Update app.py

app.py

@@ -1,10 +1,45 @@
 import gradio as gr
 import pandas as pd
+import numpy as np
+import pandas as pd
+import scipy.stats as ss
+import seaborn as sns
+from scipy.stats import chi2_contingency
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+rows= []
+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
+  obs = np.sum(crosstab) # Number of observations
+  mini = min(crosstab.shape)-1 # Take the minimum value between the columns and the rows of the cross table
+  return (stat/(obs*mini))
 
 def predict(file_obj):
-    df = pd.read_csv(file_obj)
-    return df
+    df = pd.read_csv(file_obj.name,dtype=str)
+    cat_df = df.select_dtypes(include=['object']).copy()
+    for var1 in cat_df:
+      col = []
+      for var2 in cat_df :
+        cramers =cramers_V(cat_df[var1], cat_df[var2]) # Cramer's V test
+        col.append(round(cramers,2)) # Keeping of the rounded value of the Cramer's V  
+      rows.append(col)
+    cramers_results = np.array(rows)
+    df_final= pd.DataFrame(cramers_results, columns = cat_df.columns, index =cat_df.columns)
+   # return df_final
+    data = np.random.randint(low=1,
+                         high=1000,
+                         size=(10, 10))
+    annot = True
+  
+# plotting the heatmap
+    hm = sns.heatmap(data=df_final,
+                    annot=annot)
+    # plt.show()
+    # plt.figure()
+    return plt.gcf()
 
 
-iface = gr.Interface(predict,inputs="file",outputs="dataframe",title='Cramer V Test',description="Cramer Test")
+iface = gr.Interface(predict,inputs="file",outputs="plot",title='Correlation Tool for Categorical features',description="This tool identifies and explains the correlation between categorical features.")
 iface.launch(inline=False)