replace with foundation category

algo.py

@@ -145,7 +145,7 @@ class Algo:
                     'is_food': False,
                     'food_nonfood_score': 1.0,
                     'wweia_category': 'Non-Food Item',
-                    'usda_category': 'Non-Food Item',
+                    'foundation_category': 'Non-Food Item',
                     'water_content': None,
                     'dry_matter_content': None,
                     'leakage': None
@@ -187,7 +187,7 @@ class Algo:
                 'matching_word': 'Heterogeneous Mixture',
                 'dictionary_word': 'Heterogeneous Mixture',
                 'wweia_category': 'Heterogeneous Mixture',
-                'usda_category': 'Heterogeneous Mixture',
+                'foundation_category': 'Heterogeneous Mixture',
                 'dry_matter_content': 0.27,
                 'water_content': 0.73,
                 'leakage': 0.1
@@ -197,7 +197,7 @@ class Algo:
                 'matching_word': most_conservative_mapping['matching_word'],
                 'dictionary_word': f"{most_conservative_mapping['dictionary_word']} (Lowest DMC)",
                 'wweia_category': most_conservative_mapping['wweia_category'],
-                'usda_category': most_conservative_mapping['usda_category'],
+                'foundation_category': most_conservative_mapping['foundation_category'],
                 'dry_matter_content': most_conservative_mapping['dry_matter_content'],
                 'water_content': most_conservative_mapping['water_content'],
                 'leakage': most_conservative_mapping['leakage']
@@ -283,7 +283,7 @@ class Algo:
 
         mapping.update({
             'wweia_category': dictionary_result['wweia_category'] if dictionary_result else None,
-            'usda_category': dictionary_result['usda_category'] if dictionary_result else None,
+            'foundation_category': dictionary_result['foundation_category'] if dictionary_result else None,
             'water_content': dictionary_result['water_content'] if dictionary_result else None,
             'dry_matter_content': dictionary_result['dry_matter_content'] if dictionary_result else None,
             'leakage': dictionary_result['leakage'] if dictionary_result else None,

app.py

@@ -25,10 +25,10 @@ def process_input(input_text, csv_file):
 
 
     print(f" - result -> {results}")
-    df = pd.DataFrame(results, columns=["input_word", "cleaned_word", 'matching_word', 'dictionary_word', 'usda_category', 'wweia_category', 'dry_matter_content', 
+    df = pd.DataFrame(results, columns=["input_word", "cleaned_word", 'matching_word', 'dictionary_word', 'foundation_category', 'wweia_category', 'dry_matter_content', 
     'water_content', 'similarity_score', 'confidence_score', 'similar_words', 'is_food', 'food_nonfood_score'])
     # Filter to only required columns
-    df_filtered = df[["input_word", "dictionary_word", "is_food", "usda_category", 'wweia_category', 'dry_matter_content', "water_content",  "similarity_score", "food_nonfood_score"]]
+    df_filtered = df[["input_word", "dictionary_word", "is_food", "foundation_category", 'wweia_category', 'dry_matter_content', "water_content",  "similarity_score", "food_nonfood_score"]]
     return df_filtered
 
 # Gradio interface

category_mapper.py

@@ -86,7 +86,7 @@ for row in tqdm(rows, desc="Processing"):
     print()
     fdc_id = row['fdc_id']
     food_item = row['description']
-    category = row['usda_category']
+    category = row['foundation_category']
 
     print(f"Processing '{food_item}'")
 

db/db_utils.py

@@ -44,7 +44,7 @@ def initialize_db(conn):
         CREATE TABLE IF NOT EXISTS dictionary (
             fdc_id INTEGER PRIMARY KEY,
             description TEXT,
-            usda_category TEXT,
+            foundation_category TEXT,
             wweia_category TEXT,
             water_content REAL,
             dry_matter_content REAL,
@@ -63,7 +63,7 @@ def initialize_db(conn):
             dictionary_word TEXT,
             is_food BOOLEAN,
             wweia_category TEXT,
-            usda_category TEXT,
+            foundation_category TEXT,
             dry_matter_content REAL,
             leakage REAL,
             weight REAL,
@@ -153,7 +153,7 @@ def store_mapping_to_db(cursor, conn, mapping):
 def store_result_to_db(cursor, conn, run_key, result):
     try:
         cursor.execute('''
-            INSERT INTO results (run_key, run_row, date, input_word, dictionary_word, is_food, usda_category, wweia_category, dry_matter_content, leakage, weight, weight_metric_tonnes, donor, similarity_score, food_nonfood_score, distance, ef, mt_lb_mile, baseline_emissions, leakage_emissions, project_emissions, total_emissions_reduction)
+            INSERT INTO results (run_key, run_row, date, input_word, dictionary_word, is_food, foundation_category, wweia_category, dry_matter_content, leakage, weight, weight_metric_tonnes, donor, similarity_score, food_nonfood_score, distance, ef, mt_lb_mile, baseline_emissions, leakage_emissions, project_emissions, total_emissions_reduction)
             VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
         ''', (
             run_key,
@@ -162,7 +162,7 @@ def store_result_to_db(cursor, conn, run_key, result):
             result['input_word'], 
             result['dictionary_word'], 
             result['is_food'], 
-            result['usda_category'], 
+            result['foundation_category'], 
             result['wweia_category'], 
             result['dry_matter_content'],
             result['leakage'],

mapping_template.py

@@ -9,7 +9,7 @@ def empty_template(input_word):
         'food_nonfood_score': None,
         'matching_word': None,
         'dictionary_word': None,
-        'usda_category': None,
+        'foundation_category': None,
         'wweia_category': None,
         'dry_matter_content': None,
         'water_content': None,