added some interactive graphs

app.py

@@ -7,6 +7,11 @@
 from dataset.cleanDs import cleanDs
 
 
+import plotly
+import plotly.graph_objs as go
+import json
+import pandas as pd
+
 app = Flask(__name__)
 model = pickle.load(open('testModel.pkl', 'rb'))
 
@@ -15,13 +20,55 @@
 
 @app.route('/')
 def home():
-    return render_template('index.html', url ='/static/images/line_plot.jpg')
+#    return render_template('index.html', url ='/static/images/line_plot.jpg')
 
+    labels = df['Genre']
+    values = df['Global_Sales']
+
+    graphs = [
+        dict(
+            data=[
+                dict(
+                    x = df['Genre'],
+                    y = df['Global_Sales'],
+                    type='bar'
+                ),
+            ],
+            layout=dict(
+                title='Bar Chart'
+            )
+        ),
+
+        dict(
+            data = [go.Pie(
+                    labels=labels, values=values, hole=.3
+                )
+            ],
+            layout=dict(
+                title='Pie Chart'
+            )
+        )
+    ]
+
+    # Add "ids" to each of the graphs to pass up to the client
+    # for templating
+    ids = ['graph-{}'.format(i) for i, _ in enumerate(graphs)]
+
+    # Convert the figures to JSON
+    # PlotlyJSONEncoder appropriately converts pandas, datetime, etc
+    # objects to their JSON equivalents
+    graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
+
+    return render_template('index.html',
+                           ids=ids,
+                           graphJSON=graphJSON)
+
+
+############################# Sales and User Score prediction ######################################
 @app.route('/predict-sales')
 def pSales():
     return render_template('sale-prediction.html')
 
-
 @app.route('/p-sales-new', methods=['POST'])
 def predicSalesNew():
 
@@ -58,6 +105,33 @@ def predicSalesNew():
 
     return render_template('sale-prediction.html', rVal = output)
 
+@app.route('/user-score-p', methods=['POST'])
+def usPrediction():
+
+    cScore = float(request.form['cScore'])
+    gSales = float(request.form['gSales'])
+    cCount = float(request.form['cCount'])
+
+
+    model = pickle.load(open('MultipleLinearRegression.pkl','rb'))
+    # predict using a value
+
+    new_row = {'Critic_Score': cScore, 'Global_Sales': gSales, 'Critic_Count':cCount}
+    df_Apnd = df.append(new_row, ignore_index=True)
+
+    ##################  Get features and labels     ######################
+    newDF2 = df_Apnd[['Critic_Score','Global_Sales','Critic_Count']]
+    M = newDF2
+    N = df_Apnd.iloc[:,5].values
+
+    l = M.iloc[-1].values
+
+    pred = model.predict(l.reshape(1,-1))[0]
+    pred = round(pred, 2)
+
+    return render_template('sale-prediction.html', rVal = gSales, score = pred)
+
+############################# Genre prediction ######################################
 
 @app.route('/genre-classify')
 def cGenre():
@@ -99,36 +173,101 @@ def genreClassify():
     return render_template('genre-classify.html', genre = pred)
 
 
-@app.route('/uscore-predict')
-def usPredict():
-    return render_template('uscore-prediction.html')
+############################# User Score Clustering ######################################
 
+@app.route('/uscore-clustering')
+def uscoreCluster():
+    return render_template('uscore-cluster.html')
 
-@app.route('/user-score-p', methods=['POST'])
-def usPrediction():
+@app.route('/us-cluster', methods=['POST'])
+def usCluster():
 
     cScore = float(request.form['cScore'])
     gSales = float(request.form['gSales'])
     cCount = float(request.form['cCount'])
 
 
-    model = pickle.load(open('MultipleLinearRegression.pkl','rb'))
+    model = pickle.load(open('ClusteringPredict.pkl','rb'))
     # predict using a value
 
-    new_row = {'Critic_Score': cScore, 'Global_Sales': gSales, 'Critic_Count':cCount}
+    new_row = {'User_Score': cScore, 'Global_Sales': gSales, 'Critic_Score': cCount}
     df_Apnd = df.append(new_row, ignore_index=True)
 
