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app.py

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+import streamlit as st
+st.set_page_config(page_title='ITR', page_icon="🧊", 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)

requirements.txt

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+transformers
+streamlit
+faiss-cpu