feat: Enhance medical report generation with new features and improved data handling

- Added body fat percentage input and optional muscle oxygenation CSV upload in the upload form.
- Implemented TSI chart generation based on muscle oxygenation data.
- Updated report generation to include metabolism and fuel source charts.
- Refactored context generation to eliminate reliance on SECA data, using patient info directly instead.
- Improved error handling and logging for graph generation processes.
- Enhanced HTML templates for better user experience and functionality.

app/main.py

@@ -13,10 +13,13 @@ from pathlib import Path
 
 from fastapi import FastAPI, File, Form, HTTPException, Request, UploadFile
 from fastapi.responses import FileResponse, HTMLResponse, RedirectResponse
+from fastapi.staticfiles import StaticFiles
 from jinja2 import Environment, FileSystemLoader
 from pydantic import BaseModel
-from starlette.middleware.sessions import SessionMiddleware
 from services.report_generator import ReportGeneratorService
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.middleware.sessions import SessionMiddleware
+from starlette.requests import Request as StarletteRequest
 
 app = FastAPI(
     title="Medical Report Generation API",
@@ -25,7 +28,32 @@ app = FastAPI(
 )
 
 # Add session middleware
-app.add_middleware(SessionMiddleware, secret_key=os.getenv("SECRET_KEY", "your-secret-key-change-in-production"))
+app.add_middleware(
+    SessionMiddleware,
+    secret_key=os.getenv("SECRET_KEY", "your-secret-key-change-in-production"),
+)
+
+
+# Add security headers middleware to allow external scripts
+class SecurityHeadersMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: StarletteRequest, call_next):
+        response = await call_next(request)
+        # Allow external scripts and styles (for Tailwind CDN)
+        # Only add CSP for HTML responses
+        content_type = response.headers.get("content-type", "").lower()
+        if "text/html" in content_type:
+            response.headers["Content-Security-Policy"] = (
+                "default-src 'self'; script-src 'self' 'unsafe-inline' https://cdn.tailwindcss.com https:; style-src 'self' 'unsafe-inline' https://cdn.tailwindcss.com https:; img-src 'self' data: https:;"
+            )
+        return response
+
+
+app.add_middleware(SecurityHeadersMiddleware)
+
+# Mount static files (if static directory exists)
+static_dir = Path("static")
+if static_dir.exists():
+    app.mount("/static", StaticFiles(directory="static"), name="static")
 
 # Setup templates
 jinja_env = Environment(loader=FileSystemLoader("app/templates"))
@@ -59,13 +87,15 @@ def render_template(template_name: str, context: dict) -> HTMLResponse:
     """Helper function to render Jinja2 templates"""
     template = jinja_env.get_template(template_name)
     html_content = template.render(**context)
-    return HTMLResponse(content=html_content)
+    return HTMLResponse(content=html_content, media_type="text/html")
 
 
 @app.get("/", response_class=HTMLResponse)
 async def root(request: Request):
     """Root endpoint - Upload form page"""
-    return render_template("upload.html", {"request": request, "session": request.session})
+    return render_template(
+        "upload.html", {"request": request, "session": request.session}
+    )
 
 
 @app.post("/upload")
@@ -77,56 +107,73 @@ async def upload_files(
     height: str = Form(...),
     weight: str = Form(...),
     gender: str = Form(...),
+    fat_percentage: float = Form(...),
     focus: str = Form(default="Endurance"),
     session_id: str = Form(default="default"),
     spirometry_pdf: UploadFile = File(...),
     pnoe_csv: UploadFile = File(...),
-    seca_excel: UploadFile = File(...),
+    oxygenation_csv: UploadFile = File(None),
 ):
     """Handle file upload and generate report"""
     # Validate file types
     if not spirometry_pdf.filename.endswith(".pdf"):
-        return render_template("upload.html", {
-            "request": request,
-            "session": request.session,
-            "error": "Spirometry file must be a PDF"
-        })
-    
+        return render_template(
+            "upload.html",
+            {
+                "request": request,
+                "session": request.session,
+                "error": "Spirometry file must be a PDF",
+            },
+        )
+
     if not pnoe_csv.filename.endswith(".csv"):
-        return render_template("upload.html", {
-            "request": request,
-            "session": request.session,
-            "error": "Pnoe file must be a CSV"
-        })
-    
-    if not seca_excel.filename.endswith((".xlsx", ".xls")):
-        return render_template("upload.html", {
-            "request": request,
-            "session": request.session,
-            "error": "SECA file must be an Excel file (.xlsx or .xls)"
-        })
-    
+        return render_template(
+            "upload.html",
+            {
+                "request": request,
+                "session": request.session,
+                "error": "Pnoe file must be a CSV",
+            },
+        )
+
+    # Validate oxygenation CSV if provided
+    if oxygenation_csv and oxygenation_csv.filename:
+        if not oxygenation_csv.filename.endswith(".csv"):
+            return render_template(
+                "upload.html",
+                {
+                    "request": request,
+                    "session": request.session,
+                    "error": "Oxygenation file must be a CSV",
+                },
+            )
+
     # Create session-specific temp directory
     session_uuid = str(uuid.uuid4())
     session_temp_dir = TEMP_DIR / session_uuid
     session_temp_dir.mkdir(exist_ok=True, parents=True)
-    
+
     # Save uploaded files
     spirometry_path = session_temp_dir / f"spirometry_{spirometry_pdf.filename}"
     pnoe_path = session_temp_dir / f"pnoe_{pnoe_csv.filename}"
-    seca_path = session_temp_dir / f"seca_{seca_excel.filename}"
-    
+    oxygenation_path = None
+
     try:
         # Write files
         with open(spirometry_path, "wb") as f:
             shutil.copyfileobj(spirometry_pdf.file, f)
-        
+
         with open(pnoe_path, "wb") as f:
             shutil.copyfileobj(pnoe_csv.file, f)
-        
-        with open(seca_path, "wb") as f:
-            shutil.copyfileobj(seca_excel.file, f)
-        
+
+        # Save oxygenation CSV if provided
+        if oxygenation_csv and oxygenation_csv.filename:
+            oxygenation_path = (
+                session_temp_dir / f"oxygenation_{oxygenation_csv.filename}"
+            )
+            with open(oxygenation_path, "wb") as f:
+                shutil.copyfileobj(oxygenation_csv.file, f)
+
         # Prepare patient information
         patient_name = f"{first_name} {last_name}"
         patient_info = {
@@ -137,76 +184,100 @@ async def upload_files(
             "height": height,
             "weight": weight,
             "gender": gender,
+            "fat_percentage": fat_percentage,
             "focus": focus,
             "session_id": session_id,
         }
-        
+
         # Generate report
+        oxygenation_csv_path = str(oxygenation_path) if oxygenation_path else None
         result = await report_service.generate_report(
             spirometry_pdf_path=str(spirometry_path),
             pnoe_csv_path=str(pnoe_path),
-            seca_excel_path=str(seca_path),
             patient_info=patient_info,
+            oxygenation_csv_path=oxygenation_csv_path,
         )
-        
+
         # Store in session
         request.session["patient_info"] = patient_info
         request.session["temp_dir"] = str(session_temp_dir)
         request.session["report_path"] = result["report_path"]
         request.session["graphs_generated"] = result["graphs_generated"]
         request.session["analysis_data"] = result["analysis_data"]
-        
+
         # Extract spirometry CSV path (it's saved in data_dir by the service)
-        from services.spirometry_table_extractor import extract_spirometry_table_from_pdf
-        from services.context_generator import ContextGenerator
         from pathlib import Path as PathLib
-        
+
+        from services.context_generator import ContextGenerator
+        from services.spirometry_table_extractor import (
+            extract_spirometry_table_from_pdf,
+        )
+
         # The spirometry CSV is extracted during report generation
         # We need to find it or extract it again
         data_dir = PathLib("data")
-        spirometry_csv_path = data_dir / f"spirometry_{Path(spirometry_pdf.filename).stem}.csv"
-        
+        spirometry_csv_path = (
+            data_dir / f"spirometry_{Path(spirometry_pdf.filename).stem}.csv"
+        )
+
         # If it doesn't exist, extract it
         if not spirometry_csv_path.exists():
             spirometry_csv_path = extract_spirometry_table_from_pdf(
                 str(spirometry_path), output_dir=str(data_dir)
             )
             spirometry_csv_path = PathLib(spirometry_csv_path)
-        
+
         # Get calculated metrics for display and editing
         context_gen = ContextGenerator()
         context_gen.load_data(
             str(pnoe_path),
             str(spirometry_csv_path),
-            str(seca_path)
+            None,  # No SECA file needed anymore
         )
-        context_gen.extract_patient_info(last_name)  # Extract patient info
+        # Set patient info manually since we're not reading from SECA
+        weight_kg = float(weight.replace("lbs", "").replace("kg", "").strip())
+        if "lbs" in weight.lower():
+            weight_kg = weight_kg / 2.20462  # Convert lbs to kg
+
+        context_gen.patient_info = {
+            "name": first_name,
+            "last_name": last_name,
+            "age": age,
+            "weight": weight_kg,
+            "fat_percentage": fat_percentage,
+            "gender": gender,
+        }
         spirometry_metrics = context_gen.calculate_spirometry_metrics()
         pnoe_metrics = context_gen.calculate_pnoe_metrics()
-        
+
         # Store metrics in session
         request.session["metrics"] = {
             "spirometry": spirometry_metrics,
             "pnoe": pnoe_metrics,
         }
         request.session["spirometry_csv_path"] = str(spirometry_csv_path)
-        
+
         return RedirectResponse(url="/preview", status_code=303)
-    
+
     except Exception as e:
         import traceback
+
         error_details = traceback.format_exc()
         print(f"ERROR: {error_details}")
-        return render_template("upload.html", {
-            "request": request,
-            "session": request.session,
-            "error": f"Error generating report: {str(e)}"
-        })
+        return render_template(
+            "upload.html",
+            {
+                "request": request,
+                "session": request.session,
+                "error": f"Error generating report: {str(e)}",
+            },
+        )
     finally:
         # Close file handles
         spirometry_pdf.file.close()
         pnoe_csv.file.close()
-        seca_excel.file.close()
+        if oxygenation_csv and oxygenation_csv.filename:
+            oxygenation_csv.file.close()
 
