Shell Frame
-
BackGround
-
Coding Part
-
User Interface ###### -- Preliminary Processing by GUI ###### -- Advanced Processing by Pywebio
-
Distribution
1.11> Injection: IMBD is an online database of information related to films containing cast, production crew and personal biographies, plot summaries, trivia, ratings, and fan and critical reviews.
1.12> Purpose: Built up Searching System to screen out the Target Movie for specific Type, Year, KeyWords, also having function to Recommendation_Rank
# This Python 3.11 environment
# Defined by https://www.kaggle.com/datasets/hijest/genre-classification-dataset-imdb
import numpy as pd
import pandas as pd
import os
import Create_folders as cf
import sys
import random as r
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import tkinter as tk
import Data_Year_Count_
import Data_Processing
from tkinter import ttk
from tkinter import font as tkfont
from tkinter.ttk import Combobox
from PIL import Image, ImageTk
from tkinter import font as tkfont
from PIL import Image, ImageTk# Creat Folder
folders1 = "train path"
folders2 = "image path"
os.makedirs(folders1,exist_ok=True)
os.makedirs(folders2,exist_ok=True)
#Get Path
def getpath(folderpath):
file_paths=[]
for root, dirs, files in os.walk(folderpath):
for file in files:
file_path = os.path.join(root, file)
file_paths.append(file_path)
return file_pathstrainpath = getpath(folders1)
imagepath = getpath(folders2)
tpath = trainpath[0]
ipath = imagepath[0]train_path = cf.tpath
# Seprate data by Specific symbols
# Reset Index, Rename Columns
train_data = pd.read_csv(train_path, sep=':::', names=['Title', 'Type', 'Description'], engine='python')
train_data = pd.DataFrame(train_data , index=range(1,len(train_data)+1))# train_data
print(train_data.head())| Index | Title | Type | Description |
|---|---|---|---|
| 1 | Oscar et la dame rose (2009) | drama | Listening in to a conversation between his do.. |
| 2 | Cupid (1997) | thriller | A brother and sister with a past incestuous ... |
| 3 | Young, Wild and Wonderful (1980) | adult | As the bus empties the students for their... |
| 4 | The Secret Sin (1915) | drama | To help their unemployed father make ends... |
| 5 | The Unrecovered (2007) | drama | The film's title refers not only to... |
[54214 rows , 3 columns ]
train_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 54214 entries, 1 to 54214 Data columns (total 3 columns):
0 Title 54214 non-null object 1 Type 54214 non-null object 2 Description 54214 non-null object dtypes: object(3) memory usage: 1.2+ MB
train_data.shape (54214, 4)
train_data.columnsIndex(['Title', 'Type', 'Description'], dtype='object')
train_data.isnull().sum()Title 0 Type 0 Description 0 Year 0 dtype: int64
# DataClening for Visualization
# filte out error value
def dataclean(input_list):
clean_data = []
for i in input_list:
try:
m = int(i)
except ValueError:
continue
clean_data.append(m)
clean_data.sort()
return clean_data# Using dataclean to Filter Wrong Values
set(dataclean(train_data["Year"])){1890, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022}
# Separate Title, Year
name_year = train_data["Title"]
year_container = list()
for i in range(1,len(train_data["Title"])):
each = name_year[i].split()
year_container.append((each[-1])[1:5])
# Build a new column for "Year"
year_container.append(str(2006))
year_container.sort()
train_data["Year"] = year_container
year_container = dataclean(train_data["Year"])
year_set = set(year_container)
year_list = list(year_set)
change_Container_type = []
change_Container_title = []
# Reset format for Type, Title
for each_1 , each_2 in zip(train_data["Type"], train_data["Title"]):
strip_each_type = each_1.strip()
strip_each_title = each_2.strip()
strip_each_title = strip_each_title[0:len(strip_each_title)-7]
change_Container_type.append(strip_each_type)
change_Container_title.append(strip_each_title)
# Reset Type and Title columns
train_data["Type"] = change_Container_type
train_data["Title"] = change_Container_title
type_list = []
for i in (train_data["Type"]):
if (i not in type_list):
type_list.append(i)
