File size: 6,575 Bytes
af22188 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
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
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']
# ======================
# 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:
return ""
def process_image(uploaded_file):
"""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
except Exception as e:
st.error(f"Image processing error: {e}")
return None, None
def generate_pdf(report_text):
"""Generate a PDF report"""
buffer = io.BytesIO()
doc = SimpleDocTemplate(buffer, pagesize=letter)
styles = getSampleStyleSheet()
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
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 as accurately as possible.
**Instructions:**
- 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 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
)
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")
# HTML with inline styles to change text colors
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'):
# Create two columns: one for download and one for clear button
col1, col2 = st.columns([1, 1])
# 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")
# Right column for the Clear button
with col2:
if st.button("Clear Analysis 🗑️"):
st.session_state.pop('analysis_result')
st.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"""
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")
if st.button("Analyze Meal 🍽️"):
with st.spinner("Analyzing image..."):
report = generate_analysis(uploaded_file, client)
st.session_state.analysis_result = report
st.rerun()
# ======================
# APPLICATION ENTRYPOINT
# ======================
def main():
"""Primary application controller"""
client = initialize_api_client()
display_main_interface()
render_sidebar(client)
if __name__ == "__main__":
main()
|