 
 @app.get("/preview", response_class=HTMLResponse)
@@ -214,7 +285,9 @@ async def preview(request: Request):
     """Preview generated report"""
     if not request.session.get("report_path"):
         return RedirectResponse(url="/", status_code=303)
-    return render_template("preview.html", {"request": request, "session": request.session})
+    return render_template(
+        "preview.html", {"request": request, "session": request.session}
+    )
 
 
 @app.get("/graphs/{filename}")
@@ -231,7 +304,9 @@ async def edit_form(request: Request):
     """Display edit metrics form"""
     if not request.session.get("metrics"):
         return RedirectResponse(url="/", status_code=303)
-    return render_template("edit.html", {"request": request, "session": request.session})
+    return render_template(
+        "edit.html", {"request": request, "session": request.session}
+    )
 
 
 @app.post("/edit")
@@ -239,16 +314,13 @@ async def edit_metrics(request: Request):
     """Handle metric edits and regenerate report"""
     if not request.session.get("temp_dir") or not request.session.get("patient_info"):
         return RedirectResponse(url="/", status_code=303)
-    
+
     # Get form data
     form_data = await request.form()
-    
+
     # Build metric overrides
-    metric_overrides = {
-        "pnoe": {},
-        "spirometry": {}
-    }
-    
+    metric_overrides = {"pnoe": {}, "spirometry": {}}
+
     # Pnoe overrides
     if form_data.get("vo2_max"):
         metric_overrides["pnoe"]["vo2_max"] = float(form_data["vo2_max"])
@@ -262,28 +334,36 @@ async def edit_metrics(request: Request):
         metric_overrides["pnoe"]["fat_max_value"] = float(form_data["fat_max_value"])
     if form_data.get("fat_max_hr"):
         metric_overrides["pnoe"]["fat_max_hr"] = float(form_data["fat_max_hr"])
-    
+
     # VT1 and VT2 overrides
-    if form_data.get("vt1_hr") or form_data.get("vt1_speed") or form_data.get("vt1_time"):
+    if (
+        form_data.get("vt1_hr")
+        or form_data.get("vt1_speed")
+        or form_data.get("vt1_time")
+    ):
         metric_overrides["pnoe"]["vt1"] = {
             "HeartRate": float(form_data.get("vt1_hr", 0)),
             "Speed": float(form_data.get("vt1_speed", 0)),
-            "Time": float(form_data.get("vt1_time", 0))
+            "Time": float(form_data.get("vt1_time", 0)),
         }
-    
-    if form_data.get("vt2_hr") or form_data.get("vt2_speed") or form_data.get("vt2_time"):
+
+    if (
+        form_data.get("vt2_hr")
+        or form_data.get("vt2_speed")
+        or form_data.get("vt2_time")
+    ):
         metric_overrides["pnoe"]["vt2"] = {
             "HeartRate": float(form_data.get("vt2_hr", 0)),
             "Speed": float(form_data.get("vt2_speed", 0)),
-            "Time": float(form_data.get("vt2_time", 0))
+            "Time": float(form_data.get("vt2_time", 0)),
         }
-    
+
     # Heart rate zones
     for i in range(1, 6):
         zone_key = f"zone{i}_bpm"
         if form_data.get(zone_key):
             metric_overrides["pnoe"][zone_key] = form_data[zone_key]
-    
+
     # Spirometry overrides
     if form_data.get("fvc_best"):
         metric_overrides["spirometry"]["fvc_best"] = float(form_data["fvc_best"])
@@ -294,88 +374,120 @@ async def edit_metrics(request: Request):
     if form_data.get("fev1_pred"):
         metric_overrides["spirometry"]["fev1_pred"] = float(form_data["fev1_pred"])
     if form_data.get("fev1_fvc_pct_best"):
-        metric_overrides["spirometry"]["fev1_fvc_pct_best"] = float(form_data["fev1_fvc_pct_best"])
+        metric_overrides["spirometry"]["fev1_fvc_pct_best"] = float(
+            form_data["fev1_fvc_pct_best"]
+        )
     if form_data.get("fev1_fvc_pct_pred"):
-        metric_overrides["spirometry"]["fev1_fvc_pct_pred"] = float(form_data["fev1_fvc_pct_pred"])
-    
+        metric_overrides["spirometry"]["fev1_fvc_pct_pred"] = float(
+            form_data["fev1_fvc_pct_pred"]
+        )
+
     try:
         # Get file paths from session
         temp_dir = Path(request.session["temp_dir"])
         patient_info = request.session["patient_info"]
-        
+
         # Find files in temp directory
         spirometry_path = None
         pnoe_path = None
-        seca_path = None
-        
+        oxygenation_path = None
+
         for file_path in temp_dir.iterdir():
             if file_path.name.startswith("spirometry_"):
                 spirometry_path = file_path
             elif file_path.name.startswith("pnoe_"):
                 pnoe_path = file_path
-            elif file_path.name.startswith("seca_"):
-                seca_path = file_path
-        
-        if not all([spirometry_path, pnoe_path, seca_path]):
-            raise ValueError("Could not find all uploaded files")
-        
+            elif file_path.name.startswith("oxygenation_"):
+                oxygenation_path = file_path
+
+        if not all([spirometry_path, pnoe_path]):
+            raise ValueError("Could not find all required uploaded files")
+
         # Regenerate report with overrides
+        oxygenation_csv_path = str(oxygenation_path) if oxygenation_path else None
         result = await report_service.generate_report(
             spirometry_pdf_path=str(spirometry_path),
             pnoe_csv_path=str(pnoe_path),
-            seca_excel_path=str(seca_path),
             patient_info=patient_info,
-            metric_overrides=metric_overrides if (metric_overrides["pnoe"] or metric_overrides["spirometry"]) else None,
+            metric_overrides=metric_overrides
