| if image is not None and question: | |
| # Display the image | |
| st.image(image, use_column_width=True) | |
| # Get and display the answer | |
| model, processor = load_caption_model() | |
| answer = answer_question(image, question, model, processor) | |
| st.write(answer) | |
| else: | |
| st.write("Please upload an image and enter a question.") |