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| import re | |
| from collections import Counter, defaultdict | |
| import gradio as gr | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| import networkx as nx | |
| import plotly.express as px | |
| def create_recommendation_system(pairs): | |
| sets_by_length = defaultdict(list) | |
| for pair in pairs: | |
| sets_by_length[len(pair)].append(set(pair)) | |
| sorted_keys = sorted(sets_by_length.keys(), reverse=True) | |
| def recommend(restaurant): | |
| recommended = set() | |
| for length in sorted_keys: | |
| for rest_set in sets_by_length[length]: | |
| if restaurant in rest_set: | |
| recommended.update(rest_set - {restaurant}) | |
| return list(recommended) | |
| return recommend | |
| def recommend(input_string): | |
| f = open("task1_output_n_c1_sup4.txt", "r") | |
| data = f.read() | |
| data = data.split("Frequent Itemsets:") | |
| clean_string = re.sub(r'\n', '', data[1]) | |
| pattern = r"\([^\)]+\)" | |
| extracted_tuples = re.findall(pattern, clean_string.replace("'", "")) | |
| tuple_list = [tuple(s.strip('()').split(', ')) for s in extracted_tuples] | |
| recommendation_system = create_recommendation_system(tuple_list) | |
| output_list = recommendation_system(input_string) | |
| images_labels = [("https://logowik.com/content/uploads/images/aw-restaurants5299.jpg", "A&W Restaurant"), | |
| ("https://inspirebrands.com/wp-content/uploads/2017/10/Arbys.jpg","Arbys"), | |
| ("https://www.shutterstock.com/image-vector/vinnytsia-ukraine-october-8-2023-600nw-2372329457.jpg","Burger King"), | |
| ("https://static.wixstatic.com/media/cc8696_2f0a91e1d9d245efa98b100adfcdd887~mv2.png/v1/crop/x_0,y_0,w_242,h_71/fill/w_339,h_94,al_c,lg_1,q_85,enc_auto/logo.png","California Kitchen"), | |
| ("https://logos-world.net/wp-content/uploads/2022/11/Carls-Jr.-Logo-500x281.png","Carls Jr."), | |
| ("https://upload.wikimedia.org/wikipedia/commons/thumb/0/02/Chick-fil-A_Logo.svg/873px-Chick-fil-A_Logo.svg.png","Chick-Fil-A"), | |
| ("https://upload.wikimedia.org/wikipedia/en/thumb/3/38/CECLogo2019.svg/330px-CECLogo2019.svg.png","Chuck E. Cheese"), | |
| ("https://upload.wikimedia.org/wikipedia/commons/thumb/c/cc/Culver%27s_logo.svg/558px-Culver%27s_logo.svg.png","Culvers"), | |
| ("https://upload.wikimedia.org/wikipedia/commons/thumb/a/ae/Dairy_Queen_logo.svg/300px-Dairy_Queen_logo.svg.png", "Dairy Queen"), | |
| ("https://logowik.com/content/uploads/images/493_dominospizza.jpg", "Dominos Pizza"), | |
| ("https://logos-world.net/wp-content/uploads/2023/03/Five-Guys-Logo-500x281.png", "Five Guys"), | |
| ("https://entrackr.com/storage/2023/06/Good-Flippin.jpg", "Good Flippin Burger"), | |
| ("https://logowik.com/content/uploads/images/hardees4024.jpg", "Hardees"), | |
| ("https://logos-marques.com/wp-content/uploads/2023/04/In-N-Out-Burger-Logo-768x432.png", "In-N-Out"), | |
| ("https://logos-world.net/wp-content/uploads/2022/08/Jack-in-the-Box-Logo-500x281.png", "Jack in the Box"), | |
| ("https://1000logos.net/wp-content/uploads/2021/05/Jollibee-logo-768x432.png", "Jollibee"), | |
| ("https://logowik.com/content/uploads/images/674_kfc.jpg", "KFC"), | |
| ("https://visitguernseycounty.com/wp-content/uploads/2022/05/Little-Caesars-Logo-1024x576.jpg", "Little Caesars"), | |
| ("https://logowik.com/content/uploads/images/mcdonalds-icon.jpg", "McDonalds"), | |
| ("https://images.squarespace-cdn.com/content/v1/53a47e51e4b0e78ae9ed2e97/4b8df8ab-662a-4381-92a6-97839099d5a7/Papa-Johns-logo.jpg", "Papa Johns"), | |
| ("https://cdn.nwe.io/files/x/d5/cc/c9810c9ad13188a3e0b2bc6577d7.jpg", "Pizza Inn"), | |
| ("https://logowik.com/content/uploads/images/294_pizza_hut_new_logo.jpg", "Pizza hut"), | |
| ("https://upload.wikimedia.org/wikipedia/commons/a/a6/PizzaExpress_Logo.jpg", "PizzaExpress"), | |
| ("https://images.squarespace-cdn.com/content/v1/53a47e51e4b0e78ae9ed2e97/be0aea01-84cc-4b89-a5e8-314ed394dc3c/Popeyes+logo.jpg", "Popeyes"), | |