-    ##################  Get features and labels     ######################
-    newDF2 = df_Apnd[['Critic_Score','Global_Sales','Critic_Count']]
+    # Get features and labels
+    newDF2 = df_Apnd[['User_Score','Global_Sales','Critic_Score']]
     M = newDF2
-    N = df_Apnd.iloc[:,5].values
+    N = df_Apnd.iloc[:,3].values
+
+    # Encoding
+    objList2 = M.select_dtypes(include = "object").columns
+    le = LabelEncoder()
+
+    for feat2 in objList2:
+        M[feat2] = le.fit_transform(M[feat2].astype(str))
+
 
     l = M.iloc[-1].values
 
-    pred = model.predict(l.reshape(1,-1))[0]
-    pred = round(pred, 2)
+    clstr = model.predict(l.reshape(1,-1))
+    if clstr == 0:
+        pred = "Cluster A"
+    elif clstr == 1:
+        pred = "Cluster B"
+    elif clstr == 2:
+        pred = "Cluster C"
+    else:
+        pred = "Cluster D"
+
 
-    return render_template('sale-prediction.html', rVal = gSales, score = pred)
+    return render_template('uscore-cluster.html', score = pred)
+
+
+
+
+
+
+
+
+
+@app.route('/test')
+def indexT():
+#    rng = pd.date_range('1/1/2011', periods=7500, freq='H')
+#    ts = pd.Series(np.random.randn(len(rng)), index=rng)
+
+    graphs = [
+        dict(
+            # Create a trace
+            data = [go.Scatter3d(
+                x = df['Global_Sales'],
+                y = df['User_Score'],
+                z = df['Critic_Score'],
+                mode = 'markers',marker = dict(
+                  size = 12,
+                  colorscale = 'Viridis'
+                  )
+                )]
+            )
+    ]
+
+    # Add "ids" to each of the graphs to pass up to the client
+    # for templating
+    ids = ['graph-{}'.format(i) for i, _ in enumerate(graphs)]
+
+    # Convert the figures to JSON
+    # PlotlyJSONEncoder appropriately converts pandas, datetime, etc
+    # objects to their JSON equivalents
+    graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
+
+    return render_template('test.html',
+                           ids=ids,
+                           graphJSON=graphJSON)
+
+
+
+
+
+
+
 
 
 

models/graphs.py

@@ -16,28 +16,4 @@
 cleanedDS = cleanDataset.clean_db()
 
 
-# Declaring the points for first line plot
-X1 = cleanedDS['Genre']
-Y1 = cleanedDS['Global_Sales']
-
-# Setting the figure size
-fig = plt.figure(figsize=(10,5))
-
-# plotting the first plot
-plt.bar(X1, Y1, align='center', alpha=0.5)
-
-# Labeling the X-axis 
-plt.xlabel('Genre') 
-
-# Labeling the Y-axis 
-plt.ylabel('Global_Sales') 
-
-# Give a title to the graph
-plt.title('Total sales of the Genres') 
-
-# Show a legend on the plot 
-plt.legend() 
-
-#Saving the plot as an image
-fig.savefig('../static/images/line_plot.jpg', bbox_inches='tight', dpi=150)
 

static/assets/css/style.css

@@ -625,7 +625,7 @@
   border-color: #343a40; }
 
 .social-box {
-  min-height: 160px;
+  min-height: 100px;
   margin-bottom:24px;
   margin-bottom: 1.5rem;
   text-align: center;
@@ -1781,7 +1781,7 @@ header .form-inline {
   padding-right: 10px; }
 
 .social-box i {
-  line-height: 110px; }
+  line-height: 80px; }
 
 .social-box ul {
   display: inline-block;
@@ -2537,7 +2537,6 @@ h1,h2,h3,h4,h5,h6 {
 }
 
 p {
-	font-family: Montserrat-Regular;
 	font-size: 14px;
 	line-height: 1.7;
 	color: #666666;

templates/genre-classify.html

@@ -58,7 +58,7 @@ <h3 class="menu-title">UI elements</h3><!-- /.menu-title -->
                         <a href="/genre-classify"> <i class="menu-icon fa fa-area-chart"></i>Classify Genre</a>
                     </li>
                     <li>
-                        <a href="/uscore-predict"> <i class="menu-icon fa fa-table"></i>User Score</a>
+                        <a href="/uscore-clustering"> <i class="menu-icon fa fa-table"></i>Cluster User Score</a>
                     </li>