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import sys |
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sys.path.append(".") |
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import streamlit as st |
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import pandas as pd |
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from PIL import Image |
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import time |
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from model_loader import * |
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from datasets import load_dataset |
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df = pd.read_json('vqa_samples.json', orient="columns") |
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model_name = st.sidebar.selectbox( |
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"Select a model: ", |
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('vilt', 'vilt_finetuned', 'git', 'blip', 'vbert') |
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) |
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image_selector_unspecific = st.number_input( |
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"Select an question id: ", |
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0, len(df) |
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) |
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sample = df.iloc[image_selector_unspecific] |
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img_path = sample['img_path'] |
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image = Image.open(f'images/{img_path}.jpg') |
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st.image(image, channels="RGB") |
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question = sample['ques'] |
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label = sample['label'] |
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question = st.text_input(f"Ask the model a question related to the image: \n" |
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f"(e.g. \"{sample['ques']}\")") |
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t_begin = time.perf_counter() |
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args = load_model(model_name) |
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answer = get_answer(args, image, question, model_name) |
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t_end = time.perf_counter() |
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st.text(f"Answer by {model_name}: {answer}") |
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st.text(f"Ground truth (of the example): {label}") |
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st.text(f"Time consumption: {(t_end-t_begin): .4f} s") |
