diff --git a/Phase 2/task_4.py b/Phase 2/task_4.py
new file mode 100644
index 0000000..a32be2a
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+++ b/Phase 2/task_4.py	
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+import json
+import tensorly as tl
+import numpy as np
+from pymongo import MongoClient
+from phase1_mongodb import *
+
+client = MongoClient()
+client = MongoClient(host="localhost", port=27017)
+
+# Select the database
+db = client.Multimedia_Web_DBs
+
+caltechDataset = loadDataset()
+num_labels = 101
+# Fetch all documents from the collection and then sort them by "_id"
+feature_descriptors = list(db.Feature_Descriptors.find({}))
+feature_descriptors = sorted(list(db.Feature_Descriptors.find({})), key=lambda x: x["_id"], reverse=False)
+label_ids = [x["label"] for x in feature_descriptors]
+
+def compute_cp_decomposition(feature_model, rank):
+    
+    label_vectors = [(x["label"], x[feature_model]) for x in feature_descriptors if x["_id"] % 2 == 0]
+
+    num_labels = 101
+    tensor_shape = (len(label_vectors), len(feature_descriptors[0][feature_model]), num_labels)
+    tensor = np.zeros(tensor_shape)
+    for id in range(len(label_vectors)):
+        label = label_vectors[id][0]
+        tensor[id, :, label] = label_vectors[id][1]
+    
+    weights, factors = tl.decomposition.parafac(tensor, rank=rank, normalize_factors=True)
+    return weights, factors
+
+
+def main():
+
+    
+    
+    # Step 4: Perform CP-decomposition (parafac) to extract latent semantics
+   
+    features = ['color_moments', 'hog', 'layer3', 'avgpool', 'fc']
+
+    # User input for feature model to extract
+    print("1: Color moments")
+    print("2: HOG")
+    print("3: Resnet50 Avgpool layer")
+    print("4: Resnet50 Layer 3")
+    print("5: Resnet50 FC layer")
+    feature_model = features[int(input("Select the feature model: ")) - 1]
+    k = int(input("Enter k: "))
+    weights, factors = compute_cp_decomposition(feature_model, k)
+    k_latent_semantics = list(zip(label_ids, factors[0].tolist()))
+    k_latent_semantics_display = sorted(list(zip(label_ids, factors[0].tolist())), key = lambda x: x[1][0], reverse = True)
+    k_latent_semantics_display = [{"_id": item[0], "semantics": item[1]} for item in k_latent_semantics_display]
+    filename = f'{feature_model}-CP-semantics-{k}.json'
+    k_latent_semantics = [{"_id": item[0], "semantics": item[1]} for item in k_latent_semantics]
+    with open(filename, 'w', encoding='utf-8') as f:
+        json.dump(k_latent_semantics, f, ensure_ascii = False)
+    
+    print(k_latent_semantics_display)
+
+
+if __name__ == "__main__":
+   main()
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