type_list.sort()
year_container = dataclean(year_container)
year_set = set(year_container) # every_single_list
year_list = []
count_list = []
for i1 in year_set:
year_list.append(i1)
count_list.append(year_container.count( int(i1) )
year_num_count = (dict(zip(year_list,count_list)))
dict_ = dict()
dict_["Year"] = year_list
dict_["count"]=count_list
dict_DataFrame=pd.DataFrame(dict_)# Check Year list and Type list
print(year_list) # Every Single Year
print(type_list) # Every Single Type
# year_list=[1890, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]
# type_list=['action', 'adult', 'adventure', 'animation', 'biography', 'comedy', 'crime', 'documentary', 'drama', 'family', 'fantasy', 'game-show', 'history', 'horror', 'music', 'musical', 'mystery', 'news', 'reality-tv', 'romance', 'sci-fi', 'short', 'sport', 'talk-show', 'thriller', 'war', 'western']plt.figure(figsize=(14,6))
sns.countplot(data=train_data, y='Type', order=train_data['Type'].value_counts().index)
# Setting the value for the graph
plt.xlabel('Count', fontsize=12, fontweight=10)
plt.ylabel('Type', fontsize=12, fontweight=10)
plt.title("Distribution of Types(Transverse)" , fontsize=12 , fontweight=10)
plt.savefig('plot_1 (Distribution of Types(Transverse).png')
# Plot distribution of genres using bar plot
plt.figure(figsize=(14, 8))
counts = train_data['Type'].value_counts()
sns.barplot(x=counts.index, y=counts, palette='deep')
plt.xlabel('Genre', fontsize=12, fontweight=10)
plt.ylabel('Count', fontsize=12, fontweight=10)
plt.title('Distribution of Types(Vertical)', fontsize=12, fontweight=10)
plt.xticks(rotation=90, fontsize=14, fontweight=10)
plt.savefig('plot_2 (Distribution of Types(Vertical)).png')
# Plot Distribution of Films_number in years
sns.lineplot(x='Year', data=dict_DataFrame , y='count')
plt.xlabel('Year')
plt.ylabel('Count')
plt.title('Yearly Count')
plt.savefig('plot_3 (Distribution of Films_number in years).png')
plt.show()
print(train_data.head()) # Check the processed Data
print(train_data.info)
print(train_data.columns)| Index | Title | Type | Description | Year |
|---|---|---|---|---|
| 1 | Oscar et la dame rose | drama | Listening in to a conversation between his do.. | 2009 |
| 2 | Cupid | thriller | A brother and sister with a past incestuous ... | 1997 |
| 3 | Young, Wild and Wonderful | adult | As the bus empties the students for their... | 1980 |
| 4 | The Secret Sin | drama | To help their unemployed father make ends... | 1915 |
| 5 | The Unrecovered | drama | The film's title refers not only to... | 2007 |
| [ 54214 rows, 4 columns ] |
train_data.info()<class 'pandas.core.frame.DataFrame'> RangeIndex: 54214 entries, 1 to 54214 Data columns (total 4 columns):
0 Title 54214 non-null object 1 Type 54214 non-null object 2 Description 54214 non-null object 3 Year 54214 non-null object dtypes: object(4) memory usage: 1.7+ MB None
train_data['Year'].value_counts() # Number of occurrences corresponding yearYear 2016 3275 2017 3223 2015 2910 2014 2450 ... Girl 1 Powe 1 Rome 1 SRI) 1 Atro 1 Name: count, Length: 158, dtype: int64
train_data['Type'].value_counts() # Number of occurrences corresponding typeType drama 13613 documentary 13096 comedy 7447 short 5073 horror 2204 thriller 1591 action 1315 western 1032 reality-tv 884 family 784 adventure 775 music 731 romance 672 sci-fi 647 adult 590 crime 505 animation 498 sport 432 talk-show 391 fantasy 323 mystery 319 musical 277 biography 265 history 243 game-show 194 news 181 war 132
This Part Showing in Terminal !!!
import pandas as pd
import time
import Create_folders as cf
from Data_Processing import type_list, year_list, train_data, data_rowsdef Seach():
Name_Search() # Input FilmNames to Check
Type_Search() # Input FilmTypes to Check
KeyWords_Search() # Input FilmWords to Check
Year_Search() # Input FilmYears to CheckSearch()# "TypeSearch"
# Choose the type you want:
# >>> war
# Output would like be:
# <<<
'''
No.37638
Title:Hong se niang zi jun
Type:war
Description:
---> In 1930s, Wu Qionghua was a housemaid of Nan-ba-tian, a cruel warlord of a village in Hainan Island, China...
