Update app.py

app.py

@@ -8,7 +8,7 @@ from scipy.stats import chi2_contingency
 import numpy as np
 import seaborn as sns
 import matplotlib.pyplot as plt
-
+import os
 def cramers_V(var1,var2) :
   crosstab =np.array(pd.crosstab(var1,var2, rownames=None, colnames=None)) # Cross table building
   stat = chi2_contingency(crosstab)[0] # Keeping of the test statistic of the Chi2 test
@@ -17,6 +17,7 @@ def cramers_V(var1,var2) :
   return (stat/(obs*mini))
 
 def predict(file_obj):
+    
     df = pd.read_csv(file_obj.name)
     cat_df = df.select_dtypes(include=['object'])
     rows= []
@@ -28,20 +29,27 @@ def predict(file_obj):
       rows.append(col)
     cramers_results = np.array(rows)
     df_final= pd.DataFrame(cramers_results, columns = cat_df.columns, index =cat_df.columns)
-    #plt.close()
+    
    # return df_final
-    data = np.random.randint(low=1,
-                         high=1000,
-                         size=(10, 10))
+    # data = np.random.randint(low=1,
+    #                      high=1000,
+    #                      size=(10, 10))
     annot = True
   
 # plotting the heatmap
+    plt.close('all')
     hm = sns.heatmap(data=df_final,
                     annot=annot)
-    # plt.show()
-    # plt.figure()
+    # return plt.show()
+    # return plt.figure()
+    plt.savefig('box.png')
+    
     return plt.gcf()
+    # plt.clf()
+    # return plt.plot()
+   
+    
 
 
-iface = gr.Interface(predict,inputs="file",outputs="plot",theme="dark-peach",examples=["StudentsPerformance.csv"],title='Correlation Tool for Categorical features',description="This tool identifies and explains the correlation between categorical features.")
+iface = gr.Interface(predict,inputs="file",outputs="plot",theme="dark-peach",title='Correlation Tool for Categorical features',description="This tool identifies and explains the correlation between categorical features.")
 iface.launch(inline=False)