Update main.py

main.py

@@ -452,7 +452,7 @@ def select_files(coaching_code):
 
 # Main route to get sorted scores
 @app.post("/get_sorted_scores")
-async def get_sorted_scores(data: CoachingCodeRequest):
+async def get_revison_chapters(data: CoachingCodeRequest):
     journal_file, panic_button_file, test_file = select_files(data.coachingCode)
 
     # Load data into DataFrames
@@ -523,47 +523,33 @@ async def get_sorted_scores(data: CoachingCodeRequest):
             'test_scores': test_scores_dict
         })
 
-    # Define scoring weights
-    academic_weights = {'BACKLOGS': -5, 'MISSED CLASSES': -4, 'NOT UNDERSTANDING': -3, 'BAD MARKS': -3, 'LACK OF MOTIVATION': -3}
-    non_academic_weights = {'EMOTIONAL FACTORS': -3, 'PROCRASTINATE': -2, 'LOST INTEREST': -4, 'LACK OF FOCUS': -2, 'GOALS NOT ACHIEVED': -2, 'LACK OF DISCIPLINE': -2}
-    max_weighted_panic_score = sum([max(academic_weights.values()) * 3, max(non_academic_weights.values()) * 3])
-
-    def calculate_potential_score(row):
-        if row['test_scores']:
-            avg_test_score = sum(row['test_scores'].values()) / len(row['test_scores'])
-            test_score_normalized = (avg_test_score / 40) * 70
-        else:
-            test_score_normalized = 0
-        student_panic_score = 0
-        if row['panic_button']:
-            for factor, count in row['panic_button'].items():
-                if factor in academic_weights:
-                    student_panic_score += academic_weights[factor] * count
-                elif factor in non_academic_weights:
-                    student_panic_score += non_academic_weights[factor] * count
-        else:
-            student_panic_score = 0
-        panic_score = 20 * (1 - (student_panic_score / max_weighted_panic_score) if max_weighted_panic_score != 0 else 1)
-        if pd.notna(row['productivity_yes_no']) and row['productivity_yes_no'] == 'Yes':
-            if pd.notna(row['productivity_rate']):
-                journal_score = (float(row['productivity_rate']) / 10) * 10
-            else:
-                journal_score = 0
-        elif pd.notna(row['productivity_yes_no']) and row['productivity_yes_no'] == 'No':
-            if pd.notna(row['productivity_rate']):
-                journal_score = (float(row['productivity_rate']) / 10) * 5
-            else:
-                journal_score = 0
-        else:
-            journal_score = 0
-        total_potential_score = test_score_normalized + panic_score + journal_score
-        return total_potential_score
+
 
     merged_df = merged_data_cleaned.groupby('user_id').apply(process_group).reset_index()
-    merged_df['potential_score'] = merged_df.apply(calculate_potential_score, axis=1)
-    merged_df['potential_score'] = merged_df['potential_score'].round(2)
-    sorted_df = merged_df[['user_id', 'potential_score']].sort_values(by='potential_score', ascending=False)
-    result = sorted_df.to_dict(orient="records")
+    revision_dict = {}
+
+# Define the score threshold for requiring revision
+    threshold = 16
+
+# Iterate through each student's test_scores
+    for index, row in merged_df.iterrows():
+        user_id = row['user_id']
+        test_scores = row['test_scores']
+
+    # Check each chapter's score for this student
+        for chapter, score in test_scores.items():
+            if score < threshold:
+                if chapter not in revision_dict:
+                revision_dict[chapter] = []
+                revision_dict[chapter].append(user_id)
+
+# Convert the dictionary to the desired DataFrame format
+    revision_df = pd.DataFrame({
+        'chapter_name': list(revision_dict.keys()),
+        'user_ids_needing_revision': [', '.join(users) for users in revision_dict.values()]
+    })
+ 
     
-    return {"sorted_scores": result}
+    revision_json = revision_df.to_dict(orient="records")
+    return {"data": revision_json}