Upload scripts

Data.py

@@ -0,0 +1,17 @@
+import streamlit as st
+import pandas as pd
+
+st.set_page_config(
+   page_title="Movie Explorer",
+   layout="wide",
+   initial_sidebar_state="expanded",
+)
+
+st.markdown("<h1 style='text-align: center;'>Movie Analysis</h1>", unsafe_allow_html=True)
+movies_df = pd.read_csv('movies2.csv',lineterminator='\n')
+movies_df = movies_df.drop(columns=movies_df.columns[0])
+movies_df = movies_df[movies_df['Release Date'] < "2022-01-01"]
+st.write(movies_df)
+st.caption("Data Source: TMDB")
+cast_df = pd.read_csv('movie_cast.csv')
+

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,13 +1,2 @@
----
-title: MovieExplorer
-emoji: 📉
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Movie-Analysis
+Analysis of Popular movies

cast_retrieval.py

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+import requests as req
+import pandas as pd
+from Data import movies_df
+from conf import api_key
+
+ses = req.session()
+
+crew = []
+ids = []
+for movie_id in movies_df['Id']:
+    ids.append(movie_id)
+    cast_api = "https://api.themoviedb.org/3/movie/" + str(movie_id) + '/credits?api_key=' + api_key + '&language=en-US'
+    c = ses.get(cast_api)
+    casts = c.json()
+    movie_cast = []
+    try:
+        count = 0
+        for i in casts["cast"]:
+            if count < 5:
+                movie_cast.append(i['name'])
+                count += 1
+        crew.append(movie_cast)
+    except:
+        crew.append("N/A")
+cast_df = pd.DataFrame({"Movie Id":ids, "Cast": crew})
+cast_df.to_csv("movie_cast.csv")
+

conf.py

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+# Used .gitignore to hide api key
+
+api_key = 'fdb378a1a8e6a38bc30049ee94f2b87a'

data_retrieval.py

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+# Retrieve data from TMDB using API
+
+import requests as req
+import pandas as pd
+from conf import api_key
+
+ses = req.session()
+
+genre_api = 'https://api.themoviedb.org/3/genre/movie/list?api_key=' + api_key + '&language=en-US&page=1'
+
+g = ses.get(genre_api)
+genres = g.json()
+g_id = []
+g_name = []
+for i in genres['genres']:
+    g_id.append(i['id'])
+    g_name.append(i['name'])
+
+ids = []
+titles = []
+desc = []
+pop = []
+rate = []
+date = []
+resp = []
+genre = []
+rev = []
+
+
+for k in range(1,500): # Returns 7387 movies as data
+    api = 'https://api.themoviedb.org/3/movie/popular?api_key=' + api_key + '&language=en-US&page='+str(k)
+    data = ses.get(api)
+    j = data.json()
+    for i in j['results']:
+        if i['original_language'] == 'en':
+            try:
+                ids.append(i['id'])
+            except:
+                ids.append(None)
+            try:
+                titles.append(i['title'])
+            except:
+                titles.append(None)
+            try:
+                desc.append(i['overview'])
+            except:
+                desc.append(None)
+            try:
+                pop.append(i['popularity'])
+            except:
+                pop.append(None)
+            try:
+                rate.append(i['vote_average'])
+            except:
+                rate.append(None)
+            try:
+                date.append(i['release_date'])
+            except:
+                date.append(None)
+            try:
+                resp.append(i['vote_count'])
+            except:
+                resp.append(None)
+            try:
+                p = []
+                for u in range(len(i['genre_ids'])):
+                    if i['genre_ids'][u] in g_id:
+                        p.append(g_name[g_id.index(i['genre_ids'][u])])
+                genre.append(p)
+            except:
+                genre.append(None)
+            try:
+                rev_api = 'https://api.themoviedb.org/3/movie/' + str(i['id']) + '?api_key=' + api_key + '&language=en-US'
+                r = ses.get(rev_api)
+                re = r.json()
+                rev.append(re['revenue'])
+            except:
+                rev.append(None)
+
+df = pd.DataFrame({'Id':ids, 'Title':titles,'Description':desc,'Popularity':pop,'Genres':genre,'Release Date': date,'Rating':rate,'Vote Count':resp,'Revenue':rev})
+df.to_csv('movies2.csv')

pages/Chart_Visualization.py

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+import streamlit as st
+from Data import movies_df
+import altair as alt
+import pandas as pd
+
+ops = ['Action']
+rev_action = 0
+rev_adv = 0
+rev_hor = 0
+rev_anim = 0
+rev_fant = 0
+rev_crim = 0
+rev_thr = 0
+rev_mys = 0
+rev_dr = 0
+rev_comic = 0
+rev_his = 0
+rev_tv = 0
+rev_wes = 0
+rev_sci = 0
+rev_rom = 0
+
+for i in movies_df['Genres']:
+    if 'Action' in i:
+        rev_action += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Adventure' in i:
+        rev_adv += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Animation' in i:
+        rev_anim += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Horror' in i:
+        rev_hor += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Fantasy' in i:
+        rev_fant += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Crime' in i:
+        rev_crim += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Mystery' in i:
+        rev_mys += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Comedy' in i:
+        rev_comic += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'History' in i:
+        rev_his += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'TV Show' in i:
+        rev_tv += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Western' in i:
+        rev_wes += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Science Fiction' in i:
+        rev_sci += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Romance' in i:
+        rev_rom += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Thriller' in i:
+        rev_thr += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+    elif 'Drama' in i:
+        rev_dr += int(movies_df[movies_df['Genres'] == i]['Revenue'].tolist()[0])
+# TV Shows are not included because revenue of TV Shows is 0 or unknown.
+revenue = [rev_dr,rev_action,rev_adv,rev_anim,rev_wes,rev_mys,rev_crim,rev_his,rev_comic,rev_fant,rev_rom,rev_sci,rev_thr,rev_hor]
+genres = ['Drama','Action','Adventure','Animation','Western','Mystery','Crime','History','Comedy','Fantasy','Romance','Science Fiction','Thriller','Horror']
+gen_rev = pd.DataFrame({'Genres':genres, 'Revenue':revenue})
+
+st.write('## Comparison of Revenue for each Movie Genre')
+st.altair_chart(alt.Chart(gen_rev).mark_bar().encode(x = alt.X('Genres', title = "Movie Genre", axis= alt.Axis(labelAngle = -45)), y = alt.Y('Revenue', title = 'Revenue (in USD)')).properties(width = 800,height = 500).configure_scale(bandPaddingInner=0.2))
+
+st.write('## How Revenue varies with Ratings')
+st.altair_chart(alt.Chart(movies_df).mark_bar().encode(x = alt.X('Rating', title = "Ratings", axis= alt.Axis(labelAngle = -45)), y = alt.Y('Revenue', title = 'Revenue (in USD)')).properties(width = 800,height = 500).configure_scale(bandPaddingInner=0.2))

