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import streamlit as st | |
st.set_page_config(page_title='ITR', page_icon="π§", layout='centered') | |
st.title("LCM-Independent for Pascal Dataset") | |
# import faiss | |
# import numpy as np | |
# from PIL import Image | |
# import json | |
# import zipfile | |
# import pickle | |
# from transformers import AutoTokenizer, CLIPTextModelWithProjection | |
# # loading the train dataset | |
# with open('clip_train.pkl', 'rb') as f: | |
# temp_d = pickle.load(f) | |
# # train_xv = temp_d['image'].astype(np.float64) # Array of image features : np ndarray | |
# # train_xt = temp_d['text'].astype(np.float64) # Array of text features : np ndarray | |
# # train_yv = temp_d['label'] # Array of labels | |
# train_yt = temp_d['label'] # Array of labels | |
# # ids = list(temp_d['ids']) # image names == len(images) | |
# # loading the test dataset | |
# with open('clip_test.pkl', 'rb') as f: | |
# temp_d = pickle.load(f) | |
# # test_xv = temp_d['image'].astype(np.float64) | |
# test_xt = temp_d['text'].astype(np.float64) | |
# # test_yv = temp_d['label'] | |
# # test_yt = temp_d['label'] | |
# # Map the image ids to the corresponding image URLs | |
# image_map_name = 'pascal_dataset.csv' | |
# df = pd.read_csv(image_map_name) | |
# image_list = list(df['image']) | |
# class_list = list(df['class']) | |
# zip_path = "pascal_raw.zip" | |
# zip_file = zipfile.ZipFile(zip_path) | |
# # text_model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32") | |
# # text_tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32") | |
# text_index = faiss.read_index("text_index.index") | |
# def T2Isearch(query, k=50): | |
# # Encode the text query | |
# # inputs = text_tokenizer([query], padding=True, return_tensors="pt") | |
# # outputs = text_model(**inputs) | |
# # query_embedding = outputs.text_embeds | |
# query_embedding = test_xt[0] | |
# query_vector = np.array([query_embedding]) | |
# faiss.normalize_L2(query_vector) | |
# # text_index.nprobe = index.ntotal | |
# text_index.nprobe = 100 | |
# # Search for the nearest neighbors in the FAISS text index | |
# D, I = text_index.search(query_vector, k) | |
# # get rank of all classes wrt to query | |
# classes_all = [] | |
# Y = train_yt | |
# neighbor_ys = Y[I] | |
# class_freq = np.zeros(Y.shape[1]) | |
# for neighbor_y in neighbor_ys: | |
# classes = np.where(neighbor_y > 0.5)[0] | |
# for _class in classes: | |
# class_freq[_class] += 1 | |
# count = 0 | |
# for i in range(len(class_freq)): | |
# if class_freq[i]>0: | |
# count +=1 | |
# ranked_classes = np.argsort(-class_freq) # chosen order of pivots -- predicted sequence of all labels for the query | |
# ranked_classes_after_knn = ranked_classes[:count] # predicted sequence of top labels after knn search | |
# lis = ['aeroplane', 'bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor'] | |
# class_ = lis[ranked_classes_after_knn[0]-1] | |
# # Map the image ids to the corresponding image URLs | |
# for i in range(len(image_list)): | |
# if class_list[i] == class_ : | |
# image_name = image_list[i] | |
# image_data = zip_file.open("pascal_raw/images/dataset/"+ image_name) | |
# image = Image.open(image_data) | |
# st.image(image, width=600) | |
query = st.text_input("Enter your search query here:") | |
# if st.button("Search"): | |
# if query: | |
# T2Isearch(query) |