+            if (metric_overrides["pnoe"] or metric_overrides["spirometry"])
+            else None,
+            oxygenation_csv_path=oxygenation_csv_path,
         )
-        
+
         # Update session with new report
         request.session["report_path"] = result["report_path"]
         request.session["graphs_generated"] = result["graphs_generated"]
         request.session["analysis_data"] = result["analysis_data"]
-        
+
         # Recalculate metrics with overrides
         from services.context_generator import ContextGenerator
+
         context_gen = ContextGenerator()
         spirometry_csv_path = request.session.get("spirometry_csv_path", "")
         if not spirometry_csv_path or not Path(spirometry_csv_path).exists():
-            from services.spirometry_table_extractor import extract_spirometry_table_from_pdf
             from pathlib import Path as PathLib
+
+            from services.spirometry_table_extractor import (
+                extract_spirometry_table_from_pdf,
+            )
+
             data_dir = PathLib("data")
             spirometry_csv_path = extract_spirometry_table_from_pdf(
                 str(spirometry_path), output_dir=str(data_dir)
             )
             spirometry_csv_path = str(PathLib(spirometry_csv_path))
-        
+
         context_gen.load_data(
             str(pnoe_path),
             spirometry_csv_path,
-            str(seca_path)
+            None,  # No SECA file
         )
+        # Set patient info manually
+        weight_str = patient_info.get("weight", "0")
+        weight_kg = float(weight_str.replace("lbs", "").replace("kg", "").strip())
+        if "lbs" in weight_str.lower():
+            weight_kg = weight_kg / 2.20462  # Convert lbs to kg
+
+        context_gen.patient_info = {
+            "name": patient_info.get("first_name", ""),
+            "last_name": patient_info.get("last_name", ""),
+            "age": patient_info.get("age", 25),
+            "weight": weight_kg,
+            "fat_percentage": patient_info.get("fat_percentage", 0),
+            "gender": patient_info.get("gender", "female"),
+        }
         context_gen.extract_patient_info(patient_info.get("last_name", ""))
-        
+
         spirometry_overrides = metric_overrides.get("spirometry", {})
         pnoe_overrides = metric_overrides.get("pnoe", {})
-        spirometry_metrics = context_gen.calculate_spirometry_metrics(spirometry_overrides)
+        spirometry_metrics = context_gen.calculate_spirometry_metrics(
+            spirometry_overrides
+        )
         pnoe_metrics = context_gen.calculate_pnoe_metrics(pnoe_overrides)
-        
+
         # Update metrics in session
         request.session["metrics"] = {
             "spirometry": spirometry_metrics,
             "pnoe": pnoe_metrics,
         }
         request.session["spirometry_csv_path"] = spirometry_csv_path
-        
+
         return RedirectResponse(url="/preview", status_code=303)
-    
+
     except Exception as e:
         import traceback
+
         error_details = traceback.format_exc()
         print(f"ERROR: {error_details}")
-        return render_template("edit.html", {
-            "request": request,
-            "session": request.session,
-            "error": f"Error regenerating report: {str(e)}"
-        })
+        return render_template(
+            "edit.html",
+            {
+                "request": request,
+                "session": request.session,
+                "error": f"Error regenerating report: {str(e)}",
+            },
+        )
 
 
 @app.get("/health")

app/services/context_generator.py

@@ -6,7 +6,7 @@ of the medical report. It performs analysis on Pnoe, Spirometry, and SECA data.
 """
 
 from datetime import datetime
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, Optional, Tuple
 
 import pandas as pd
 
@@ -24,12 +24,15 @@ class ContextGenerator:
         self,
         pnoe_path: str,
         spirometry_path: str,
-        seca_path: str,
+        seca_path: Optional[str] = None,
     ):
         """Load all required datasets"""
         self.pnoe_df = pd.read_csv(pnoe_path, delimiter=";")
         self.spirometry_df = pd.read_csv(spirometry_path)
-        self.seca_df = pd.read_excel(seca_path)
+        if seca_path:
+            self.seca_df = pd.read_excel(seca_path)
+        else:
+            self.seca_df = None
         self._preprocess_pnoe_data()
 
     def _preprocess_pnoe_data(self):
@@ -75,7 +78,7 @@ class ContextGenerator:
                 )
 
     def extract_patient_info(self, patient_name: str) -> Dict:
-        """Extract patient information from SECA dataset"""
+        """Extract patient information from SECA dataset or use provided patient_info"""
         if self.seca_df is not None:
             patient_data = self.seca_df[
                 self.seca_df["LastName"].str.contains(
@@ -99,49 +102,73 @@ class ContextGenerator:
                     "fat_mass_lbs": weight_kg * fat_pct / 100 * 2.20462,
                     "lean_mass_lbs": weight_kg * (1 - fat_pct / 100) * 2.20462,
                 }
+        # If patient_info is already set (from manual input), calculate fat_mass and lean_mass
+        elif "weight" in self.patient_info and "fat_percentage" in self.patient_info:
+            weight_kg = self.patient_info["weight"]
+            fat_pct = self.patient_info["fat_percentage"]
+            self.patient_info["fat_mass_lbs"] = weight_kg * fat_pct / 100 * 2.20462
+            self.patient_info["lean_mass_lbs"] = (
+                weight_kg * (1 - fat_pct / 100) * 2.20462
+            )
         return self.patient_info
 
