feat: Fix PDF download, RAG memory, logo, and contact info

Author: pavanbadempet

backend/report.py

@@ -54,3 +54,66 @@ async def analyze_report(file: UploadFile = File(...)) -> Dict[str, Any]:
     except Exception as e:
         logger.error(f"Report Analysis Failed: {e}")
         raise HTTPException(status_code=500, detail="Failed to analyze report")
+        raise HTTPException(status_code=500, detail="Failed to analyze report")
+
+# --- PDF Download Endpoint ---
+from fastapi import Depends
+from fastapi.responses import Response
+from sqlalchemy.orm import Session
+from . import database, auth, models, pdf_service
+
+@router.get("/download/health-report")
+def download_health_report(
+    current_user: models.User = Depends(auth.get_current_user),
+    db: Session = Depends(database.get_db)
+):
+    """
+    Generate and download a comprehensive health report PDF for the authenticated user.
+    """
+    try:
+        # 1. Fetch latest health record (Simulating a general summary for now)
+        # In a real app, we'd aggregate multiple records.
+        # For now, let's grab the most recent prediction if any.
+        latest_record = db.query(models.Prediction).filter(
+            models.Prediction.user_id == current_user.id
+        ).order_by(models.Prediction.created_at.desc()).first()
+
+        report_data = {
+            "age": 30, # Default/Placeholder if profile empty
+            "gender": "Unknown"
+        }
+        
+        # Merge Profile Data
+        if current_user.about_me:
+            # Simplistic parsing or just dumping raw profile fields
+            report_data["about"] = current_user.about_me
+            
+        prediction_val = "General Health Summary"
+        advice_list = ["maintain a balanced diet", "regular exercise"]
+
+        if latest_record:
+            prediction_val = f"{latest_record.record_type.capitalize()} Analysis: {latest_record.prediction_result}"
+            # Extract clinical data from the record if stored in a structured way
+            # (Assuming models.Prediction has a 'input_data' JSON column or similar, 
+            # skipping complex parsing for this hotfix)
+            pass
+
+        # 2. Generate PDF using the existing service
+        pdf_bytes = pdf_service.generate_medical_report(
+            user_name=current_user.username,
+            report_type="Comprehensive Health Profile",
+            prediction=prediction_val,
+            data=report_data,
+            advice=advice_list
+        )
+
+        # 3. Return as downloadable file
+        return Response(
+            content=pdf_bytes, 
+            media_type="application/pdf",
+            headers={"Content-Disposition": f"attachment; filename=Health_Report_{current_user.username}.pdf"}
+        )
+
+    except Exception as e:
+        logger.error(f"PDF Generation Failed: {e}")
+        raise HTTPException(status_code=500, detail="Could not generate report")

frontend/components/sidebar.py

@@ -18,33 +18,28 @@ def render_sidebar():
 
     # --- SIDEBAR RENDER ---
     with st.sidebar:
-        # 1. Logo & Brand
-        # 1. Logo & Brand - Refactored for Flexbox Alignment
-        import base64
-        import os
+        # 1. Logo & Brand - Refactored for Flexbox Alignment (SVG Mode)
 
-        # Helper to load image as base64
-        def get_img_base64(path):
-            try:
-                if os.path.exists(path):
-                    with open(path, "rb") as f:
-                        data = f.read()
-                        return base64.b64encode(data).decode()
-            except:
-                pass
-            return None
-
-        base_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-        logo_path = os.path.join(base_dir, "frontend", "static", "logo.png")
-        img_b64 = get_img_base64(logo_path)
-        
-        img_html = f'<img src="data:image/png;base64,{img_b64}" style="width: 48px; height: 48px; border-radius: 12px;">' if img_b64 else '<div style="font-size: 40px;">🏥</div>'
+        # Professional Vector Logo (Heart + Pulse)
+        # Using SVG ensures perfect scaling and 100% transparent background
+        svg_logo = """
+        <svg width="48" height="48" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+            <defs>
+                <linearGradient id="grad1" x1="0%" y1="0%" x2="100%" y2="100%">
+                    <stop offset="0%" style="stop-color:#60A5FA;stop-opacity:1" />
+                    <stop offset="100%" style="stop-color:#34D399;stop-opacity:1" />
+                </linearGradient>
+            </defs>
+            <path d="M12 21.35l-1.45-1.32C5.4 15.36 2 12.28 2 8.5 2 5.42 4.42 3 7.5 3c1.74 0 3.41.81 4.5 2.09C13.09 3.81 14.76 3 16.5 3 19.58 3 22 5.42 22 8.5c0 3.78-3.4 6.86-8.55 11.54L12 21.35z" fill="url(#grad1)" fill-opacity="0.2" stroke="url(#grad1)" stroke-width="2"/>
+            <path d="M6 12h2l2-4 2 8 2-4h2" stroke="url(#grad1)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+        </svg>
+        """
 
