added new functions, fixed data processing:

- changed to top 5 features
- rounded floats to 2 decimals

utils.py

@@ -2,8 +2,6 @@ import streamlit.components.v1 as components
 import streamlit as st
 from random import randrange, uniform
 import pandas as pd
-import joblib
-import dill
 import logging
 import numpy as np
 
@@ -52,24 +50,26 @@ def get_explainability_texts(shap_values, feature_texts):
     # Sort dictionaries based on the magnitude of values
     sorted_positive_indices = [index for index, _ in sorted(positive_dict.items(), key=lambda item: abs(item[1]), reverse=True)]
     positive_texts = [feature_texts[x] for x in sorted_positive_indices]
-    if len(positive_texts) > 8:
-        positive_texts = positive_texts[:8]
+    positive_texts = positive_texts[2:]
+    if len(positive_texts) > 5:
+        positive_texts = positive_texts[:5]
     return positive_texts, sorted_positive_indices
 
 
 def get_explainability_values(pos_indices, datapoint):
-    data = datapoint.iloc[0].tolist()  
-    transformed_data = transformation(input=data, categories=CATEGORIES)
-    print(transformed_data)
+    data = datapoint.iloc[0].tolist()
+    rounded_data = [round(value, 2) if isinstance(value, float) else value for value in data]
+    transformed_data = transformation(input=rounded_data, categories=CATEGORIES)
     vals = []
     for idx in pos_indices:
         if idx in range(7,11) or idx in range(13,18):
-            val = str(bool(data[idx])).capitalize()
+            val = str(bool(transformed_data[idx])).capitalize()
         else:
-            val = data[idx]
+            val = transformed_data[idx]
         vals.append(val)
-    if len(vals) > 8:
-        vals = vals[:8]
+    vals = vals[2:]
+    if len(vals) > 5:
+        vals = vals[:5]
     return vals
 
 def get_fake_certainty():
@@ -120,10 +120,39 @@ def get_model_url():
         deployment_id = ""
     return model_url, workspace_id, deployment_id
 
-def get_explanation(certainty, explainability_texts, explainability_values):
+def get_comment_explanation(certainty, explainability_texts, explainability_values):
     cleaned = [x.replace(':', '') for x in explainability_texts]
-    fi = [f'{explainability_texts[i]} is {x}' for i,x in enumerate(cleaned)]
+    fi = [f'{cleaned[i]} is {x}' for i, x in enumerate(explainability_values)]
     fi.insert(0, 'Important suspicious features: ')
     result = '\n'.join(fi)
     comment = f"Model certainty is {certainty}" + '\n''\n' + result
-    return comment
+    return comment
+
+def create_data_input_table(datapoint, col_names):
+    st.subheader("Flagged Transaction:")
+    data = datapoint.iloc[0].tolist()
+    data[7:12] = [bool(value) for value in data[7:12]]
+    rounded_list = [round(value, 2) if isinstance(value, float) else value for value in data]
+    df = pd.DataFrame({"Feature name": col_names, "Value": rounded_list })
+    st.dataframe(df, hide_index=True, width=450, height=35*len(df)+38)
+
+# Create a function to generate a table
+def create_table(texts, values, title):
+    df = pd.DataFrame({"Feature Explanation": texts, 'Value': values})
+    st.markdown(f'#### {title}')  # Markdown for styling
+    st.dataframe(df, hide_index=True, width=450)  # Display a simple table
+
+
+def ChangeButtonColour(widget_label, font_color, background_color='transparent'):
+    htmlstr = f"""
+        <script>
+            var elements = window.parent.document.querySelectorAll('button');
+            for (var i = 0; i < elements.length; ++i) {{ 
+                if (elements[i].innerText == '{widget_label}') {{ 
+                    elements[i].style.color ='{font_color}';
+                    elements[i].style.background = '{background_color}'
+                }}
+            }}
+        </script>
+        """
+    components.html(f"{htmlstr}", height=0, width=0)