Update app.py

app.py

@@ -1,4 +1,4 @@
-import streamlit as st
+ streamlit as st
 from PIL import Image
 import os
 import base64
@@ -44,30 +44,15 @@ def encode_image(image_path):
         return ""
 
 
-def resize_image(image):
-    """Resize the image to reduce size before encoding it to base64"""
-    max_width = 800  # Max width in pixels
-    max_height = 800  # Max height in pixels
-    image.thumbnail((max_width, max_height))
-    return image
-
-
-def compress_image(image):
-    """Compress the image to JPEG format with a lower quality to reduce size"""
-    buffer = io.BytesIO()
-    image.save(buffer, format="JPEG", quality=70)  # Reduce quality to 70%
-    return base64.b64encode(buffer.getvalue()).decode('utf-8')
-
-
 def process_image(uploaded_file):
-    """Convert uploaded image file to base64 string with compression"""
+    """Convert image to base64 string"""
     try:
         image = Image.open(uploaded_file)
-        image = resize_image(image)  # Resize the image to reduce its size
-        base64_image = compress_image(image)  # Compress the image
-        return base64_image, "jpeg"  # Use 'jpeg' as format
+        buffer = io.BytesIO()
+        image.save(buffer, format=image.format)
+        return base64.b64encode(buffer.getvalue()).decode('utf-8'), image.format
     except Exception as e:
-        st.error(f"Error processing image: {e}")
+        st.error(f"Image processing error: {e}")
         return None, None
 
 
@@ -106,10 +91,9 @@ def generate_pdf(report_text, logo_b64):
 
 
 def generate_analysis(uploaded_file, client):
-    """Generate nutrition analysis using AI (Groq API)"""
+    """Generate AI-powered food analysis"""
     base64_image, img_format = process_image(uploaded_file)
     if not base64_image:
-        st.error("Failed to process image. Cannot generate analysis.")
         return None
     
     image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
@@ -117,35 +101,34 @@ def generate_analysis(uploaded_file, client):
     try:
         response = client.chat.completions.create(
             model="llama-3.2-11b-vision-preview",
-            messages=[{
-                "role": "system",  # Define the role for the system message
-                "content": """
-                    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.
-                """
-            },
-            {
-                "role": "user",  # Define the role for the user message
-                "content": f"Analyze this image and provide the nutrition analysis: {image_url}"
-            }],
+            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.
+  """
+}
+
+            ],
             temperature=0.2,
             max_tokens=400,
             top_p=0.5
         )
         return response.choices[0].message.content
     except Exception as e:
-        st.error(f"Error communicating with API: {e}")
+        st.error(f"API communication error: {e}")
         return None
 
 # ======================
@@ -212,4 +195,4 @@ def main():
     render_sidebar(client)
 
 if __name__ == "__main__":
-    main()
+    main()