Small bug fixes and enabling flie uploads

.streamlit/config.toml

@@ -0,0 +1,17 @@
+[server]
+# Enable file uploads
+enableXsrfProtection = false
+enableCORS = false
+maxUploadSize = 200
+
+[browser]
+# Gather usage stats
+gatherUsageStats = false
+
+[theme]
+# Orange theme to match the app
+primaryColor = "#e67e22"
+backgroundColor = "#ffffff"
+secondaryBackgroundColor = "#f8f9fa"
+textColor = "#262730"
+

Dockerfile

@@ -14,4 +14,4 @@ COPY . ./
 EXPOSE 8501
 
 # Run Streamlit app
-CMD ["streamlit", "run", "app.py", "--server.address=0.0.0.0", "--server.port=8501"] 
+CMD ["streamlit", "run", "Z-Score_Calculator.py", "--server.address=0.0.0.0", "--server.port=8501"] 

batch_utils.py

@@ -31,6 +31,27 @@ BIOMARKER_LABELS = {
     "pwv": "Pulse Wave Velocity",
 }
 
+# Biomarkers where HIGHER values are BETTER (more is good)
+# These get green for high z-scores, concerning colors for low
+HIGHER_IS_BETTER = {
+    "nb_steps",
+    "max_steps",
+    "mean_active_time",
+    "sleep_duration",
+    "nb_moderate_active_minutes",
+    "nb_vigorous_active_minutes",
+}
+
+# Biomarkers where LOWER values are BETTER (less is good)
+# These get green for low z-scores, concerning colors for high
+LOWER_IS_BETTER = {
+    "sbp",
+    "dbp",
+    "pwv",
+    "avg_night_hr",
+    "weight",
+}
+
 # Biomarkers available for batch processing (excluding disabled ones)
 AVAILABLE_BIOMARKERS = [
     "nb_steps",
@@ -100,18 +121,34 @@ def process_batch_data(df: pd.DataFrame, normative_df: pd.DataFrame,
                     result[f'{biomarker}_z'] = round(res['z_score'], 2)
                     result[f'{biomarker}_percentile'] = round(res['percentile'], 1)
                     
-                    # Add interpretation
+                    # Context-aware interpretation
                     z = res['z_score']
-                    if z < -2:
-                        result[f'{biomarker}_interpretation'] = 'Very Low'
-                    elif z < -1:
-                        result[f'{biomarker}_interpretation'] = 'Below Average'
-                    elif z < 1:
-                        result[f'{biomarker}_interpretation'] = 'Average'
-                    elif z < 2:
-                        result[f'{biomarker}_interpretation'] = 'Above Average'
+                    higher_is_better = biomarker in HIGHER_IS_BETTER
+                    
+                    if higher_is_better:
+                        # For steps, sleep, activity: high is good
+                        if z < -2:
+                            result[f'{biomarker}_interpretation'] = 'Very Low ⚠️'
+                        elif z < -1:
+                            result[f'{biomarker}_interpretation'] = 'Below Average'
+                        elif z < 1:
+                            result[f'{biomarker}_interpretation'] = 'Average'
+                        elif z < 2:
+                            result[f'{biomarker}_interpretation'] = 'Above Average ✓'
+                        else:
+                            result[f'{biomarker}_interpretation'] = 'Excellent ✓✓'
                     else:
-                        result[f'{biomarker}_interpretation'] = 'Very High'
+                        # For HR, BP, PWV: low is good
+                        if z < -2:
+                            result[f'{biomarker}_interpretation'] = 'Very Low ✓✓'
+                        elif z < -1:
+                            result[f'{biomarker}_interpretation'] = 'Below Average ✓'
+                        elif z < 1:
+                            result[f'{biomarker}_interpretation'] = 'Average'
+                        elif z < 2:
+                            result[f'{biomarker}_interpretation'] = 'Above Average'
+                        else:
+                            result[f'{biomarker}_interpretation'] = 'Elevated ⚠️'
                         
                 except Exception as e:
                     result[f'{biomarker}_z'] = 'N/A'
@@ -127,8 +164,9 @@ def process_batch_data(df: pd.DataFrame, normative_df: pd.DataFrame,
     return pd.DataFrame(results)
 
 
-def create_z_score_gauge(z_score: float, label: str, width: float = 350, height: float = 100) -> Drawing:
-    """Create a horizontal gauge showing z-score position with orange theme."""
+def create_z_score_gauge(z_score: float, label: str, biomarker: str = None, 
+                         width: float = 350, height: float = 100) -> Drawing:
+    """Create a horizontal gauge showing z-score position with context-aware coloring."""
     d = Drawing(width, height)
     
     gauge_y = 35
@@ -136,15 +174,29 @@ def create_z_score_gauge(z_score: float, label: str, width: float = 350, height:
     gauge_left = 50
     gauge_width = width - 100
     
