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)