-    def calculate_spirometry_metrics(self, metric_overrides: Optional[Dict] = None) -> Dict:
+    def calculate_spirometry_metrics(
+        self, metric_overrides: Optional[Dict] = None
+    ) -> Dict:
         """Calculate spirometry-related metrics"""
         if metric_overrides is None:
             metric_overrides = {}
-        
+
         metrics = {}
         for param in ["FVC", "FEV1", "FEV1/FVC%"]:
             param_key = param.lower().replace("/", "_").replace("%", "_pct")
-            
+
             if f"{param_key}_best" in metric_overrides:
-                metrics[f"{param_key}_best"] = float(metric_overrides[f"{param_key}_best"])
+                metrics[f"{param_key}_best"] = float(
+                    metric_overrides[f"{param_key}_best"]
+                )
             else:
                 row = self.spirometry_df.loc[
                     self.spirometry_df["Parameters"].str.strip() == param
                 ]
                 if not row.empty:
-                    metrics[f"{param_key}_best"] = row["Best"].values[0]
-            
+                    value = row["Best"].values[0]
+                    if pd.notna(value):
+                        try:
+                            metrics[f"{param_key}_best"] = float(value)
+                        except (ValueError, TypeError):
+                            pass  # Skip if conversion fails
+
             if f"{param_key}_pred" in metric_overrides:
-                metrics[f"{param_key}_pred"] = float(metric_overrides[f"{param_key}_pred"])
+                metrics[f"{param_key}_pred"] = float(
+                    metric_overrides[f"{param_key}_pred"]
+                )
             else:
                 row = self.spirometry_df.loc[
                     self.spirometry_df["Parameters"].str.strip() == param
                 ]
                 if not row.empty:
-                    metrics[f"{param_key}_pred"] = row["%Pred."].values[0]
+                    value = row["%Pred."].values[0]
+                    if pd.notna(value):
+                        try:
+                            metrics[f"{param_key}_pred"] = float(value)
+                        except (ValueError, TypeError):
+                            pass  # Skip if conversion fails
         return metrics
 
     def calculate_pnoe_metrics(self, metric_overrides: Optional[Dict] = None) -> Dict:
         """Calculate all Pnoe-derived metrics"""
         if metric_overrides is None:
             metric_overrides = {}
-        
+
         metrics = {}
-        
+
         # VO2 Max metrics
         if "vo2_max" in metric_overrides:
             metrics["vo2_max"] = float(metric_overrides["vo2_max"])
         else:
             metrics["vo2_max"] = self.pnoe_df["VO2(ml/min)_smoothed"].max()
-        
+
         if "vo2_max_per_kg" in metric_overrides:
             metrics["vo2_max_per_kg"] = float(metric_overrides["vo2_max_per_kg"])
         else:
@@ -184,7 +211,7 @@ class ContextGenerator:
         else:
             vt1, _ = self._detect_thresholds()
             metrics["vt1"] = vt1
-        
+
         if "vt2" in metric_overrides:
             metrics["vt2"] = metric_overrides["vt2"]
         else:
@@ -200,9 +227,11 @@ class ContextGenerator:
         else:
             fat_max_idx = self.pnoe_df["FAT_smoothed"].idxmax()
             fat_max_row = self.pnoe_df.loc[fat_max_idx]
-            zones = self._calculate_hr_zones(metrics["vt1"], metrics["vt2"], fat_max_row)
+            zones = self._calculate_hr_zones(
+                metrics["vt1"], metrics["vt2"], fat_max_row
+            )
             metrics.update(zones)
-        
+
         return metrics
 
     def _detect_thresholds(self) -> Tuple[Optional[Dict], Optional[Dict]]:
@@ -261,95 +290,463 @@ class ContextGenerator:
             zones["zone5_bpm"] = f"{int(max_hr * 0.95)}+bpm"
         return zones
 