No.38113
Title:Dvazhdy rozhdyonnyy
Type:war
Description:
---> World War 2, White Sea. A Soviet transport ship carrying wounded from the front line is sunk by German planes...
............ More can be shown
'''# "KeyWords"
# Input any KeyWords (Input 0 TO SHUT DOWN):
# >>> love
# Output would like be:
# <<<
'''
Title:Even Just
Year:2019
Description: Jerry J. Garanyan, an ambulance-chasing lawyer ...
Title:Bypass
Year:2019
Description: An American adventure seen through the eyes of a …
Title:Duped: The Jezebel Experience
Year:2019
Description: A successful man meets an attractive woman that b...
Title:Concrete Girl
Year:2019
Description: An amateur female fighter works multiple jobs to ….
Title:Before I lay me down to Sleep
Year:2019
Description: If you were given only six months to a year to'''# "YearSearch"
# Input Year Single Choice(1) / Year Range(num!=1)
# >>> 0
# Input the Beginning_Time:
# >>> 2017
# Input the End_Time:
# <<< 2019
"""
No.31
Title:<Love Bites>
Type: <horror>
Introduce: Hollywood has long since been a haven for vampires and other underworld creatur......Wanting U Explore
No.43
Title:<Brad Cuts Loose>
Type: <short>
Introduce: Brad, an uptight office drone, seemingly discovers the perfect vehicle for lett......Wanting U Explore
No.57
Title:<Race Across America: Push Beyond>
Type: <documentary>
Introduce: Marshall Nord is a 49-year-old executive, Father, husband and amateur endurance......Wanting U Explore
More will be shown.......
"""- 3.0 Frame
class Application(tk.Frame):
def __init__(self, root):
super().__init__(root)
self.master = root
self.pack()
root.minsize(800, 500) # size
root.title('HomePage') # name
self.HomePage()
self.search_var = tk.StringVar()
image_path =cf.ipath # Replace with your image path
original_image = Image.open(image_path)
resized_image = original_image.resize((500, 300))
self.bg_image = ImageTk.PhotoImage(resized_image)
self.bg_label = tk.Label(root, image=self.bg_image)
# Adjust the label to fill the available space
self.bg_label.pack(fill="both", expand=False)- 3.1 Login Interface
# The login interface Code- 3.2 HomePage
def HomePage(self):
title_font = tkfont.Font(family='Helvetica', size=24, weight='bold')
# Title
tk.Label(self, text="Nostalgic Movies House", fg='#556B2F', font=title_font).grid(row=0, column=0) # title
subtitle_font = tkfont.Font(family='Helvetica', size=18, weight='bold')
# Sub-Title
tk.Label(self, text="Searching the movies you want", fg='#966F33', font=subtitle_font).grid(row=1, column=0)
frame = tk.Frame(self)
frame.grid(row=3, column=0, columnspan=2, pady=0.2)
# Random recommendation
random_list = set()
for i in range(5):
random_num = r.randint(1, 54215)
if random_num not in random_list:
random_list.add(random_num)
ran = [train_data["Title"].loc[i] for i in random_list]
button = tk.Button(frame, text='Q',command=self.buttonActive)
button.grid(row=1, column=0, columnspan=2)
frame1 = tk.Frame(self)
frame1.grid(pady=10)
frame1.configure(height=500, width=200)
tk.Label(frame1, text="Top Five Recommendation: "+''.join(ran)).grid(row=0, column=0)
def buttonActive(self): # button
if not hasattr(self, "search_window"): # Check if the Searching window has been created already
self.search_window = Searching()
self.search_window.mainloop()
else:
self.search_window.destroy() # If the window is already created, close it before creating a new one
self.search_window = Searching()
self.search_window.mainloop()HomePage Show as fellow :
Introduction:
- Middle Five Recommendation is randomly generated
- Click The "Search Button" like a Magnifier in the middle ---> TO Jump to the Next Search Page for More Detailed Search
- 3.3 Search Page
