Update app.py

app.py

@@ -1,88 +1,88 @@
 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
+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 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']
+# 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'])
+# 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)
+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_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")
+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
+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)
+    # 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
+    # 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
+    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]
+    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)
+    # 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)
+if st.button("Search"):
+    if query:
+        T2Isearch(query)