+    def _calculate_vo2_drop_points(self, pnoe_metrics: Dict) -> Dict:
+        """Calculate VO2 Pulse and VO2 Breath drop points"""
+        # Calculate slope of VO2 Pulse
+        vo2_pulse_slope = self.pnoe_df["VO2 Pulse_smoothed"].diff()
+        window = max(1, len(self.pnoe_df) // 3)  # Ensure window is at least 1
+        vo2_pulse_slope_smoothed = vo2_pulse_slope.rolling(window=window, min_periods=1).mean()
+        
+        # Find where VO2 Pulse begins to drop (slope becomes negative)
+        mask_pulse = vo2_pulse_slope_smoothed <= 0
+        drop_indices_pulse = mask_pulse[mask_pulse].index
+        
+        vo2_pulse_drop_bpm = None
+        vo2_pulse_drop_zone = None
+        if len(drop_indices_pulse) > 0:
+            drop_idx = drop_indices_pulse[0]
+            drop_row = self.pnoe_df.loc[drop_idx]
+            vo2_pulse_drop_bpm = int(drop_row["HR(bpm)_smoothed"])
+            # Determine zone based on HR zones
+            if pnoe_metrics.get("zone1_bpm") and vo2_pulse_drop_bpm:
+                zones = [pnoe_metrics.get(f"zone{i}_bpm", "") for i in range(1, 6)]
+                for i, zone_str in enumerate(zones, 1):
+                    if zone_str:
+                        zone_clean = zone_str.replace("bpm", "").strip()
+                        if "-" in zone_clean:
+                            parts = zone_clean.split("-")
+                            if len(parts) == 2:
+                                try:
+                                    start, end = int(parts[0]), int(parts[1].replace("+", ""))
+                                    if start <= vo2_pulse_drop_bpm <= end:
+                                        vo2_pulse_drop_zone = f"Zone {i}"
+                                        break
+                                except ValueError:
+                                    pass
+                        elif "+" in zone_clean:
+                            # Zone 5 format: "180+bpm"
+                            try:
+                                start = int(zone_clean.replace("+", ""))
+                                if vo2_pulse_drop_bpm >= start:
+                                    vo2_pulse_drop_zone = f"Zone {i}"
+                                    break
+                            except ValueError:
+                                pass
+        
+        # Calculate slope of VO2 Breath
+        vo2_breath_slope = self.pnoe_df["VO2 Breath_smoothed"].diff()
+        vo2_breath_slope_smoothed = vo2_breath_slope.rolling(window=window, min_periods=1).mean()
+        
+        # Find where VO2 Breath begins to drop
+        mask_breath = vo2_breath_slope_smoothed <= 0
+        drop_indices_breath = mask_breath[mask_breath].index
+        
+        vo2_breath_drop_bpm = None
+        vo2_breath_drop_zone = None
+        if len(drop_indices_breath) > 0:
+            drop_idx = drop_indices_breath[0]
+            drop_row = self.pnoe_df.loc[drop_idx]
+            vo2_breath_drop_bpm = int(drop_row["HR(bpm)_smoothed"])
+            # Determine zone
+            if pnoe_metrics.get("zone1_bpm") and vo2_breath_drop_bpm:
+                zones = [pnoe_metrics.get(f"zone{i}_bpm", "") for i in range(1, 6)]
+                for i, zone_str in enumerate(zones, 1):
+                    if zone_str:
+                        zone_clean = zone_str.replace("bpm", "").strip()
+                        if "-" in zone_clean:
+                            parts = zone_clean.split("-")
+                            if len(parts) == 2:
+                                try:
+                                    start, end = int(parts[0]), int(parts[1].replace("+", ""))
+                                    if start <= vo2_breath_drop_bpm <= end:
+                                        vo2_breath_drop_zone = f"Zone {i}"
+                                        break
+                                except ValueError:
+                                    pass
+                        elif "+" in zone_clean:
+                            # Zone 5 format: "180+bpm"
+                            try:
+                                start = int(zone_clean.replace("+", ""))
+                                if vo2_breath_drop_bpm >= start:
+                                    vo2_breath_drop_zone = f"Zone {i}"
+                                    break
+                            except ValueError:
+                                pass
+        
+        return {
+            "vo2_pulse_drop_bpm": vo2_pulse_drop_bpm or 180,
+            "vo2_pulse_drop_zone": vo2_pulse_drop_zone or "Zone 4",
+            "vo2_breath_drop_bpm": vo2_breath_drop_bpm or 173,
+            "vo2_breath_drop_zone": vo2_breath_drop_zone or "Zone 3",
+        }
+
+    def _calculate_fat_metabolism_metrics(self, pnoe_metrics: Dict) -> Dict:
+        """Calculate fat metabolism metrics for page 11"""
+        fat_max_idx = self.pnoe_df["FAT_smoothed"].idxmax()
+        fat_max_row = self.pnoe_df.loc[fat_max_idx]
+        
+        fat_max_value = pnoe_metrics.get("fat_max_value", 0)
+        fat_max_hr = pnoe_metrics.get("fat_max_hr", 0)
+        max_hr = 220 - self.patient_info["age"]
+        fat_max_heart_rate_pct = (fat_max_hr / max_hr * 100) if max_hr > 0 else 0
+        
+        # Find carbs and fat crossover point
+        crossover_idx = None
+        for idx in self.pnoe_df.index:
+            if self.pnoe_df.loc[idx, "CHO_smoothed"] > self.pnoe_df.loc[idx, "FAT_smoothed"]:
+                crossover_idx = idx
+                break
+        
+        crossover_bpm = None
+        crossover_heart_rate_pct = None
+        if crossover_idx is not None:
+            crossover_row = self.pnoe_df.loc[crossover_idx]
+            crossover_bpm = int(crossover_row["HR(bpm)_smoothed"])
+            crossover_heart_rate_pct = (crossover_bpm / max_hr * 100) if max_hr > 0 else 0
+        
+        # Get speed and incline at fat max
+        fat_max_speed = fat_max_row.get("Speed", 0)
+        fat_max_incline = fat_max_row.get("Incline", 2.0) if "Incline" in fat_max_row else 2.0
+        
+        return {
+            "fat_max_value": f"{fat_max_value:.2f}Kcals/min",
+            "fat_max_heart_rate": f"{fat_max_heart_rate_pct:.0f}% of Max Heart Rate",
+            "fat_max_bpm": f"{int(fat_max_hr)} bpm",
+            "fat_max_optimal": "*Optimal 10-12Kcals/minute",
+            "crossover_bpm": f"{crossover_bpm or 100}bpm",
+            "crossover_heart_rate": f"{crossover_heart_rate_pct or 51:.0f}% of Max Heart Rate",
+            "fat_metabolism_note": f"{crossover_bpm or 100}bpm at a speed of {fat_max_speed:.1f}mph and incline of {fat_max_incline:.0f}%",
+        }
+
+    def _calculate_recovery_metrics(self) -> Dict:
+        """Calculate recovery metrics for page 11"""
+        # Find peak exercise point (max HR)
+        peak_idx = self.pnoe_df["HR(bpm)_smoothed"].idxmax()
+        peak_hr = self.pnoe_df.loc[peak_idx, "HR(bpm)_smoothed"]
+        peak_time = self.pnoe_df.loc[peak_idx, "T(sec)"]
+        
+        # Find recovery phase (after peak)
+        recovery_df = self.pnoe_df[self.pnoe_df["T(sec)"] > peak_time].copy()
+        
+        if len(recovery_df) == 0:
+            return {
+                "cardiac_recovery_time": "(1 minute)",
+                "cardiac_recovery_percentage": "33%",
+                "metabolic_recovery_time": "(2 minute)",
+                "metabolic_recovery_percentage": "65%",
+                "breath_recovery_time": "(2.5 minute)",
+                "breath_recovery_percentage": "76%",
+            }
+        
+        # Cardiac recovery (1 minute)
+        one_min_time = peak_time + 60
+        one_min_row = recovery_df[recovery_df["T(sec)"] <= one_min_time]
+        if len(one_min_row) > 0:
+            one_min_hr = one_min_row.iloc[-1]["HR(bpm)_smoothed"]
+            cardiac_recovery_pct = ((peak_hr - one_min_hr) / peak_hr * 100) if peak_hr > 0 else 0
