| import requests | |
| import torch | |
| import streamlit as st | |
| from transformers import pipeline, AutoProcessor, LlavaForConditionalGeneration | |
| from PIL import Image | |
| pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") | |
| # processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") | |
| # model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b") | |
| st.title("Hot Dog? Or Not?") | |
| file_name = st.file_uploader("Upload a hot dog candidate image") | |
| if file_name is not None: | |
| col1, col2 = st.columns(2) | |
| image = Image.open(file_name) | |
| col1.image(image, use_column_width=True) | |
| predictions = pipeline(image) | |
| col2.header("Probabilities") | |
| for p in predictions: | |
| col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%") | |
| # img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' | |
| # raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') | |
| # | |
| # question = "how many dogs are in the picture?" | |
| # inputs = processor(raw_image, question, return_tensors="pt") | |
| # | |
| # out = model.generate(**inputs) | |
| # print(processor.decode(out[0], skip_special_tokens=True).strip()) | |
| # | |
| # model_id = "llava-hf/llava-1.5-7b-hf" | |
| # | |
| # prompt = "USER: <image>\nWhat are these?\nASSISTANT:" | |
| # image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
| # | |
| # model = LlavaForConditionalGeneration.from_pretrained( | |
| # model_id, | |
| # torch_dtype=torch.float16, | |
| # low_cpu_mem_usage=True, | |
| # ).to(0) | |
| # | |
| # processor = AutoProcessor.from_pretrained(model_id) | |
| # | |
| # | |
| # raw_image = Image.open(requests.get(image_file, stream=True).raw) | |
| # inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16) | |
| # | |
| # output = model.generate(**inputs, max_new_tokens=200, do_sample=False) | |
| # print(processor.decode(output[0][2:], skip_special_tokens=True)) |