Spaces:
Running
Running
File size: 5,578 Bytes
9ce656b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | #!/usr/bin/env python
# coding: utf-8
# In[3]:
import streamlit as st
import pandas as pd
import joblib
d=pd.read_csv(r"video_game_reviews.csv")
d.dropna(inplace=True)
d.drop_duplicates(inplace=True)
d.drop(axis=1,columns=['Requires Special Device', 'Developer', 'Publisher','Game Length (Hours)', 'Graphics Quality',
'Soundtrack Quality', 'Story Quality',
'Min Number of Players'],inplace=True)
bins = [10, 20, 30, 40, 45, 50]
labels = ['Very Low Rating', 'Low Rating', 'Medium Rating', 'High Rating', 'Very High Rating']
d['User Rating'] = pd.cut(
d['User Rating'],
bins=bins,
labels=labels,
include_lowest=True)
pipeline=joblib.load("gaussian_nb_pipelines.pkl")
label_encoder = joblib.load("game title_label_encoders.pkl")
st.set_page_config(
page_title="VGRS")
st.markdown("""
<style>
/* Overall App Background */
body, .stApp {
background: linear-gradient(to bottom right, #f8fafd, #eef2fb); /* light pastel background */
color: #222;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
/* Title with gradient neon text */
h1 {
background: linear-gradient(90deg, #00f0ff, #ff00ff);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-weight: 800;
font-size: 2.5em;
text-shadow: 0 0 8px rgba(0, 240, 255, 0.6), 0 0 16px rgba(255, 0, 255, 0.4);
}
/* Styled Selects, Sliders, Multiselects */
.stSelectbox > div, .stSlider, .stMultiSelect > div {
background-color: #ffffff;
border: 2px solid #00f0ff;
border-radius: 10px;
padding: 8px;
box-shadow: 0 0 8px rgba(0, 240, 255, 0.3);
transition: box-shadow 0.3s ease;
}
.stSelectbox > div:hover, .stSlider:hover, .stMultiSelect > div:hover {
box-shadow: 0 0 14px rgba(0, 240, 255, 0.6);
}
/* Neon Button with rainbow glow */
button[kind="primary"] {
background: linear-gradient(90deg, #00f0ff, #a200ff);
color: #fff !important;
font-weight: bold;
border-radius: 12px;
border: none;
padding: 0.6em 1.2em;
box-shadow: 0 0 10px #00f0ff;
transition: all 0.3s ease;
}
button[kind="primary"]:hover {
transform: scale(1.05);
box-shadow: 0 0 18px #a200ff;
}
/* Success prediction box */
.stAlert-success {
background-color: #ecf9ff !important;
border-left: 6px solid #00f0ff !important;
color: #007c91 !important;
font-weight: bold;
}
/* Table header with shiny colors */
.stDataFrame thead th {
background: linear-gradient(to right, #00f0ff, #c084fc);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-weight: bold;
text-shadow: 0 0 6px rgba(0, 240, 255, 0.5);
}
/* Table rows */
.stDataFrame tbody td {
background-color: #ffffff !important;
color: #222 !important;
}
.stDataFrame tbody tr:hover td {
background-color: #f0faff !important;
box-shadow: inset 0 0 10px #00f0ff;
}
/* Expander */
.stExpanderHeader {
color: #00f0ff !important;
font-weight: bold;
}
</style>
""", unsafe_allow_html=True)
st.title("🎮 Video Game Recommendation System")
release_years = sorted(d['Release Year'].dropna().unique())
selected_year = st.selectbox("Select Release Year", release_years)
filtered_df = d[d['Release Year'] == selected_year]
game_modes = filtered_df['Game Mode'].dropna().unique()
selected_game_mode = st.selectbox("Select Game Mode", game_modes)
filtered_df = filtered_df[filtered_df['Game Mode'] == selected_game_mode]
multiplayer_options = filtered_df['Multiplayer'].dropna().unique()
selected_multiplayer = st.selectbox("Select Multiplayer Option", multiplayer_options)
filtered_df = filtered_df[filtered_df['Multiplayer'] == selected_multiplayer]
platforms = filtered_df['Platform'].dropna().unique()
selected_platform = st.selectbox("Select Platform", platforms)
filtered_df = filtered_df[filtered_df['Platform'] == selected_platform]
genres = filtered_df['Genre'].dropna().unique()
selected_genre = st.selectbox("Select Genre", genres)
filtered_df = filtered_df[filtered_df['Genre'] == selected_genre]
age_groups = filtered_df['Age Group Targeted'].dropna().unique()
selected_age_group = st.selectbox("Select Age Group Targeted", age_groups)
filtered_df = filtered_df[filtered_df['Age Group Targeted'] == selected_age_group]
user_ratings = filtered_df['User Rating'].dropna().unique()
selected_user_rating = st.selectbox("Select User Rating", user_ratings)
prices = sorted(filtered_df['Price'].dropna().unique())
selected_price = st.select_slider(
"Select Price",
options=prices,
value=prices[0],
format_func=lambda x: f"${x:.2f}"
)
filtered_df = filtered_df[(filtered_df['Price'] <= selected_price)&(filtered_df['User Rating']==selected_user_rating)]
input_df = pd.DataFrame([{
'User Rating': selected_user_rating,
'Age Group Targeted': selected_age_group,
'Platform': selected_platform,
'Genre': selected_genre,
'Multiplayer': selected_multiplayer,
'Game Mode': selected_game_mode,
'Price': selected_price,
'Release Year': selected_year
}])
if st.button("🎮 Recommend Video Game"):
prediction = pipeline.predict(input_df)
predicted_title = label_encoder.inverse_transform(prediction)[0]
st.success(f"🎯 Recommended Game: **{predicted_title}**")
with st.expander("🔍 View Games Matching Your Criteria"):
st.dataframe(filtered_df[["Game Title","Price"]])
st.write("\n")
# In[ ]:
|