Update app.py

app.py

@@ -6,7 +6,7 @@ import io
 from dotenv import load_dotenv
 from groq import Groq
 from reportlab.lib.pagesizes import letter
-from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
 from reportlab.lib.styles import getSampleStyleSheet
 
 # ======================
@@ -26,7 +26,7 @@ ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg']
 # ======================
 
 def initialize_api_client():
-    """Initialize Groq API client"""
+    """Initialize Groq API client."""
     load_dotenv()
     api_key = os.getenv("GROQ_API_KEY")
     if not api_key:
@@ -36,63 +36,72 @@ def initialize_api_client():
 
 
 def encode_image(image_path):
-    """Encode an image to base64"""
+    """Encode an image to base64."""
     try:
         with open(image_path, "rb") as img_file:
             return base64.b64encode(img_file.read()).decode("utf-8")
     except FileNotFoundError:
+        st.error(f"Logo file not found at {image_path}")
         return ""
 
 
 def process_image(uploaded_file):
-    """Convert image to base64 string"""
+    """Convert image to base64 string."""
     try:
         image = Image.open(uploaded_file)
         buffer = io.BytesIO()
-        image.save(buffer, format=image.format)
-        return base64.b64encode(buffer.getvalue()).decode('utf-8'), image.format
+        # Ensure image.format is valid; default to PNG if not available.
+        fmt = image.format if image.format else "PNG"
+        image.save(buffer, format=fmt)
+        return base64.b64encode(buffer.getvalue()).decode('utf-8'), fmt
     except Exception as e:
         st.error(f"Image processing error: {e}")
         return None, None
 
 
 def generate_pdf(report_text, logo_b64):
-    """Generate a PDF report with logo"""
+    """Generate a PDF report with logo."""
     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))
+    story = []
     
-    # 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 this value to change the max width of the logo
-    logo_width = min(logo_width, max_logo_width)
-    logo_height = int(logo_width * logo_aspect)
+    # If a logo is provided, decode and add it
+    if logo_b64:
+        try:
+            logo_data = base64.b64decode(logo_b64)
+            logo_image = Image.open(io.BytesIO(logo_data))
+            # Resize the logo (adjust max width as needed)
+            logo_width, logo_height = logo_image.size
+            logo_aspect = logo_height / logo_width
+            max_logo_width = 150
+            logo_width = min(logo_width, max_logo_width)
+            logo_height = int(logo_width * logo_aspect)
+            
+            logo = ReportLabImage(io.BytesIO(logo_data), width=logo_width, height=logo_height)
+            story.append(logo)
+            story.append(Spacer(1, 12))
+        except Exception as e:
+            st.error(f"Error adding logo to PDF: {e}")
     
-    # 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
+    story.extend([
         Paragraph("<b>Nutrition Analysis Report</b>", styles['Title']),
         Spacer(1, 12),
         Paragraph(report_text.replace('\n', '<br/>'), styles['BodyText'])
-    ]
+    ])
+    
+    try:
+        doc.build(story)
+    except Exception as e:
+        st.error(f"Error generating PDF: {e}")
     
-    doc.build(story)
     buffer.seek(0)
     return buffer
 
 
-
 def generate_analysis(uploaded_file, client):
-    """Generate AI-powered food analysis"""
+    """Generate AI-powered food analysis."""
     base64_image, img_format = process_image(uploaded_file)
     if not base64_image:
         return None
@@ -107,21 +116,20 @@ def generate_analysis(uploaded_file, client):
                     "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.  
-                        **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.
-                
+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": "image_url", "image_url": {"url": image_url}}
                     ]
@@ -140,11 +148,8 @@ def generate_analysis(uploaded_file, client):
 # UI COMPONENTS
 # ======================
 
-def display_main_interface():
-    """Render primary application interface"""
-    logo_b64 = encode_image("src/logo.png")
-    
-    # HTML with inline styles to change text colors
+def display_main_interface(logo_b64):
+    """Render primary application interface."""
     st.markdown(f"""
         <div style="text-align: center;">
             <img src="data:image/png;base64,{logo_b64}" width="100">
@@ -156,48 +161,62 @@ def display_main_interface():
     st.markdown("---")
     
     if st.session_state.get('analysis_result'):
-        # Create two columns: one for download and one for clear button
-        col1, col2 = st.columns([1, 1])
+        # Create two columns: one for download and one for the clear button
+        col1, col2 = st.columns(2)
         
         # Left column for the Download button
         with col1:
-            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")
+            pdf_report = generate_pdf(st.session_state.analysis_result, logo_b64)
+            st.download_button("📄 Download Nutrition Report",
+                               data=pdf_report,
+                               file_name="nutrition_report.pdf",
+                               mime="application/pdf")
         
         # Right column for the Clear button
         with col2:
             if st.button("Clear Analysis 🗑️"):
                 st.session_state.pop('analysis_result')
-                st.rerun()
+                st.experimental_rerun()
     
     if st.session_state.get('analysis_result'):
         st.markdown("### 🎯 Nutrition Analysis Report")
         st.info(st.session_state.analysis_result)
 
 
-def render_sidebar(client):
-    """Create sidebar UI elements"""
+def render_sidebar(client, logo_b64):
+    """Create sidebar UI elements."""
     with st.sidebar:
         st.subheader("Image Upload")
         uploaded_file = st.file_uploader("Upload Food Image", type=ALLOWED_FILE_TYPES)
         
         if uploaded_file:
-            st.image(Image.open(uploaded_file), caption="Uploaded Food Image")
+            try:
+                image = Image.open(uploaded_file)
+                st.image(image, caption="Uploaded Food Image")
+            except Exception as e:
+                st.error(f"Error displaying image: {e}")
+            
             if st.button("Analyze Meal 🍽️"):
                 with st.spinner("Analyzing image..."):
                     report = generate_analysis(uploaded_file, client)
-                    st.session_state.analysis_result = report
-                    st.rerun()
+                    if report:
+                        st.session_state.analysis_result = report
+                        st.experimental_rerun()
+                    else:
+                        st.error("Failed to generate analysis.")
+
 
 # ======================
 # APPLICATION ENTRYPOINT
 # ======================
 
 def main():
-    """Primary application controller"""
+    """Primary application controller."""
     client = initialize_api_client()
-    display_main_interface()
-    render_sidebar(client)
+    logo_b64 = encode_image("src/logo.png")
+    display_main_interface(logo_b64)
+    render_sidebar(client, logo_b64)
+
 
 if __name__ == "__main__":
     main()