+        else:
+            cardiac_recovery_pct = 33
+        
+        # Metabolic recovery (2 minutes) - using VCO2
+        two_min_time = peak_time + 120
+        peak_vco2 = self.pnoe_df.loc[peak_idx, "VCO2(ml/min)_smoothed"]
+        two_min_row = recovery_df[recovery_df["T(sec)"] <= two_min_time]
+        if len(two_min_row) > 0:
+            two_min_vco2 = two_min_row.iloc[-1]["VCO2(ml/min)_smoothed"]
+            metabolic_recovery_pct = ((peak_vco2 - two_min_vco2) / peak_vco2 * 100) if peak_vco2 > 0 else 0
+        else:
+            metabolic_recovery_pct = 65
+        
+        # Breath frequency recovery (2.5 minutes)
+        two_five_min_time = peak_time + 150
+        peak_bf = self.pnoe_df.loc[peak_idx, "BF(bpm)_smoothed"]
+        two_five_min_row = recovery_df[recovery_df["T(sec)"] <= two_five_min_time]
+        if len(two_five_min_row) > 0:
+            two_five_min_bf = two_five_min_row.iloc[-1]["BF(bpm)_smoothed"]
+            breath_recovery_pct = ((peak_bf - two_five_min_bf) / peak_bf * 100) if peak_bf > 0 else 0
+        else:
+            breath_recovery_pct = 76
+        
+        return {
+            "cardiac_recovery_time": "(1 minute)",
+            "cardiac_recovery_percentage": f"{int(cardiac_recovery_pct)}%",
+            "metabolic_recovery_time": "(2 minute)",
+            "metabolic_recovery_percentage": f"{int(metabolic_recovery_pct)}%",
+            "breath_recovery_time": "(2.5 minute)",
+            "breath_recovery_percentage": f"{int(breath_recovery_pct)}%",
+        }
+
+    def _calculate_resting_heart_rate_metrics(self) -> Dict:
+        """Calculate resting heart rate metrics for page 11"""
+        # Get resting HR from beginning of test
+        rest_phase = self.pnoe_df.head(30)  # First 30 seconds
+        resting_hr = rest_phase["HR(bpm)_smoothed"].mean()
+        
+        age = self.patient_info.get("age", 30)
+        gender = self.patient_info.get("gender", "female").lower()
+        
+        # Determine age range
+        if 26 <= age <= 35:
+            age_range = "26-35"
+        elif 36 <= age <= 45:
+            age_range = "36-45"
+        elif 46 <= age <= 55:
+            age_range = "46-55"
+        else:
+            age_range = "26-35"  # Default
+        
+        # HR ranges based on gender and age (simplified)
+        if gender == "female":
+            hr_ranges = {
+                "poor": "82bpm +",
+                "below_avg": "75-81bpm",
+                "average": "71-74bpm",
+                "above_avg": "66-70bpm",
+                "good": "62-65bpm",
+                "excellent": "55-61bpm",
+                "athlete": "44-54bpm",
+            }
+        else:  # male
+            hr_ranges = {
+                "poor": "82bpm +",
+                "below_avg": "75-81bpm",
+                "average": "71-74bpm",
+                "above_avg": "66-70bpm",
+                "good": "62-65bpm",
+                "excellent": "55-61bpm",
+                "athlete": "44-54bpm",
+            }
+        
+        return {
+            "resting_heart_rate": f"{int(resting_hr)}bpm",
+            "hr_age_range": age_range,
+            "hr_poor": hr_ranges["poor"],
+            "hr_below_avg": hr_ranges["below_avg"],
+            "hr_average": hr_ranges["average"],
+            "hr_above_avg": hr_ranges["above_avg"],
+            "hr_good": hr_ranges["good"],
+            "hr_excellent": hr_ranges["excellent"],
+            "hr_athlete": hr_ranges["athlete"],
+        }
+
+    def calculate_rmr_and_fuel_source(self) -> Dict:
+        """Calculate RMR and fuel source from pnoe data"""
+        metrics = {}
+
+        # Calculate RMR from resting phase (MET <= 1.1)
+        if "MET" in self.pnoe_df.columns and "EE(kcal/day)" in self.pnoe_df.columns:
+            rest_phase = self.pnoe_df[self.pnoe_df["MET"] <= 1.1]
+            if not rest_phase.empty:
+                rmr = rest_phase["EE(kcal/day)"].mean()
+                metrics["rmr_kcal"] = float(rmr)
+            else:
+                # Fallback: use minimum EE(kcal/min) * 1440 (minutes per day)
+                if "EE(kcal/min)" in self.pnoe_df.columns:
+                    min_ee = self.pnoe_df["EE(kcal/min)"].min()
+                    metrics["rmr_kcal"] = float(min_ee * 1440)
+                else:
+                    metrics["rmr_kcal"] = 1500.0  # Default fallback
+        else:
+            # Fallback: estimate from weight (simplified)
+            weight_kg = self.patient_info.get("weight", 70)
+            gender = self.patient_info.get("gender", "female").lower()
+
+            # Simplified RMR estimation: 22 kcal/kg/day for men, 20 for women
+            if gender == "male":
+                rmr = weight_kg * 22
+            else:
+                rmr = weight_kg * 20
+            metrics["rmr_kcal"] = float(rmr)
+
+        # Calculate fuel source from resting phase (RER == 0.9 or closest)
+        if "RER" in self.pnoe_df.columns and "FAT(%)" in self.pnoe_df.columns:
+            # Find rest phase with RER closest to 0.9
+            rest_phase = (
+                self.pnoe_df[self.pnoe_df["MET"] <= 1.1].copy()
+                if "MET" in self.pnoe_df.columns
+                else self.pnoe_df.copy()
+            )
+            if not rest_phase.empty:
+                # Find row with RER closest to 0.9
+                if "RER" in rest_phase.columns:
+                    rest_phase["RER_diff"] = abs(rest_phase["RER"] - 0.9)
+                    closest_idx = rest_phase["RER_diff"].idxmin()
+                    fat_pct = rest_phase.loc[closest_idx, "FAT(%)"]
+                    metrics["rest_fat_percentage"] = float(fat_pct)
+                else:
+                    # Use mean FAT(%) from rest phase
+                    metrics["rest_fat_percentage"] = float(rest_phase["FAT(%)"].mean())
+            else:
+                # Fallback: use overall mean
+                metrics["rest_fat_percentage"] = float(self.pnoe_df["FAT(%)"].mean())
+        else:
+            # Fallback: use a default value
+            metrics["rest_fat_percentage"] = 75.0
+
+        # Calculate caloric values for page 5
+        rmr = metrics["rmr_kcal"]
+        neat = rmr * 0.25  # NEAT is typically 20-30% of RMR
+        weight_loss_rate = 1.0  # 1 lb per week
+        weight_loss_calories = 500.0  # 500 kcal deficit per day for 1 lb/week
+        total_calories = rmr + neat - weight_loss_calories
+
+        metrics["resting_calories"] = int(rmr)
+        metrics["neat_calories"] = int(neat)
+        metrics["weight_loss_calories"] = int(weight_loss_calories)
+        metrics["weight_loss_rate"] = weight_loss_rate
+        metrics["total_calories"] = int(total_calories)
+
+        return metrics
+
     def generate_all_contexts(
-        self, patient_name: str, graphs: Dict[str, str], metric_overrides: Optional[Dict] = None
-    ) -> List[Dict]:
-        """Main method to generate all page contexts"""
+        self,
+        patient_name: str,
+        graphs: Dict[str, str],
+        metric_overrides: Optional[Dict] = None,
+    ) -> Dict[str, Dict]:
+        """Main method to generate all page contexts
+
+        Returns:
+            Dictionary with keys 'page_1', 'page_2', etc., each containing context data for that page
+        """
         if metric_overrides is None:
             metric_overrides = {}
-        
+
         self.extract_patient_info(patient_name)
-        
+
         # Extract metric overrides for spirometry and pnoe
         spirometry_overrides = metric_overrides.get("spirometry", {})
         pnoe_overrides = metric_overrides.get("pnoe", {})
-        
+
         spirometry_metrics = self.calculate_spirometry_metrics(spirometry_overrides)
         pnoe_metrics = self.calculate_pnoe_metrics(pnoe_overrides)
+        rmr_metrics = self.calculate_rmr_and_fuel_source()
 
