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import streamlit as st
import torch
import bitsandbytes
import accelerate
import scipy
from PIL import Image
import torch.nn as nn
from transformers import Blip2Processor, Blip2ForConditionalGeneration, InstructBlipProcessor, InstructBlipForConditionalGeneration
from My_Model.object_detection import ObjectDetector
def load_caption_model(blip2=False, instructblip=True):
if blip2:
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True,torch_dtype=torch.float16)
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True,torch_dtype=torch.float16)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.to('cuda')
#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)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.to('cuda')
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b", load_in_8bit=True,torch_dtype=torch.float16)
return model, processor
def answer_question(image, question, model, processor):
image = Image.open(image)
inputs = processor(image, question, return_tensors="pt").to("cuda", torch.float16)
if isinstance(model, torch.nn.DataParallel):
# Use the 'module' attribute to access the original model
out = model.module.generate(**inputs, max_length=100, min_length=20)
else:
out = model.generate(**inputs, max_length=100, min_length=20)
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.")
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