Update app.py

app.py

@@ -1,4 +1,4 @@
- streamlit as st
+import streamlit as st
 from PIL import Image
 import os
 import base64
@@ -9,9 +9,7 @@ from reportlab.lib.pagesizes import letter
 from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
 from reportlab.lib.styles import getSampleStyleSheet
 
-# ======================
 # CONFIGURATION SETTINGS
-# ======================
 st.set_page_config(
     page_title="Smart Diet Analyzer",
     page_icon="🍎",
@@ -21,10 +19,7 @@ st.set_page_config(
 
 ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
 
-# ======================
 # UTILITY FUNCTIONS
-# ======================
-
 def initialize_api_client():
     """Initialize Groq API client"""
     load_dotenv()
@@ -103,24 +98,23 @@ def generate_analysis(uploaded_file, client):
             model="llama-3.2-11b-vision-preview",
             messages=[
                 {
-  "type": "text",
-  "text": """
-    You are an expert nutritionist with advanced image analysis capabilities.  
-    Your task is to analyze the provided image, identify all visible food items, and estimate their calorie content with high accuracy.  
-    **Instructions:**  
-    - Identify and list each food item visible in the image.  
-    - For each item, estimate the calorie content based on standard nutritional data, considering portion size, cooking method, and food density.  
-    - Clearly mark any calorie estimate as "approximate" if based on assumptions due to unclear details.  
-    - Calculate and provide the total estimated calories for the entire meal.  
-    **Output Format:**  
-    - Food Item 1: [Name] – Estimated Calories: [value] kcal  
-    - Food Item 2: [Name] – Estimated Calories: [value] kcal  
-    - ...  
-    - **Total Estimated Calories:** [value] kcal  
-    If the image lacks sufficient detail or is unclear, specify the limitations and include your confidence level in the estimate as a percentage.
-  """
-}
-
+                    "type": "text",
+                    "text": """
+                        You are an expert nutritionist with advanced image analysis capabilities.  
+                        Your task is to analyze the provided image, identify all visible food items, and estimate their calorie content with high accuracy.  
+                        **Instructions:**  
+                        - Identify and list each food item visible in the image.  
+                        - For each item, estimate the calorie content based on standard nutritional data, considering portion size, cooking method, and food density.  
+                        - Clearly mark any calorie estimate as "approximate" if based on assumptions due to unclear details.  
+                        - Calculate and provide the total estimated calories for the entire meal.  
+                        **Output Format:**  
+                        - Food Item 1: [Name] – Estimated Calories: [value] kcal  
+                        - Food Item 2: [Name] – Estimated Calories: [value] kcal  
+                        - ...  
+                        - **Total Estimated Calories:** [value] kcal  
+                        If the image lacks sufficient detail or is unclear, specify the limitations and include your confidence level in the estimate as a percentage.
+                    """
+                }
             ],
             temperature=0.2,
             max_tokens=400,
@@ -131,10 +125,7 @@ def generate_analysis(uploaded_file, client):
         st.error(f"API communication error: {e}")
         return None
 
-# ======================
 # UI COMPONENTS
-# ======================
-
 def display_main_interface():
     """Render primary application interface"""
     logo_b64 = encode_image("src/logo.png")
@@ -184,10 +175,7 @@ def render_sidebar(client):
                     st.session_state.analysis_result = report
                     st.rerun()
 
-# ======================
 # APPLICATION ENTRYPOINT
-# ======================
-
 def main():
     """Primary application controller"""
     client = initialize_api_client()
@@ -195,4 +183,4 @@ def main():
     render_sidebar(client)
 
 if __name__ == "__main__":
-    main()
+    main()