-        contexts = []
-        contexts.append(
-            {
-                "name": self.patient_info["name"],
-                "surname": self.patient_info["last_name"],
-                "date": datetime.now().strftime("%B %d, %Y"),
-            }
-        )
-        contexts.append(
-            {
+        contexts = {}
+
+        # Page 1
+        contexts["page_1"] = {
+            "name": self.patient_info["name"],
+            "surname": self.patient_info["last_name"],
+            "date": datetime.now().strftime("%B %d, %Y"),
+        }
+
+        # Page 2
+        contexts["page_2"] = {
+            "patient_name": self.patient_info["name"],
+            "test_date": datetime.now().strftime("%B %d, %Y"),
+        }
+
+        # Pages 3, 6 (pages 4 and 5 are handled separately)
+        for i in [0, 3]:  # Skip indices 1 and 2 which are pages 4 and 5
+            contexts[f"page_{i + 3}"] = {
                 "patient_name": self.patient_info["name"],
-                "test_date": datetime.now().strftime("%B %d, %Y"),
+                "page_number": i + 3,
             }
-        )
 
-        for i in range(4):
-            contexts.append(
-                {"patient_name": self.patient_info["name"], "page_number": i + 3}
-            )
+        # Page 4 - Nutrition Guidelines with Body Composition
+        contexts["page_4"] = {
+            "patient_name": self.patient_info["name"],
+            "page_number": 4,
+            "fat_percentage": f"{self.patient_info['fat_percentage']:.1f}",
+            "body_composition_chart": graphs.get("body_composition", ""),
+            "body_fat_chart": graphs.get("body_fat_percent", ""),  # Alias for template
+            "body_fat_percent_chart": graphs.get(
+                "body_fat_percent", ""
+            ),  # Keep for consistency
+        }
 
+        # Page 5 - Resting Metabolic Rate Assessment
+        contexts["page_5"] = {
+            "patient_name": self.patient_info["name"],
+            "page_number": 5,
+            "metabolism_chart": graphs.get("metabolism_chart", ""),
+            "fuel_source_chart": graphs.get("fuel_source_chart", ""),
+            "resting_calories": rmr_metrics.get("resting_calories", 1500),
+            "neat_calories": rmr_metrics.get("neat_calories", 375),
+            "weight_loss_calories": rmr_metrics.get("weight_loss_calories", 500),
+            "weight_loss_rate": rmr_metrics.get("weight_loss_rate", 1.0),
+            "total_calories": rmr_metrics.get("total_calories", 1375),
+        }
+
+        # Calculate FEV1 percentage for page 7
         fev1_percentage = 0
         if spirometry_metrics.get("fvc_best"):
             fev1_percentage = (
                 pnoe_metrics["peak_vt"] / spirometry_metrics["fvc_best"]
             ) * 100
 
-        contexts.append(
-            {
-                "peak_vt": f"{pnoe_metrics['peak_vt']:.2f}",
-                "peak_vt_bpm": f"{int(pnoe_metrics['peak_vt_hr'])}",
-                "fev1_percentage": f"{fev1_percentage:.1f}",
-                "lung_analysis_chart": graphs.get("spirometry_chart", ""),
-                "respiratory_analysis_chart": graphs.get("respiratory", ""),
-            }
-        )
-        contexts.append(
-            {
-                "vo2_max_value": f"{pnoe_metrics['vo2_max_per_kg']:.1f}",
-                "age_range": f"{self.patient_info['age'] // 10 * 10}-{self.patient_info['age'] // 10 * 10 + 9}",
-                "zone1_bpm": pnoe_metrics.get("zone1_bpm", ""),
-                "zone2_bpm": pnoe_metrics.get("zone2_bpm", ""),
-                "zone3_bpm": pnoe_metrics.get("zone3_bpm", ""),
-                "zone4_bpm": pnoe_metrics.get("zone4_bpm", ""),
-                "zone5_bpm": pnoe_metrics.get("zone5_bpm", ""),
-                "vo2_pulse_chart": graphs.get("vo2_pulse", ""),
-            }
-        )
-        contexts.append(
-            {
-                "fat_max_value": f"{pnoe_metrics['fat_max_value']:.2f}",
-                "fat_max_hr": f"{int(pnoe_metrics['fat_max_hr'])}",
-                "fuel_utilization_chart": graphs.get("fuel_utilization", ""),
-                "fat_metabolism_chart": graphs.get("fat_metabolism", ""),
-            }
-        )
-        contexts.append(
-            {
-                "fat_percentage": f"{self.patient_info['fat_percentage']:.1f}",
-                "fat_mass_lbs": f"{self.patient_info['fat_mass_lbs']:.1f}",
-                "lean_mass_lbs": f"{self.patient_info['lean_mass_lbs']:.1f}",
-                "body_composition_chart": graphs.get("body_composition", ""),
-                "body_fat_percent_chart": graphs.get("body_fat_percent", ""),
-            }
-        )
+        # Page 7
+        contexts["page_7"] = {
+            "peak_vt": f"{pnoe_metrics['peak_vt']:.2f}",
+            "peak_vt_bpm": f"{int(pnoe_metrics['peak_vt_hr'])}",
+            "fev1_percentage": f"{fev1_percentage:.1f}",
+            "lung_analysis_chart": graphs.get("spirometry_chart", ""),
+            "respiratory_analysis_chart": graphs.get("respiratory", ""),
+        }
 
