Update app.py

app.py

@@ -6,10 +6,12 @@ import io
 from dotenv import load_dotenv
 from groq import Groq
 from reportlab.lib.pagesizes import letter
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
 from reportlab.lib.styles import getSampleStyleSheet
 
+# ======================
 # CONFIGURATION SETTINGS
+# ======================
 st.set_page_config(
     page_title="Smart Diet Analyzer",
     page_icon="🍎",
@@ -19,7 +21,10 @@ st.set_page_config(
 
 ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
 
+# ======================
 # UTILITY FUNCTIONS
+# ======================
+
 def initialize_api_client():
     """Initialize Groq API client"""
     load_dotenv()
@@ -51,35 +56,13 @@ def process_image(uploaded_file):
         return None, None
 
 
-def generate_pdf(report_text, logo_b64):
-    """Generate a PDF report with logo"""
+def generate_pdf(report_text):
+    """Generate a PDF report"""
     buffer = io.BytesIO()
     doc = SimpleDocTemplate(buffer, pagesize=letter)
     styles = getSampleStyleSheet()
-    
-    # Decode the base64 logo image
-    logo_data = base64.b64decode(logo_b64)
-    logo_image = Image.open(io.BytesIO(logo_data))
-    
-    # Resize the logo to fit the page width (you can adjust size if necessary)
-    logo_width, logo_height = logo_image.size
-    logo_aspect = logo_height / logo_width
-    max_logo_width = 150  # Adjust as needed
-    logo_width = min(logo_width, max_logo_width)
-    logo_height = int(logo_width * logo_aspect)
-    
-    # Create a ReportLab Image element to add the logo to the PDF
-    logo = ReportLabImage(io.BytesIO(logo_data), width=logo_width, height=logo_height)
-    
-    # Build the PDF content
-    story = [
-        logo,  # Add the logo at the top of the page
-        Spacer(1, 12),  # Space after the logo
-        Paragraph("<b>Nutrition Analysis Report</b>", styles['Title']),
-        Spacer(1, 12),
-        Paragraph(report_text.replace('\n', '<br/>'), styles['BodyText'])
-    ]
-    
+    story = [Paragraph("<b>Nutrition Analysis Report</b>", styles['Title']), Spacer(1, 12),
+             Paragraph(report_text.replace('\n', '<br/>'), styles['BodyText'])]
     doc.build(story)
     buffer.seek(0)
     return buffer
@@ -89,56 +72,53 @@ def generate_analysis(uploaded_file, client):
     """Generate AI-powered food analysis"""
     base64_image, img_format = process_image(uploaded_file)
     if not base64_image:
-        st.error("Image processing failed")
         return None
     
     image_url = f"data:image/{img_format.lower()};base64,{base64_image}"
     
     try:
-        st.write("Calling the API...")  # Debugging statement
-        
-        # Corrected the message format
         response = client.chat.completions.create(
             model="llama-3.2-11b-vision-preview",
             messages=[
                 {
-                    "role": "system",  # System message
-                    "content": """
+                    "role": "user",
+                    "content": [
+                        {"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.  
+                        Your task is to analyze the provided image, identify all visible food items, and estimate their calorie content as accurately as possible.  
+
                         **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.  
+                        - List each identified food item separately.  
+                        - Use known nutritional data to provide accurate calorie estimates.  
+                        - Consider portion size, cooking method, and density of food.  
+                        - Clearly specify if an item's calorie count is an estimate due to ambiguity.  
+                        - Provide the total estimated calorie count 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",  # User message
-                    "content": f"Analyze the following image of food: {image_url}"  # Include the image URL
+
+                        If the image is unclear or lacks enough details, state the limitations and provide a confidence percentage for the estimation.
+                        """},
+                        {"type": "image_url", "image_url": {"url": image_url}}
+                    ]
                 }
             ],
             temperature=0.2,
             max_tokens=400,
             top_p=0.5
         )
-        
-        st.write("API response received.")  # Debugging statement
-        analysis_result = response.choices[0].message.content
-        st.write("Analysis Result: ", analysis_result)  # Debugging statement
-        return analysis_result
+        return response.choices[0].message.content
     except Exception as e:
         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")
@@ -160,7 +140,7 @@ def display_main_interface():
         
         # Left column for the Download button
         with col1:
-            pdf_report = generate_pdf(st.session_state.analysis_result, logo_b64)
+            pdf_report = generate_pdf(st.session_state.analysis_result)
             st.download_button("📄 Download Nutrition Report", data=pdf_report, file_name="nutrition_report.pdf", mime="application/pdf")
         
         # Right column for the Clear button
@@ -185,14 +165,13 @@ def render_sidebar(client):
             if st.button("Analyze Meal 🍽️"):
                 with st.spinner("Analyzing image..."):
                     report = generate_analysis(uploaded_file, client)
-                    if report:
-                        st.session_state.analysis_result = report
-                        st.write("Analysis Result:", report)  # Debugging line to display result
-                        st.rerun()
-                    else:
-                        st.error("Analysis failed. Please try again.")
+                    st.session_state.analysis_result = report
+                    st.rerun()
 
+# ======================
 # APPLICATION ENTRYPOINT
+# ======================
+
 def main():
     """Primary application controller"""
     client = initialize_api_client()