-    # Color zones - orange themed
-    zone_colors = [
-        (colors.HexColor('#2ecc71'), -2),   # Green - very low (good for some metrics)
-        (colors.HexColor('#27ae60'), -1),   # Darker green
-        (colors.HexColor('#f39c12'), 0),    # Orange - average
-        (colors.HexColor('#e67e22'), 1),    # Darker orange
-        (colors.HexColor('#d35400'), 2),    # Deep orange
-        (colors.HexColor('#c0392b'), 3),    # Red - extreme
-    ]
+    # Determine if higher is better for this biomarker
+    higher_is_better = biomarker in HIGHER_IS_BETTER if biomarker else False
+    
+    if higher_is_better:
+        # For steps, sleep, activity: LOW is bad (red), HIGH is good (green)
+        zone_colors = [
+            (colors.HexColor('#c0392b'), -3),   # Red - very low (bad)
+            (colors.HexColor('#e74c3c'), -2),   # Lighter red
+            (colors.HexColor('#f39c12'), -1),   # Orange - below average
+            (colors.HexColor('#f1c40f'), 0),    # Yellow - average
+            (colors.HexColor('#2ecc71'), 1),    # Light green - above average
+            (colors.HexColor('#27ae60'), 2),    # Green - high (good)
+        ]
+    else:
+        # For BP, HR, PWV: HIGH is bad (red), LOW is good (green)
+        zone_colors = [
+            (colors.HexColor('#27ae60'), -3),   # Green - very low (good)
+            (colors.HexColor('#2ecc71'), -2),   # Light green
+            (colors.HexColor('#f1c40f'), -1),   # Yellow - average
+            (colors.HexColor('#f39c12'), 0),    # Orange
+            (colors.HexColor('#e74c3c'), 1),    # Lighter red - elevated
+            (colors.HexColor('#c0392b'), 2),    # Red - high (bad)
+        ]
     
     zone_width = gauge_width / 6
     for i, (color, _) in enumerate(zone_colors):
@@ -282,17 +334,33 @@ def generate_pdf_report(patient_info: dict, measurements: dict, z_scores: dict =
                 value = measurements.get(biomarker, 'N/A')
                 label = BIOMARKER_LABELS.get(biomarker, biomarker.replace('_', ' ').title())
                 
-                # Interpretation
-                if z < -2:
-                    interp = "Very Low"
-                elif z < -1:
-                    interp = "Below Average"
-                elif z < 1:
-                    interp = "Average"
-                elif z < 2:
-                    interp = "Above Average"
+                # Context-aware interpretation
+                higher_is_better = biomarker in HIGHER_IS_BETTER
+                
+                if higher_is_better:
+                    # For steps, sleep, activity: high is good
+                    if z < -2:
+                        interp = "Very Low ⚠️"
+                    elif z < -1:
+                        interp = "Below Average"
+                    elif z < 1:
+                        interp = "Average"
+                    elif z < 2:
+                        interp = "Above Average ✓"
+                    else:
+                        interp = "Excellent ✓✓"
                 else:
-                    interp = "Very High"
+                    # For HR, BP, PWV: low is good
+                    if z < -2:
+                        interp = "Very Low ✓✓"
+                    elif z < -1:
+                        interp = "Below Average ✓"
+                    elif z < 1:
+                        interp = "Average"
+                    elif z < 2:
+                        interp = "Above Average"
+                    else:
+                        interp = "Elevated ⚠️"
                 
                 z_data.append([label, str(value), f"{z:.2f}", f"{pct:.1f}%", interp])
         
@@ -312,11 +380,11 @@ def generate_pdf_report(patient_info: dict, measurements: dict, z_scores: dict =
             elements.append(z_table)
             elements.append(Spacer(1, 0.15*inch))
             
-            # Add Z-score gauges
+            # Add Z-score gauges with context-aware coloring
             for biomarker, data in z_scores.items():
                 if isinstance(data, dict) and 'z_score' in data:
                     label = BIOMARKER_LABELS.get(biomarker, biomarker.replace('_', ' ').title())
-                    gauge = create_z_score_gauge(data['z_score'], label)
+                    gauge = create_z_score_gauge(data['z_score'], label, biomarker=biomarker)
                     elements.append(gauge)
                     elements.append(Spacer(1, 0.1*inch))
         

pages/2_PDF_Report.py

@@ -9,7 +9,7 @@ import os
 
 # Add parent directory to path for imports
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from batch_utils import generate_pdf_report, BIOMARKER_LABELS, AVAILABLE_BIOMARKERS
+from batch_utils import generate_pdf_report, BIOMARKER_LABELS, AVAILABLE_BIOMARKERS, HIGHER_IS_BETTER
 import normalizer_model
 
 st.set_page_config(
@@ -218,17 +218,33 @@ if st.button("📄 Generate PDF Report", type="primary"):
                             pct = data['percentile']
                             value = measurements[biomarker]
                             
-                            # Determine interpretation
-                            if z < -2:
-                                interp = "Very Low"
-                            elif z < -1:
-                                interp = "Below Average"
-                            elif z < 1:
-                                interp = "Average"
-                            elif z < 2:
-                                interp = "Above Average"
+                            # Context-aware interpretation
+                            higher_is_better = biomarker in HIGHER_IS_BETTER
+                            
+                            if higher_is_better:
+                                # For steps, sleep, activity: high is good
+                                if z < -2:
+                                    interp = "Very Low ⚠️"
+                                elif z < -1:
+                                    interp = "Below Average"
+                                elif z < 1:
+                                    interp = "Average"
+                                elif z < 2:
+                                    interp = "Above Average ✓"
+                                else:
+                                    interp = "Excellent ✓✓"
                             else:
-                                interp = "Very High"
+                                # For HR: low is good
+                                if z < -2:
+                                    interp = "Very Low ✓✓"
+                                elif z < -1:
+                                    interp = "Below Average ✓"
+                                elif z < 1:
+                                    interp = "Average"
+                                elif z < 2:
+                                    interp = "Above Average"
+                                else:
+                                    interp = "Elevated ⚠️"
                             
                             st.metric(
                                 label,