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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+src/logo.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,182 @@
+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()

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+groq
+Pillow
+python-dotenv
+reportlab