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 pandas as pd
import pickle
import pickletools
from transformers import AutoTokenizer, CLIPTextModelWithProjection
from sklearn.preprocessing import normalize, OneHotEncoder

# 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']

enc = OneHotEncoder(sparse=False)
enc.fit(np.concatenate((train_yt, test_yt)).reshape((-1, 1)))
train_yv = enc.transform(train_yv.reshape((-1, 1))).astype(np.float64)
test_yv = enc.transform(test_yv.reshape((-1, 1))).astype(np.float64)
train_yt = enc.transform(train_yt.reshape((-1, 1))).astype(np.float64)
test_yt = enc.transform(test_yt.reshape((-1, 1))).astype(np.float64)

# 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")
d = 32
text_index = faiss.index_factory(d, "Flat", faiss.METRIC_INNER_PRODUCT)
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_vector = query_embedding.detach().numpy()
    query_vector = query_vector.reshape(1,512)
    faiss.normalize_L2(query_vector)
    index.nprobe = index.ntotal
    
    # 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
    count = 0
    for i in range(len(image_list)):
        if class_list[i] == class_ :
            count+=1
            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)
            if count == 5: break

query = st.text_input("Enter your search query here:")

if st.button("Search"):
    if query:
        T2Isearch(query)