| ("https://upload.wikimedia.org/wikipedia/commons/d/d9/Original_Round_Table_Pizza_Logo.jpg", "Round Table Pizza"), | |
| ("https://logowik.com/content/uploads/images/328_sbarro.jpg", "Sbarro"), | |
| ("https://1000logos.net/wp-content/uploads/2023/04/Shake-Shack-Logo-768x432.png", "Shake Shack"), | |
| ("https://upload.wikimedia.org/wikipedia/en/thumb/6/6d/Shakey%27s_US_logo.svg/330px-Shakey%27s_US_logo.svg.png", "Shakeys Pizza"), | |
| ("https://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/SONIC_New_Logo_2020.svg/1199px-SONIC_New_Logo_2020.svg.png", "Sonic"), | |
| ("https://images.getbento.com/accounts/236fb3743b9522eafb90c6d2d20b8115/media/accounts/media/V1G5LTCrSDqyixbNUDpr_logo-tasty-burger.png", "Tasty burger"), | |
| ("https://cdn.worldvectorlogo.com/logos/uno-pizzeria.svg", "Uno Pizzeria"), | |
| ("https://upload.wikimedia.org/wikipedia/commons/thumb/2/28/Wahlburgers_Logo.svg/768px-Wahlburgers_Logo.svg.png", "Wahlburgers"), | |
| ("https://logowik.com/content/uploads/images/866_wendys.jpg", "Wendys"), | |
| ("https://logowik.com/content/uploads/images/whataburger8433.jpg", "Whataburger"), | |
| ("https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcTsUZPMFzUmACfaeSrMSZsgGn5gnbXNyYRPxIrS1PfCrFWI9QJhrlkmdG9IhFbi", "White Castle")] | |
| output = [] | |
| for i in images_labels: | |
| if(i[1] in output_list): | |
| output.append(i) | |
| return output | |
| def plot_heatmap(): | |
| f = open("task1_output_n_c1_sup4.txt", "r") | |
| data = f.read() | |
| data = data.split("Frequent Itemsets:") | |
| clean_string = re.sub(r'\n', '', data[1]) | |
| pattern = r"\([^\)]+\)" | |
| extracted_tuples = re.findall(pattern, clean_string.replace("'", "")) | |
| frequent_itemsets = [tuple(s.strip('()').split(', ')) for s in extracted_tuples] | |
| # Initialize a defaultdict for item pair frequencies | |
| pair_counts = defaultdict(int) | |
| # Count frequencies of item pairs, excluding self-pairings | |
| for itemset in frequent_itemsets: | |
| unique_items = list(set(itemset)) | |
| for i in range(len(unique_items)): | |
| for j in range(i + 1, len(unique_items)): | |
| pair = tuple(sorted([unique_items[i], unique_items[j]])) | |
| pair_counts[pair] += 1 | |
| # Get a sorted list of unique items | |
| all_items = sorted(set(item for pair in pair_counts for item in pair)) | |
| # Create a DataFrame for the heatmap | |
| heatmap_data = pd.DataFrame(0, index=all_items, columns=all_items) | |
| for (item1, item2), count in pair_counts.items(): | |
| heatmap_data.at[item1, item2] = count | |
| heatmap_data.at[item2, item1] = count # Ensure symmetry | |
| # Create the heatmap | |
| fig = px.imshow(heatmap_data, labels=dict(x="Item", y="Item", color="Frequency"), | |
| x=all_items, y=all_items, | |
| color_continuous_scale=px.colors.sequential.Blues) | |
| fig.update_layout(title='Heatmap of Item Co-occurrences (Excluding Self-Pairings)', | |
| title_x=0.5) | |
| return fig | |
| def plot_bar(): | |
| f = open("task1_output_n_c1_sup4.txt", "r") | |
| data = f.read() | |
| data = data.split("Frequent Itemsets:") | |
| clean_string = re.sub(r'\n', '', data[1]) | |
| pattern = r"\([^\)]+\)" | |
| extracted_tuples = re.findall(pattern, clean_string.replace("'", "")) | |
| frequent_itemsets = [tuple(s.strip('()').split(', ')) for s in extracted_tuples] | |
| # Flatten the list of itemsets and count occurrences of each item | |
| items = [item for itemset in frequent_itemsets for item in itemset] | |
| item_counts = Counter(items) | |
| # Convert the counter to a DataFrame | |
| df = pd.DataFrame(item_counts.items(), columns=['Item', 'Count']) | |
| # Sort the DataFrame in descending order by 'Count' | |
| df = df.sort_values(by='Count', ascending=False) | |
| # Create the bar chart | |
| fig = px.bar(df, x='Item', y='Count', title='Frequency of Items in Frequent Itemsets', | |
| labels={'Count': 'Frequency'}, color='Count', height=600) | |
| fig.update_layout(xaxis_title='Item', yaxis_title='Frequency', title_x=0.5) | |
