diff --git a/Essential/networkgraphvisual.py b/Essential/networkgraphvisual.py
new file mode 100644
index 0000000..9d986be
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+++ b/Essential/networkgraphvisual.py
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+import sqlite3
+import pandas as pd
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+import networkx as nx
+import plotly.graph_objects as go
+import os.path
+
+current_dir = os.path.dirname(os.path.abspath(__file__))
+db_path = (current_dir + r"\data\food_data.db")
+
+conn = sqlite3.connect(db_path)
+query = "SELECT * FROM food_data LIMIT 100"
+df = pd.read_sql_query(query, conn)
+
+df= df.fillna(0)
+
+nutrient_columns = [
+    'energy-kcal_100g', 'fat_100g', 'saturated-fat_100g', 'unsaturated-fat_100g',
+    'omega-3-fat_100g', 'omega-6-fat_100g', 'omega-9-fat_100g', 'trans-fat_100g',
+    'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', 'sucrose_100g',
+    'glucose_100g', 'fructose_100g', 'lactose_100g', 'maltose_100g', 'fiber_100g',
+    'soluble-fiber_100g', 'insoluble-fiber_100g', 'proteins_100g', 'salt_100g',
+    'added-salt_100g', 'sodium_100g', 'alcohol_100g', 'vitamin-a_100g',
+    'beta-carotene_100g', 'vitamin-d_100g', 'vitamin-e_100g', 'vitamin-k_100g',
+    'vitamin-c_100g', 'vitamin-b1_100g', 'vitamin-b2_100g', 'vitamin-pp_100g',
+    'vitamin-b6_100g', 'vitamin-b9_100g', 'vitamin-b12_100g', 'bicarbonate_100g',
+    'potassium_100g', 'chloride_100g', 'calcium_100g', 'phosphorus_100g', 'iron_100g',
+    'magnesium_100g', 'zinc_100g', 'copper_100g', 'manganese_100g', 'fluoride_100g',
+    'selenium_100g', 'chromium_100g', 'molybdenum_100g', 'iodine_100g',
+    'caffeine_100g', 'cocoa_100g', 'carbon-footprint_100g'
+]
+
+conn.close()
+chunk_size = 1000  
+
+similarity_df = pd.DataFrame(index=df['product_name'], columns=df['product_name'])
+for i in range(0, len(df), chunk_size):
+    chunk = df.iloc[i:i+chunk_size]
+    chunk_similarity_matrix = cosine_similarity(chunk[nutrient_columns])
+
+    chunk_similarity_df = pd.DataFrame(chunk_similarity_matrix, index=chunk['product_name'], columns=chunk['product_name'])
+    similarity_df.update(chunk_similarity_df)
+similarity_df = similarity_df.fillna(1.0)
+
+# * Similarities calculate by cosine similarities
+
+graph = nx.from_pandas_adjacency(similarity_df)
+node_degrees = graph.degree()
+sorted_nodes = sorted(node_degrees, key=lambda x: x[1], reverse=True)
+top_nodes = [node[0] for node in sorted_nodes[:10]]
+subgraph = graph.subgraph(top_nodes)
+pos = nx.spring_layout(subgraph)
+x, y = zip(*pos.values())
+edge_trace = go.Scatter(
+    x=[],
+    y=[],
+    line=dict(width=0.5, color='#888'),
+    hoverinfo='none',
+    mode='lines')
+
+for edge in subgraph.edges():
+    x0, y0 = pos[edge[0]]
+    x1, y1 = pos[edge[1]]
+    edge_trace['x'] += tuple([x0, x1, None])
+    edge_trace['y'] += tuple([y0, y1, None])
+
+node_trace = go.Scatter(
+    x=x,
+    y=y,
+    mode='markers+text',
+    hoverinfo='text',
+    text=list(subgraph.nodes()),
+    textposition='top center',
+    marker=dict(
+        showscale=False,
+        color='rgb(150,150,150)',
+        size=10,
+        line=dict(width=2, color='rgb(255,255,255)')))
+
+layout = go.Layout(
+    showlegend=False,
+    hovermode='closest',
+    margin=dict(b=20, l=5, r=5, t=40),
+    xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+    yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
+
+fig = go.Figure(data=[edge_trace, node_trace], layout=layout)
+fig.show()
+
+
+# ! Shortest Path
+
+G = nx.Graph()
+G.add_nodes_from(df['product_name'])
+
+# ! Add edges to the graph with weight as the modified similarity score(Reverse similarities score)
+for i in range(len(similarity_df)):
+    for j in range(i+1, len(similarity_df)):
+        if similarity_df.iloc[i,j] > 0:
+            similarity_score = similarity_df.iloc[i,j]
+            similarity_weight = 1 - similarity_score
+            G.add_edge(similarity_df.index[i], similarity_df.columns[j], weight=similarity_weight)
+
+# * Example
+start_node = 'Cheese twist'
+end_node = 'Pepperidge farm cookies'
+shortest_path = nx.shortest_path(G, start_node, end_node, weight='weight')
+
+print(shortest_path)
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