Update app.py

app.py

@@ -142,14 +142,14 @@ if uploaded_file is not None:
     for item in json_output:
         for label, phrases in item.items():
             for phrase in phrases:
-                data.append({'Label': label, 'Dimensions': phrase})
+                data.append({'Rater': label, 'Dimensions': phrase})
     
     df4 = pd.DataFrame(data)
 
     #Step 9: Converting Streangths and Weaknesses with scores into json
 
     # Filter dataframes based on 'Label' value
-    boss, direct, colleague, other_colleague = [df4[df4['Label'] == label].copy() for label in ['Boss', 'Direct', 'Colleagues', 'Colleague (o']]
+    boss, direct, colleague, other_colleague = [df4[df4['Rater'] == label].copy() for label in ['Boss', 'Direct', 'Colleagues', 'Colleague (o']]
     
     # Create mapping dictionaries from df3
     mappings = {
@@ -170,32 +170,24 @@ if uploaded_file is not None:
     colleague = colleague.sort_values(by = 'Colleague_score', ascending = False).reset_index(drop = True)
     other_colleague = other_colleague.sort_values(by = 'Other_colleague_score', ascending = False).reset_index(drop = True)
 
-    
-    boss_json = boss.iloc[:,1:].head(3).to_dict(orient='records')
-    prompt = f"You are a corporate trainer who guides me how to behave in corporate settings. You will analyze the top 3 strengths and scores{boss_json} rated by my boss out of 6. You will generate a nudge for each dimension to improveupon these strengths." 
-         
+    def assign_strength_weakness(df):
+    df['Strength/Weakness'] = np.nan
+    df.loc[df.index.isin([0, 1, 2]) & df['Score'].notna(), 'Strength/Weakness'] = 'S'
+    df.loc[df.index.isin([3, 4, 5]) & df['Score'].notna(), 'Strength/Weakness'] = 'W'
+    return df
+
+    # Apply the function to each DataFrame
+    boss = assign_strength_weakness(boss)
+    direct = assign_strength_weakness(direct)
+    colleague = assign_strength_weakness(colleague)
+    other_colleague = assign_strength_weakness(other_colleague)
+
+    df5 = pd.concat([boss, direct, colleague, other_colleague], axis = 0)
+    df5 = df5.dropna()
+  
     st.write("## Output:")
-    st.write("### 1. Extracted data: Dimensions Assessment of the leader by: Boss, Colleagues, Colleagues (other b.), Direct reports, Customers and All Raters")
-    st.write("### 2. Derived data: Self score")
-    st.write("#### Dataset Table")
+    st.write("### 1. Extracted dataset: Dimensions, Compentency Cluster, Raters and Scores by Raters")
     st.dataframe(df_combined)
     
-    st.write("### 3.1 Extracted list of Strengths rated by Boss")
-    st.write(boss.head(3))
-    st.write("### 3.2 Extracted list of Weaknesses rated by Boss")
-    
-    st.write(boss.tail(3))
-    st.write("### 4.1 Extracted list of Strengths rated by Direct Reports")
-    st.write(direct.head(3))
-    st.write("### 4.2 Extracted list of Weaknesses rated by Direct Reports")
-    st.write(direct.tail(3))
-    
-    st.write("### 5.1 Extracted list of Strengths rated by Colleague")
-    st.write(colleague.head(3))
-    st.write("### 5.2 Extracted list of Weaknesses rated by Colleague")
-    st.write(colleague.tail(3))
-    
-    st.write("### 6.1 Extracted list of Strengths rated by Other Colleague")
-    st.write(other_colleague.head(3))
-    st.write("### 6.2 Extracted list of Weaknesses rated by Other Colleague")
-    st.write(other_colleague.tail(3))
+    st.write("### 2. Extracted list of Strengths and Weaknesses rated by each Rater")
+    st.write(df5)