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| import streamlit as st | |
| from PIL import Image | |
| import os | |
| import base64 | |
| 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.lib.styles import getSampleStyleSheet | |
| # ====================== | |
| # CONFIGURATION SETTINGS | |
| # ====================== | |
| st.set_page_config( | |
| page_title="Smart Diet Analyzer", | |
| page_icon="🍎", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| ALLOWED_FILE_TYPES = ['png', 'jpg', 'jpeg'] | |
| # ====================== | |
| # RERUN HELPER FUNCTION | |
| # ====================== | |
| def rerun(): | |
| """ | |
| Helper function to rerun the app. | |
| Tries to use st.experimental_rerun if available; otherwise, does nothing. | |
| """ | |
| if hasattr(st, "experimental_rerun"): | |
| st.experimental_rerun() | |
| else: | |
| # Fallback: you might consider raising an exception to force a rerun. | |
| # However, this is not recommended for production. Instead, you might simply | |
| # notify the user to manually refresh the page. | |
| st.warning("Please refresh the page to update the results.") | |
| # ====================== | |
| # UTILITY FUNCTIONS | |
| # ====================== | |
| def initialize_api_client(): | |
| """Initialize Groq API client.""" | |
| load_dotenv() | |
| api_key = os.getenv("GROQ_API_KEY") | |
| if not api_key: | |
| st.error("API key not found. Please verify .env configuration.") | |
| st.stop() | |
| return Groq(api_key=api_key) | |
| def encode_image(image_path): | |
| """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.""" | |
| try: | |
| image = Image.open(uploaded_file) | |
| buffer = io.BytesIO() | |
| 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.""" | |
| buffer = io.BytesIO() | |
| doc = SimpleDocTemplate(buffer, pagesize=letter) | |
| styles = getSampleStyleSheet() | |
| story = [] | |
| # 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)) | |
| 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}") | |
| 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}") | |
| buffer.seek(0) | |
| return buffer | |
| def generate_analysis(uploaded_file, client): | |
| """Generate AI-powered food analysis.""" | |
| base64_image, img_format = process_image(uploaded_file) | |
| if not base64_image: | |
| return None | |
| image_url = f"data:image/{img_format.lower()};base64,{base64_image}" | |
| try: | |
| response = client.chat.completions.create( | |
| model="llama-3.2-11b-vision-preview", | |
| messages=[ | |
| { | |
| "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. | |
| """}, | |
| {"type": "image_url", "image_url": {"url": image_url}} | |
| ] | |
| } | |
| ], | |
| temperature=0.2, | |
| max_tokens=400, | |
| top_p=0.5 | |
| ) | |
| 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(logo_b64): | |
| """Render primary application interface.""" | |
| st.markdown(f""" | |
| <div style="text-align: center;"> | |
| <img src="data:image/png;base64,{logo_b64}" width="100"> | |
| <h2 style="color: #4CAF50;">Smart Diet Analyzer</h2> | |
| <p style="color: #FF6347;">AI-Powered Food & Nutrition Analysis</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown("---") | |
| if st.session_state.get('analysis_result'): | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| 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") | |
| with col2: | |
| if st.button("Clear Analysis 🗑️"): | |
| st.session_state.pop('analysis_result') | |
| rerun() | |
| if st.session_state.get('analysis_result'): | |
| st.markdown("### 🎯 Nutrition Analysis Report") | |
| st.info(st.session_state.analysis_result) | |
| 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: | |
| 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) | |
| if report: | |
| st.session_state.analysis_result = report | |
| rerun() | |
| else: | |
| st.error("Failed to generate analysis.") | |
| # ====================== | |
| # APPLICATION ENTRYPOINT | |
| # ====================== | |
| def main(): | |
| """Primary application controller.""" | |
| client = initialize_api_client() | |
| logo_b64 = encode_image("src/logo.png") | |
| display_main_interface(logo_b64) | |
| render_sidebar(client, logo_b64) | |
| if __name__ == "__main__": | |
| main() | |