         st.markdown(f"""
-        <div style="display: flex; align-items: center; gap: 15px; padding: 10px 0 20px 0;">
-            {img_html}
+        <div style="display: flex; align-items: center; gap: 12px; padding: 15px 0 25px 0;">
+            {svg_logo}
             <div style="line-height: 1.2;">
-                <div style="font-size: 20px; font-weight: 700; color: #F8FAFC; letter-spacing: -0.5px;">AI Healthcare</div>
+                <div style="font-size: 22px; font-weight: 700; color: #F8FAFC; letter-spacing: -0.5px;">AI Healthcare</div>
                 <div style="font-size: 13px; color: #94A3B8; font-weight: 400;">Patient Portal</div>
             </div>
         </div>

frontend/utils/api.py

@@ -140,6 +140,16 @@ def update_profile(data: Dict[str, Any]) -> bool:
         st.error(f"Error: {e}")
     return False
 
+def fetch_health_report() -> Optional[bytes]:
+    """Fetch the PDF health report with auth headers."""
+    if 'token' not in st.session_state: return None
+    try:
+        resp = requests.get(f"{BACKEND_URL}/download/health-report", headers=_headers())
+        if resp.status_code == 200:
+            return resp.content
+    except Exception as e:
+        print(f"Error fetching report: {e}")
+    return None
 
 # --- Records ---
 

frontend/views/about_view.py

@@ -44,7 +44,7 @@ def render_about_page():
         
         ---
         
-        **Support:** help@patientconnect.ai  
+        **Support:** pavan9b@gmail.com  
         
         **License:** Patient Personal Use License  
         **Privacy:** HIPAA Compliant Data Vault
@@ -157,7 +157,7 @@ def render_about_page():
         
         ### 7. Contact Us
         
-        For privacy concerns: privacy@aihealthcare.example.com
+        For privacy concerns: pavan9b@gmail.com
         
         ---
         

frontend/views/profile_view.py

@@ -64,30 +64,27 @@ def render_profile_page():
     </p>
     """, unsafe_allow_html=True)
 
-    # Download button with link to backend endpoint
-    backend_url = api.get_backend_url()
-    token = st.session_state.get('token', '')
+    # Authenticated Download Button
+    c1, c2 = st.columns([1, 2])
 
-    col1, col2 = st.columns([1, 3])
-    with col1:
-        st.markdown(f"""
-        <a href="{backend_url}/download/health-report" target="_blank" style="text-decoration: none;">
-            <button style="
-                background: linear-gradient(135deg, #3B82F6, #8B5CF6);
-                color: white;
-                border: none;
-                padding: 10px 20px;
-                border-radius: 8px;
-                font-weight: 600;
-                cursor: pointer;
-                box-shadow: 0 4px 6px rgba(0,0,0,0.1);
-            ">
-                📥 Download PDF Report
-            </button>
-        </a>
-        """, unsafe_allow_html=True)
-    
-    with col2:
+    with c1:
+        # We use a callback pattern or direct fetch if it's fast enough.
+        # Since generating PDF might take a second, we'll make it direct for now.
+        report_data = api.fetch_health_report()
+        
+        if report_data:
+            st.download_button(
+                label="📥 Download PDF Report",
+                data=report_data,
+                file_name=f"Health_Report_{username}.pdf",
+                mime="application/pdf",
+                key="dl_pdf_btn",
+                use_container_width=True,
+            )
+        else:
+            st.warning("Report unavailable (check connection)")
+            
+    with c2:
         st.info("💡 **Tip:** The PDF includes your profile, recent health assessments, and personalized recommendations.")
 
     # --- Personalized Health Tips ---