| return fig | |
| def plot_association(): | |
| f = open("task1_output_n_c1_sup4.txt", "r") | |
| data = f.read() | |
| data = data.split("Frequent Itemsets:") | |
| clean_string = re.sub(r'\n', '', data[1]) | |
| pattern = r"\([^\)]+\)" | |
| extracted_tuples = re.findall(pattern, clean_string.replace("'", "")) | |
| frequent_itemsets = [tuple(s.strip('()').split(', ')) for s in extracted_tuples] | |
| G = nx.DiGraph() | |
| for itemset in frequent_itemsets: | |
| if len(itemset) == 2: | |
| G.add_edge(itemset[0], itemset[1]) | |
| else: | |
| for i in range(len(itemset)): | |
| for j in range(i + 1, len(itemset)): | |
| G.add_edge(itemset[i], itemset[j]) | |
| pos = nx.spring_layout(G) | |
| edge_x = [] | |
| edge_y = [] | |
| for edge in G.edges(): | |
| x0, y0 = pos[edge[0]] | |
| x1, y1 = pos[edge[1]] | |
| edge_x.append(x0) | |
| edge_x.append(x1) | |
| edge_x.append(None) | |
| edge_y.append(y0) | |
| edge_y.append(y1) | |
| edge_y.append(None) | |
| edge_trace = go.Scatter( | |
| x=edge_x, y=edge_y, | |
| line=dict(width=0.5, color='#888'), | |
| hoverinfo='none', | |
| mode='lines') | |
| node_x = [] | |
| node_y = [] | |
| for node in G.nodes(): | |
| x, y = pos[node] | |
| node_x.append(x) | |
| node_y.append(y) | |
| node_trace = go.Scatter( | |
| x=node_x, y=node_y, | |
| mode='markers+text', | |
| text=[node for node in G.nodes()], | |
| textposition="top center", | |
| hoverinfo='text', | |
| marker=dict( | |
| showscale=True, | |
| colorscale='YlGnBu', | |
| size=10, | |
| colorbar=dict( | |
| thickness=15, | |
| title='Node Connections', | |
| xanchor='left', | |
| titleside='right' | |
| ), | |
| line_width=2)) | |
| fig = go.Figure(data=[edge_trace, node_trace], | |
| layout=go.Layout( | |
| title='<br>Association Rule Graph', | |
| titlefont_size=16, | |
| showlegend=False, | |
| hovermode='closest', | |
| margin=dict(b=20,l=5,r=5,t=40), | |
| annotations=[ dict( | |
| text="Association Rule Network", | |
| showarrow=False, | |
| xref="paper", yref="paper", | |
| x=0.005, y=-0.002 ) ], | |
| xaxis=dict(showgrid=False, zeroline=False), | |
| yaxis=dict(showgrid=False, zeroline=False)) | |
| ) | |
| return fig | |
| sorted_unique_restaurants = [ | |
| 'A&W Restaurant', | |
| 'Arbys', | |
| 'Burger King', | |
| 'California Kitchen', | |
| 'Carls Jr.', | |
| 'Chick-Fil-A', | |
| 'Chuck E. Cheese', | |
| 'Culvers', | |
| 'Dairy Queen', | |
| 'Dominos Pizza', | |
| 'Five Guys', | |
| 'Good Flippin Burger', | |
| 'Hardees', | |
| 'In-N-Out', | |
| 'Jack in the Box', | |
| 'Jollibee', | |
| 'KFC', | |
| 'Little Caesars', | |
| 'McDonalds', | |
| 'Papa Johns', | |
| 'Pizza Inn', | |
| 'Pizza hut', | |
| 'PizzaExpress', | |
| 'Popeyes', | |
| 'Round Table Pizza', | |
| 'Sbarro', | |
| 'Shake Shack', | |
| 'Shakeys Pizza', | |
| 'Sonic', | |
| 'Tasty burger', | |
| 'Uno Pizzeria', | |
| 'Wahlburgers', | |
| 'Wendys', | |
| 'Whataburger', | |
| 'White Castle'] | |
| recommender = gr.Interface(fn=recommend, inputs=gr.Dropdown(choices=sorted_unique_restaurants, value="Burger King"), outputs=gr.Gallery(label="Generated images", show_label=False, elem_id="gallery", columns=[4], object_fit="contain", height="auto"), title='Restaurant Recommender System', description='Find your ideal restaurant') | |
| vizz_heatmap = gr.Interface(fn=plot_heatmap, inputs = None, outputs=gr.Plot(label="Heatmap"), description = "Restaurant Itemset Frequency") | |
| vizz_bar = gr.Interface(fn=plot_bar, inputs = None, outputs=gr.Plot(label="Bar Graph"), description = "Restaurant Frequency") | |
| vizz_graph = gr.Interface(fn=plot_association, inputs = None, outputs=gr.Plot(label="Association Graph"), description = "Association Graph of Restaurants") | |
| tabbed = gr.TabbedInterface([recommender, vizz_heatmap, vizz_bar, vizz_graph], ["Restaurant Recommender", "Heatmap", "Restaurant Frequency", "Association Graph"]) | |
| tabbed.queue().launch(share = True, debug = True) |