Update app.py

app.py

@@ -1,18 +1,13 @@
-import os
-# 1. Force legacy Keras BEFORE any other imports
-os.environ["TF_USE_LEGACY_KERAS"] = "1"
-
 import gradio as gr
 import tensorflow as tf
-import tf_keras as keras  # Use the legacy loader
 import numpy as np
 from PIL import Image
 
-# 2. Load the model using the legacy loader
-MODEL_PATH = "model/FOOD CAL MODEL.h5"
-model = keras.models.load_model(MODEL_PATH)
+# 1. Load your model
+MODEL_PATH = "model/best_food_model.keras"
+model = tf.keras.models.load_model(MODEL_PATH)
 
-# 3. Full Food-101 Labels
+# 2. Labels & Database (Make sure to include the full dictionary from previous turns)
 LABELS = [
     'apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare', 'beet_salad', 'beignets', 
     'bibimbap', 'bread_pudding', 'breakfast_burrito', 'bruschetta', 'caesar_salad', 'cannoli', 'caprese_salad', 
@@ -29,8 +24,6 @@ LABELS = [
     'shrimp_and_grits', 'spaghetti_bolognese', 'spaghetti_carbonara', 'spring_rolls', 'steak', 'strawberry_shortcake', 
     'sushi', 'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles'
 ]
-
-# 4. Full Nutrition Database
 NUTRITION_DB = {
     'apple_pie': {'cal': 237, 'protein': 1.9, 'carbs': 34.0, 'fat': 11.0},
     'baby_back_ribs': {'cal': 292, 'protein': 17.0, 'carbs': 0.0, 'fat': 24.0},
@@ -135,14 +128,13 @@ NUTRITION_DB = {
     'waffles': {'cal': 291, 'protein': 8.0, 'carbs': 33.0, 'fat': 14.0}
 }
 
-# 5. Prediction Logic
 def predict_nutrition(img):
     if img is None:
         return None, "Please upload an image."
-        
-    # Preprocessing using tf_keras
+    
+    # Preprocessing
     img = Image.fromarray(img).resize((224, 224))
-    img_array = keras.preprocessing.image.img_to_array(img)
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
     img_array = np.expand_dims(img_array, axis=0) / 255.0
 
     # Prediction
@@ -163,7 +155,6 @@ def predict_nutrition(img):
     nutri_markdown = f"""
     ### 🥗 Nutrition Facts: {clean_name}
     *(Estimated per 100g)*
-    
     | Nutrient | Amount |
     | :--- | :--- |
     | 🔥 **Calories** | {nutri['cal']} kcal |
@@ -174,9 +165,9 @@ def predict_nutrition(img):
     
     return confidences, nutri_markdown
 
-# 6. Gradio Interface
+# 3. Enhanced Gradio UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🍎 Food-101 Nutrition AI")
+    gr.Markdown("# 🍎 Food-101 Deep Learning Classifier")
     gr.Markdown("Identify food items and see their nutritional breakdown instantly.")
     
     with gr.Row():
@@ -185,7 +176,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             submit_btn = gr.Button("Analyze Meal", variant="primary")
             
         with gr.Column(scale=1):
+            # Output 1: Top 3 Confidence Chart
             output_chart = gr.Label(num_top_classes=3, label="Top 3 Predictions")
+            # Output 2: Nutrition Table
             output_nutri = gr.Markdown(label="Nutrition Breakdown")
     
     submit_btn.click(
@@ -194,5 +187,8 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         outputs=[output_chart, output_nutri]
     )
 
+    gr.Markdown("---")
+    gr.Markdown("*Note: This model is for educational purposes. For medical dietary tracking, please consult a professional.*")
+
 if __name__ == "__main__":
     demo.launch()