Update app.py

app.py

@@ -31,7 +31,7 @@ GI_RANGES = {
     "high": (70, 100)
 }
 
-# Utility Functions (unchanged except where noted)
+# Utility Functions (mostly unchanged)
 def estimate_gi_timing(gi_value: Optional[int]) -> tuple[float, float]:
     if gi_value is None:
         return 1.0, 2.5
@@ -77,12 +77,16 @@ def classify_food(image):
         print(f"Classify food error: {e}")
         return "unknown"
 
-def get_food_nutrition(food_name: str, food_data: pd.DataFrame, weight_grams: Optional[float] = None) -> Optional[Dict[str, Any]]:
+def get_food_nutrition(food_name: str, food_data: pd.DataFrame, weight_grams: Optional[float] = None) -> tuple[Optional[Dict[str, Any]], str]:
     food_name_lower = food_name.lower().strip()
     matches = get_close_matches(food_name_lower, food_data['food_name'].tolist(), n=1, cutoff=0.6)
+    correction_note = ""
 
     if matches:
-        matched_row = food_data[food_data['food_name'] == matches[0]].iloc[0]
+        corrected_name = matches[0]
+        if corrected_name != food_name_lower:
+            correction_note = f"Note: '{food_name}' corrected to '{corrected_name}'"
+        matched_row = food_data[food_data['food_name'] == corrected_name].iloc[0]
         base_carbs = float(matched_row.get('unit_serving_carb_g', matched_row.get('carb_g', 0.0)))
         serving_size = matched_row.get('servings_unit', 'unknown')
         gi_value = matched_row.get('glycemic_index', None)
@@ -94,10 +98,9 @@ def get_food_nutrition(food_name: str, food_data: pd.DataFrame, weight_grams: Op
             except (ValueError, TypeError):
                 gi_value = None
 
-        # Adjust portion based on weight in grams if provided
         if weight_grams and serving_size != 'unknown':
             try:
-                serving_weight = float(matched_row.get('serving_weight_grams', 100))  # Default to 100g if not specified
+                serving_weight = float(matched_row.get('serving_weight_grams', 100))
                 portion_size = weight_grams / serving_weight
             except (ValueError, TypeError):
                 portion_size = 1.0
@@ -106,7 +109,7 @@ def get_food_nutrition(food_name: str, food_data: pd.DataFrame, weight_grams: Op
 
         adjusted_carbs = base_carbs * portion_size
 
-        return {
+        nutrition_info = {
             'matched_food': matched_row['food_name'],
             'category': matched_row.get('primarysource', 'unknown'),
             'subcategory': 'unknown',
@@ -117,7 +120,8 @@ def get_food_nutrition(food_name: str, food_data: pd.DataFrame, weight_grams: Op
             'notes': 'none',
             'glycemic_index': gi_value
         }
-    return None
+        return nutrition_info, correction_note
+    return None, f"No close match found for '{food_name}'"
 
 def determine_gi_level(gi_value: Optional[int]) -> str:
     if gi_value is None:
@@ -188,7 +192,7 @@ def calculate_insulin_needs(carbs: float, glucose_current: float, glucose_target
     }
 
 def create_detailed_report(nutrition_info: Dict[str, Any], insulin_info: Dict[str, Any],
-                         current_basal_rate: float) -> tuple[str, str, str]:
+                         current_basal_rate: float, correction_note: str) -> tuple[str, str, str]:
     gi_level = determine_gi_level(nutrition_info.get('glycemic_index'))
     peak_time, duration = estimate_gi_timing(nutrition_info.get('glycemic_index'))
 
@@ -203,6 +207,7 @@ def create_detailed_report(nutrition_info: Dict[str, Any], insulin_info: Dict[st
     - Carbs per Serving: {nutrition_info['base_carbs']}g
     - Portion Multiplier: {nutrition_info['portion_multiplier']}x
     - Total Carbs: {nutrition_info['adjusted_carbs']}g
+    {correction_note}
     """
 
     insulin_details = f"""
@@ -235,17 +240,15 @@ def diabetes_dashboard(initial_glucose, food_image, food_name_input, weight_gram
     if food_data.empty:
         return "Error loading food data", None, None, None, None
 
-    # Determine food name: use manual input if provided, otherwise classify from image
     if food_name_input and food_name_input.strip():
         food_name = food_name_input.strip()
     else:
         food_name = classify_food(food_image)
 
-    # Get nutrition info with weight in grams if provided
-    nutrition_info = get_food_nutrition(food_name, food_data, weight_grams)
+    nutrition_info, correction_note = get_food_nutrition(food_name, food_data, weight_grams)
 
     if not nutrition_info:
-        return f"No nutrition data for '{food_name}'", None, None, None, None
+        return correction_note, None, None, None, None
 
     try:
         basal_rates = json.loads(basal_rates_input)
@@ -261,9 +264,8 @@ def diabetes_dashboard(initial_glucose, food_image, food_name_input, weight_gram
         return insulin_info['error'], None, None, None, None
 
     current_basal_rate = get_basal_rate(12, basal_rates)
-    glucose_meal_details, insulin_details, basal_details = create_detailed_report(nutrition_info, insulin_info, current_basal_rate)
+    glucose_meal_details, insulin_details, basal_details = create_detailed_report(nutrition_info, insulin_info, current_basal_rate, correction_note)
 
-    # Glucose Prediction (unchanged)
     hours = list(range(time_hours))
     glucose_levels = []
     current_glucose = initial_glucose
@@ -291,7 +293,7 @@ def diabetes_dashboard(initial_glucose, food_image, food_name_input, weight_gram
 
     return glucose_meal_details, insulin_details, basal_details, insulin_info['total_bolus'], fig
 
-# Updated Gradio Interface
+# Gradio Interface (unchanged)
 with gr.Blocks(title="Type 1 Diabetes Management Dashboard") as app:
     gr.Markdown("# Type 1 Diabetes Management Dashboard")