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| import json | |
| import matplotlib.pyplot as plt | |
| import nmslib | |
| import numpy as np | |
| import os | |
| import streamlit as st | |
| from PIL import Image | |
| from transformers import CLIPProcessor, FlaxCLIPModel | |
| def load_index(image_vector_file): | |
| filenames, image_vecs = [], [] | |
| fvec = open(image_vector_file, "r") | |
| for line in fvec: | |
| cols = line.strip().split('\t') | |
| filename = cols[0] | |
| image_vec = np.array([float(x) for x in cols[1].split(',')]) | |
| filenames.append(filename) | |
| image_vecs.append(image_vec) | |
| V = np.array(image_vecs) | |
| index = nmslib.init(method='hnsw', space='cosinesimil') | |
| index.addDataPointBatch(V) | |
| index.createIndex({'post': 2}, print_progress=True) | |
| return filenames, index | |
| def load_model(model_path, baseline_model): | |
| model = FlaxCLIPModel.from_pretrained(model_path) | |
| # processor = CLIPProcessor.from_pretrained(baseline_model) | |
| processor = CLIPProcessor.from_pretrained(model_path) | |
| return model, processor | |
| def load_captions(caption_file): | |
| image2caption = {} | |
| with open(caption_file, "r") as fcap: | |
| for line in fcap: | |
| data = json.loads(line.strip()) | |
| filename = data["filename"] | |
| captions = data["captions"] | |
| image2caption[filename] = captions | |
| return image2caption | |