pages/Popular_Movies.py

@@ -0,0 +1,20 @@
+import streamlit as st
+from Data import movies_df
+import altair as alt
+
+sort_by = st.selectbox("Select criteria to sort by",('Revenue','Rating'))
+if sort_by== 'Revenue':
+    n = st.slider('Select a value',5, 20, 10)
+    st.subheader(f'Top {n} Movies by Revenue (in USD)')
+    st.altair_chart(alt.Chart(movies_df.sort_values(by = "Revenue", ascending=False).head(n)).mark_bar().encode(x = alt.X('Title', title = "Movie Title", axis= alt.Axis(labelAngle = -45)), color = 'Revenue', y = alt.Y('Revenue', title = 'Revenue')).properties(width = 790,height = 500).configure_scale(bandPaddingInner=0.2))
+    
+elif sort_by == 'Rating':
+    n = st.slider('Select a value',5, 20, 10)
+    st.subheader(f'Top {n} Movies/TV Shows by Rating (out of 10)')
+    st.altair_chart(alt.Chart(movies_df.sort_values(by = "Rating", ascending=False).head(n)).mark_bar().encode(x = alt.X('Title', title = "Movie Title", axis= alt.Axis(labelAngle = -45)), color = 'Rating', y = alt.Y('Rating', title = 'Rating')).properties(width = 790,height = 500).configure_scale(bandPaddingInner=0.2))
+
+ops = st.multiselect("Select genres",['Action','Animation','Adventure','Fantasy','Horror','Crime','Thriller','Mystery','Drama','Comedy','History','TV Movie','Western','Science Fiction','Romance'])
+if len(movies_df[movies_df['Genres'] == str(list(ops))]) != 0:
+    st.write(movies_df[movies_df['Genres'] == str(list(ops))].drop(['Popularity', 'Genres'], axis="columns"))
+else:
+    st.write("No movie with given genres")

pages/View_Specific_Data.py

@@ -0,0 +1,51 @@
+import streamlit as st
+from Data import movies_df, cast_df
+
+st.header("Find Data by Movie Title")
+name = st.selectbox("Enter Movie Name", movies_df["Title"].tolist())
+id = movies_df.index[movies_df['Title'] == name]
+st.write("### Description")
+st.write(movies_df[movies_df['Title'] == name]['Description'].tolist()[0])
+st.write("### Movie ID")
+st.write(str(movies_df[movies_df['Title'] == name]['Id'].tolist()[0]))
+st.write('### Movie Genres')
+genre_list = movies_df[movies_df['Title'] == name]['Genres'].tolist()[0].lstrip('[').rstrip(']').split(",")
+s = ''
+for i in genre_list:
+    i = i.strip().strip("\'")
+    s += "<li>" + i + "</li>"
+
+st.markdown("<ul>"+s+"</ul>",unsafe_allow_html=True)
+st.write('### Release Date')
+date, format = st.tabs(["Date","Format"])
+with date:
+    st.write(movies_df[movies_df['Title'] == name]['Release Date'].tolist()[0])
+with format:
+    st.caption('YYYY-MM-DD')
+
+
+st.write("### Ratings")
+rating, vote_count = st.tabs(["Ratings","Vote Count"])
+with rating:
+    st.markdown("<p style = 'font-size: 16px;'>"+str(movies_df[movies_df['Title'] == name]['Rating'].tolist()[0])+"/10"+"</p>", unsafe_allow_html = True)
+with vote_count:
+    st.write(str(movies_df[movies_df['Title'] == name]['Vote Count'].tolist()[0]))
+
+st.write("### Revenue (in USD)")
+if movies_df[movies_df['Title'] == name]['Revenue'].tolist()[0] != 0:
+    st.markdown("<p style = 'font-size: 18px'>$ " + str(movies_df[movies_df['Title'] == name]['Revenue'].tolist()[0])+'</p>', unsafe_allow_html = True)
+else:
+    st.write("The revenue is unknown or N/A")
+
+st.write("### Movie Cast")
+movie_id = movies_df[movies_df["Title"] == name]["Id"].tolist()[0]
+cast_list = eval(cast_df[cast_df["Movie Id"] == movie_id]['Cast'].tolist()[0])
+
+if not cast_list:
+    st.write("Cast is not yet available")
+
+s = ''
+for i in cast_list:
+    s += "<li style='font-size: 16px; display: inline-block;'>" + i + "</li>"
+
+st.markdown("<ol>"+s+"</ol>", unsafe_allow_html = True)

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+pandas
+altair