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app.py

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+import streamlit as st
+from gotLibrary import GotLib
+import plotly.express as px
+import pandas as pd 
+import matplotlib.pyplot as plt
+
+class plot_type:
+    def __init__(self,data):
+        self.data = data
+        self.fig=None
+        self.update_layout=None
+
+    def bar(self,x,y,color):
+        self.fig=px.bar(self.data,x=x,y=y,color=color)
+
+    def pie(self,x,y):
+        self.fig = px.pie(self.data,values=x,names=y)
+
+        
+    def set_title(self,title):
+        
+        self.fig.update_layout(
+                title=f"{title}",
+                    yaxis=dict(tickmode="linear"),
+                    paper_bgcolor='rgba(0,0,0,0)',
+                    plot_bgcolor='rgba(0,0,0,0)',
+                    font=dict(color='white',size=18))
+
+    def set_title_x(self,title):
+        
+        self.fig.update_layout(
+                title=f"{title}",
+                    xaxis=dict(tickmode="linear"),
+                    paper_bgcolor='rgba(0,0,0,0)',
+                    plot_bgcolor='rgba(0,0,0,0)',
+                    font=dict(color='white',size=18))
+
+    def set_title_pie(self,title):
+        self.fig.update_layout(title=title,
+                                paper_bgcolor='rgba(0,0,0,0)',
+                                plot_bgcolor='rgba(0,0,0,0)',
+                                font=dict(color='white',size=18))
+        
+
+
+    def plot(self):
+        st.write(self.fig)
+
+class slide_bar:
+    value=4
+    def __init__(self,title,x,y):
+        self.title = title
+        self.x=x
+        self.y=y
+        self.slide_bar = None
+        
+
+    def set(self):
+        self.slide_bar = st.slider(self.title,self.x,self.y)
+        slide_bar.value=self.slide_bar
+
+class select_box:
+    value="tyrion"
+    def __init__(self,data):
+        self.data=data
+        self.box=None
+    def place(self,title,key):
+        header(title)
+        self.box = st.selectbox(str(key),self.data)
+        select_box.value=self.box
+
+def title(text,size,color):
+    st.markdown(f'<h1 style="font-weight:bolder;font-size:{size}px;color:{color};text-align:center;">{text}</h1>',unsafe_allow_html=True)
+
+def header(text):
+    st.markdown(f"<p style='color:white;'>{text}</p>",unsafe_allow_html=True)
+
+
+
+
+@st.cache(persist=True,suppress_st_warning=True)
+def load_data():
+    df = pd.read_csv('final_data.csv')
+    df = df.iloc[:,1:]
+    return df
+
+#loading the data
+df = load_data()
+
+#intializing the GotLib object
+got = GotLib(df)
+
+
+
+with open("styles/style.css") as f:
+    st.markdown(f'<style>{f.read()}</style>',unsafe_allow_html=True)
+
+
+#-------------------------------Header-----------------------
+
+st.markdown('<h1 style="text-align:center;color:white;font-weight:bolder;font-size:100px;">GAME<br>OF<br>THRONES</h1>',unsafe_allow_html=True)
+# st.markdown('<h1 style="text-align:center;color:white;background-image:url("m1.png");">An analysis..</h1>',unsafe_allow_html=True)
+st.markdown('<h2 style="text-align:center;color:white;">An analysis..</h2>',unsafe_allow_html=True)
+st.image('images/got1.jpg',width=700)
+st.markdown('### This is an analysis based project on the tv series game of thrones')
+
+
+
+#------------------------Module 1--------------------------
+title("Top characters based on number of words spoken in a season",60,"white")
+
+header('season')
+
+option_1_s = st.selectbox('',[1,2,3,4,5,6,7,8])
+
+header("number of results")
+num = st.slider("",4,50)
+
+temp_data = got.show_top_by_season(option_1_s)
+number=10
+
+bar1 = plot_type(temp_data[-num:])
+bar1.bar("spoken_words","character","spoken_words")
+bar1.set_title(f"Season {option_1_s}")
+bar1.plot()
+
+
+#-----------------------Module 2------------------------------
+
+title("Overall top characters based on number of spoken words",60,'white')
+
+header("number of results")
+
+num1 = st.slider("",5,60)
+
+temp_data1 = got.get_overall_top()
+
+bar2 = plot_type(temp_data1[-num1:])
+bar2.bar("words","character","words")
+bar2.set_title("Overall Top")
+bar2.plot()
+
+#------------------------Module 3-----------------------------
+
+title("Character's number of dialogues over the seasons",60,"white")
+st.markdown('### NOTE: displaying only top 100 characters in drop downs as there are more than 500+ it would be awkward to display it all :)')
+
+characters = got.get_data_seasons()
+stb1 = select_box(characters)
+stb1.place("character",0)
+@st.cache(persist=True)
+def sbyc(df,stb1):
+    return got.show_bar_by_character_allSeason(stb1)
+
+t_data = sbyc(df,stb1.value)
+
+bar0 = plot_type(t_data)
+bar0.bar("season","spoken_words","spoken_words")
+# bar0.update_layout(title=f"{stbl.value}")
+bar0.set_title_x(stb1.value)
+
+bar0.plot()
+
+#----------------------Module 4----------------------------------
+
+title("Percentage of a character's performance in seasons",60,"white")
+st.write("what is the character's distribution of his/her/(uhh. you know the rest) dialogue percentage over the seasons")
+
+stb2 = select_box(characters)
+stb2.place("character",9)
+t_data1 = got.cal_importance(df,stb2.value)
+
+pie2 = plot_type(t_data1)
+pie2.pie("imp","season")
+pie2.set_title_pie(stb2.value)
+pie2.plot()
+
+
+#-------------------------Module 5-----------------------------
+
+title('Most number of words spoken by a character',60,'white')
+st.markdown('#### removing all the stop words in the sense common words.')