class Searching(tk.Frame):
def __init__(self):
super().__init__()
self.master = root
self.pack()
root.minsize(520, 300) # Size
root.title('Searching') # Name
self.intWind()
def intWind(self):
frame3 = tk.Frame(self) # Year
tk.Label(frame3, text='Year').grid(row=0, column=0)
year_container = list(year_set)
self.year_var = tk.StringVar()
ttk.Combobox(frame3, values=year_container, textvariable=self.year_var,width='15').grid(row=0, column=1)
frame3.grid(pady=0.2)
empty_frame = tk.Frame(self) #empty_frame:Type
empty_frame.grid(pady=0.1)
tk.Label(empty_frame, text="Type:").grid(row=0, column=0)
frame4 = tk.Frame(self) #type
type_list = type_container
self.var_list = [tk.StringVar() for _ in type_list]
# In Turn
for index, var in enumerate(self.var_list):
checkbutton = tk.Checkbutton(frame4, text=type_list[index], font=("System", 15,'bold'),fg='#556B2F',variable=var)
checkbutton.grid(row=index // 4, column=index % 4, sticky='w')
frame4.grid(pady=6)
frame5 = tk.Frame(self) #keywords
tk.Label(frame5, text="Search:").grid(row=0, column=0)
self.search_var = tk.StringVar()
search_entry = tk.Entry(frame5, textvariable=self.search_var)
search_entry.grid(row=0, column=1)
frame5.grid(pady=6)
tk.Button(self, text='Searching', width=10, command=self.buttonActive).grid(pady=6)
def buttonActive(self): #button
selected_year = self.year_var.get()
selected_types = [var.get() for var in self.var_list if var.get()]
selected_type_data = [type for type, var in zip(type_container, self.var_list) if var.get()]
search_term = self.search_var.get().lower()
print("Selected type data:", selected_type_data)
print("Selected year:", selected_year)
print("Search term:", search_term)
# Contain the input Infomation
container=[]
for i1 in range(1,54214):
if (train_data["Type"].loc[i1]in selected_type_data ) and (train_data["Year"].loc[i1]== selected_year) and (search_term in (train_data['Description'].loc[i1]).split()):
container.append(i1)
results = ["Title:"+train_data['Title'].loc[i1]+" Year:"+train_data['Year'].loc[i1]+" Description:"+train_data['Description'].loc[i1][:50]+"..." for i1 in container]
result_window = tk.Toplevel(root)
result_window.minsize(520, 300)
result_window.title("Search Results")
for result in results:
label = tk.Label(result_window, text=result)
label.pack()if __name__ == '__main__':
root = tk.Tk()
application = Application(root=root)
application.mainloop()
root.mainloop()Page 1.
Page 2.
Page 3.
Introduction
- Step 1: Select appropriate Year from Dropdown box and Click
- Step 2:Select appropriate Type from Checkbox and Click
- Step 3:Input any numbers of KeyWords (Any Input Format OK)
- Step 4:Click the Searching Button TO get results
Thanks for every members in Gruop No.5 Each member carefully and responsibly completed their tasks.
labels = ['Huang Siqi', 'Chen Zhuoxuan', 'Liang Yirui', 'Tu Yixin','Chen Qianqi','Li Minghao']
each = 100/6
sizes = [each, each, each, each,each,each]
colors = ['burlywood', 'khaki', 'salmon', 'orchid','pink','dodgerblue']
# Plot Work Contribution PieChart
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%')
text_content="""
1>Import Path and Dataset:\n--Huang Siqi\n
2>Data Preview and Visualization:\n--Tu Yixin\n--Li Minghao\n--Huang Siqi\n
3>User Interface (GUI):\n--Liang Yirui\n--Chen Qianqi\n--Chen Zhuoxuan\n
4>PPT:\n--Tu Yixin
"""
plt.text(1.5,0.4,text_content,fontsize=10,fontstyle='italic',fontweight=15)
plt.title('Work contribution')
plt.show()
.png){kind=link}
).png){kind=link}
.png){kind=link}
- [1] Code: All
Liang Yirui (t330034030) Chen Qianqi (t330034001 Chen Zhuoxuan (t330034003) Huang Siqi (t330034020) Tu Yixin (t330034048) Li Minghao (t330034027)
- [2] PPT: All
- [3] Pre: All