+
+
+
+select_box1 = select_box(characters)
+select_box1.place('character',1)
+header("range")
+num2 = slide_bar("",5,55)
+num2.set()
+temp_data2,size = got.get_most_spokenwords_by_character(df,select_box1.value,num2.value)
+
+
+bar3 = plot_type(temp_data2)
+bar3.bar("frequency","words","frequency")
+bar3.set_title(f"{select_box1.value.capitalize()} total words spoken - {size}")
+bar3.plot()
+
+#--------------------------WORD_CLOUD---------------------------
+
+title("WordCloud of a character",60,'white')
+st.markdown('#### It may take a few seconds to load the result, so please hold on to your dragons.')
+
+select_box2 = select_box(characters)
+select_box2.place('character',2)
+
+header('range')
+sl = slide_bar('',50,200)
+sl.set()
+@st.cache(persist=True,suppress_st_warning=True)
+def swc(df,v1,v2):
+    return got.show_word_cloud(df,v1,v2)
+wc = swc(df,sl.value,select_box2.value)
+fig = plt.figure(figsize=(8,8))
+plt.imshow(wc,interpolation="bilinear")
+plt.axis('off')
+plt.title(select_box2.value,fontsize=18)
+plt.tight_layout()
+st.pyplot(fig)
+
+#--------------------------Module 4------------------------------
+
+title("Emotional characteristics",70,"white")
+st.write("The below pie chart depicts the distribution of emotions of a character.")
+st.write('Note: This is purely my calculations based on the text-corpus I created and also based on the words used by a character.')
+
+select_box3 = select_box(characters)
+select_box3.place('character',3)
+
+temp_data3 = got.cal_character(select_box3.value)
+pie1 = plot_type(temp_data3['data'])
+pie1.pie(temp_data3['y'],temp_data3['x'])
+pie1.set_title_pie(select_box3.value)
+pie1.plot()
+
+#---------------------------Module 5--------------------------
+title("Most used name by a character",50,"white")
+
+stb = select_box(characters[:50])
+stb.place("character",4)
+temp_df = got.most_name(stb.value)
+num_range = temp_df.shape[0]
+rangesl = slide_bar("",1,num_range)
+rangesl.set()
+
+bar5 = plot_type(temp_df.iloc[-rangesl.value:,:])
+bar5.bar("number","name","number")
+bar5.set_title(stb.value)
+bar5.plot()
+
+
+
+#----------------------Module 6----------------------------------
+
+title('Similar Characters',60,'white')
+st.write('The chart shows characters who are similar to a character with their similarity precentage, based on their similar usage of words, this same alogrithm is also used in movie recommender systems.')
+st.write('Note: This is very much experimental and purely based on the scripts. And only depends on script text and nothing else.')
+ch=characters[:]
+ch1 = select_box(ch)
+ch1.place('character',5)
+val=ch1.value
+
+
+results = got.get_similar_character(val)
+
+colors = ['rgb(0,255,42)','rgb(221, 235, 30)','rgb(224, 91, 43)']
+char = list(results['character'])
+score = list(results['similarity'])
+i=0
+for name,sc in zip(char,score):
+    
+    st.markdown(f"<h3 style='text-align:center;color:rgb(196, 196, 196);'><span style='font-weight:bolder;color:{colors[i]};font-size:50px;'>{name} </span> [{sc}%]</h3>",unsafe_allow_html=True)
+    i+=1
+
+
+#----------------------------------------------------------------------THE END---------------------------------------------------------------------
+
+st.write(' ')
+
+st.markdown('#### The dataset here is created from the scripts, involved a lot of data cleaning,wrangling and pre-processing!. Took a lot of time to prepare it!. And is 85% accurate.')
+
+st.write('check the box below to peak at the dataset')
+if st.checkbox('',False):
+    st.subheader("Game_of_Thrones")
+    st.write(df)
+
+st.write('')
+st.write('')
+
+
+st.markdown('<h3 style="text-align:center;">Made By <span style="color:#4f9bce;font-weight:bolder;font-size:40px;">Mario 😎</span></h3>',unsafe_allow_html=True)
+st.markdown('<h2 style="text-align:center;text-decoration:none;font-weight:bolder;"><a style="text-decoration:none;color:rgb(96, 235, 133);" href="https://github.com/Mario-Vishal">-> GitHub <-</a></h2>',unsafe_allow_html=True)
+st.markdown('<h2 style="text-align:center;text-decoration:none;font-weight:bolder;"><a style="text-decoration:none;color:rgb(20, 166, 219);" href="https://www.linkedin.com/in/mario-vishal">-> Linkedin <-</a></h2>',unsafe_allow_html=True)
+st.markdown('<h2 style="text-align:center;text-decoration:none;font-weight:bolder;"><a style="text-decoration:none;color:red;" href="mailto:mariovishal12@gmail.com">-> Contact Me <-</a></h2>',unsafe_allow_html=True)