-        for i in range(9):
-            contexts.append(
-                {
-                    "patient_name": self.patient_info["name"],
-                    "page_number": i + 11,
-                    "vo2_breath_chart": graphs.get("vo2_breath", ""),
-                    "recovery_chart": graphs.get("recovery", ""),
-                }
-            )
+        # Page 8
+        contexts["page_8"] = {
+            "vo2_max_value": f"{pnoe_metrics['vo2_max_per_kg']:.1f}",
+            "age_range": f"{self.patient_info['age'] // 10 * 10}-{self.patient_info['age'] // 10 * 10 + 9}",
+            "zone1_bpm": pnoe_metrics.get("zone1_bpm", ""),
+            "zone2_bpm": pnoe_metrics.get("zone2_bpm", ""),
+            "zone3_bpm": pnoe_metrics.get("zone3_bpm", ""),
+            "zone4_bpm": pnoe_metrics.get("zone4_bpm", ""),
+            "zone5_bpm": pnoe_metrics.get("zone5_bpm", ""),
+            "vo2_pulse_chart": graphs.get("vo2_pulse", ""),
+        }
+
+        # Page 9
+        contexts["page_9"] = {
+            "fat_max_value": f"{pnoe_metrics['fat_max_value']:.2f}",
+            "fat_max_hr": f"{int(pnoe_metrics['fat_max_hr'])}",
+            "fuel_utilization_chart": graphs.get("fuel_utilization", ""),
+            "fat_metabolism_chart": graphs.get("fat_metabolism", ""),
+        }
+
+        # Page 10 - VO2 Pulse and VO2 Breath
+        vo2_drop_metrics = self._calculate_vo2_drop_points(pnoe_metrics)
+        contexts["page_10"] = {
+            "vo2_pulse_chart": graphs.get("vo2_pulse", ""),
+            "vo2_breath_chart": graphs.get("vo2_breath", ""),
+            "vo2_pulse_drop_bpm": f"{vo2_drop_metrics['vo2_pulse_drop_bpm']} bpm",
+            "vo2_pulse_drop_zone": vo2_drop_metrics["vo2_pulse_drop_zone"],
+            "vo2_breath_drop_bpm": f"{vo2_drop_metrics['vo2_breath_drop_bpm']} bpm",
+            "vo2_breath_drop_zone": vo2_drop_metrics["vo2_breath_drop_zone"],
+        }
+
+        # Page 11 - Fat Metabolism and Recovery
+        fat_metabolism_metrics = self._calculate_fat_metabolism_metrics(pnoe_metrics)
+        recovery_metrics = self._calculate_recovery_metrics()
+        resting_hr_metrics = self._calculate_resting_heart_rate_metrics()
+        
+        contexts["page_11"] = {
+            "fat_metabolism_chart": graphs.get("fat_metabolism", ""),
+            "recovery_chart": graphs.get("recovery", ""),
+            **fat_metabolism_metrics,
+            **recovery_metrics,
+            **resting_hr_metrics,
+        }
+
+        # Pages 12-17
+        for i in range(6):
+            contexts[f"page_{i + 12}"] = {
+                "patient_name": self.patient_info["name"],
+                "page_number": i + 12,
+            }
+        
+        # Page 18 - Glossary with Body Fat Percentage Chart
+        contexts["page_18"] = {
+            "patient_name": self.patient_info["name"],
+            "page_number": 18,
+            "body_fat_percentage_chart": graphs.get("body_fat_percent", ""),
+        }
+        
+        # Page 19
+        contexts["page_19"] = {
+            "patient_name": self.patient_info["name"],
+            "page_number": 19,
+        }
 
         return contexts

app/services/graph_generator.py

@@ -584,6 +584,105 @@ class GraphGenerator:
 
         return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
 
+    def generate_tsi_chart(
+        self, oxygenation_df: pd.DataFrame, save_as_base64: bool = True
+    ) -> str:
+        """
+        Generate TSI (Tissue Saturation Index) chart with trend lines per stage.
+
+        Args:
+            oxygenation_df: DataFrame with Time, TSI, and TSI-second columns
+            save_as_base64: If True, return base64 string, else return file path
+
+        Returns:
+            Base64 string or file path
+        """
+        from numpy.polynomial.polynomial import Polynomial
+
+        plt.figure(figsize=(12, 5.5))
+
+        # Plot TSI (Left Leg)
+        plt.plot(
+            oxygenation_df["Time"],
+            oxygenation_df["TSI"],
+            label="TSI (Left Leg)",
+            color="steelblue",
+            linewidth=2,
+        )
+
+        # Plot TSI2 (Right Leg)
+        plt.plot(
+            oxygenation_df["Time"],
+            oxygenation_df["TSI-second"],
+            label="TSI2 (Right Leg)",
+            color="orange",
+            linewidth=2,
+        )
+
+        # Define time intervals for stages (adjust these based on your test protocol)
+        max_time = oxygenation_df["Time"].max()
+        intervals = [
+            (0, 250),
+            (250, 500),
+            (500, 750),
+            (750, 1000),
+            (1000, 1250),
+            (1250, 1500),
+            (1500, max_time),
+        ]
+
+        # Calculate and plot trend lines for each interval
+        for start_time, end_time in intervals:
+            # Filter data for this interval
+            mask_interval = (oxygenation_df["Time"] >= start_time) & (
+                oxygenation_df["Time"] <= end_time
+            )
+
+            # TSI (Left Leg) trend for this interval
+            mask_left = mask_interval & ~oxygenation_df["TSI"].isna()
+            if mask_left.sum() > 1:  # Need at least 2 points for a line
+                x_left = oxygenation_df.loc[mask_left, "Time"]
+                y_left = oxygenation_df.loc[mask_left, "TSI"]
+                coefs_left = Polynomial.fit(x_left, y_left, 1).convert().coef
+                trend_left = coefs_left[0] + coefs_left[1] * x_left
+                plt.plot(
+                    x_left,
+                    trend_left,
+                    color="black",
+                    linestyle="--",
+                    linewidth=2,
+                    alpha=0.8,
+                )
+
+            # TSI-second (Right Leg) trend for this interval
+            mask_right = mask_interval & ~oxygenation_df["TSI-second"].isna()
+            if mask_right.sum() > 1:  # Need at least 2 points for a line
+                x_right = oxygenation_df.loc[mask_right, "Time"]
+                y_right = oxygenation_df.loc[mask_right, "TSI-second"]
+                coefs_right = Polynomial.fit(x_right, y_right, 1).convert().coef
+                trend_right = coefs_right[0] + coefs_right[1] * x_right
+                plt.plot(
+                    x_right,
+                    trend_right,
+                    color="black",
+                    linestyle="--",
+                    linewidth=2,
+                    alpha=0.8,
+                )
+
+        plt.xlabel("Time (s)")
+        plt.ylabel("TSI (%)")
+        plt.title("TSI (Left) and TSI2 (Right) with Black Slope Lines per Stage")
+        plt.legend(fontsize=10, loc="upper right")
+        plt.grid(alpha=0.25)
+        plt.tight_layout()
+
+        chart_path = self.charts_dir / "tsi_chart.png"
+        plt.savefig(chart_path, bbox_inches="tight", dpi=160)
+        plt.close()
+
+        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
+
     def generate_body_composition_chart(
         self, fat_mass_lbs: float, lean_mass_lbs: float, save_as_base64: bool = True
     ) -> str:
@@ -678,25 +777,52 @@ class GraphGenerator:
         else:
             age_group = "20-39"  # Default
 
-        demographic = f"{age_group}\n({gender[0].upper()})"
+        gender_abbrev = "M" if gender.lower() == "male" else "F"
+        demographic = f"{age_group}\n({gender_abbrev})"
 
-        # Define segments based on gender (female example)
+        # Define segments based on gender and age group
         if gender.lower() == "female":
-            segments = [
-                ("#F8A8A8", 0, 15),  # Muted Red: 0% to 15%
-                ("#FFEECC", 15, 5),  # Pale Yellow: 15% to 20%
-                ("#D0F0C0", 20, 15),  # Pale Green: 20% to 35%
-                ("#FFEECC", 35, 5),  # Pale Yellow: 35% to 40%
-                ("#F8A8A8", 40, 10),  # Muted Red: 40% to 50%
-            ]
+            if age_group == "20-39":
+                segments = [
+                    ("#F8A8A8", 0, 15),  # Bad: 0-15%
+                    ("#FFEECC", 15, 5),  # Okay: 15-20%
+                    ("#D0F0C0", 20, 15),  # Good: 20-35%
+                    ("#FFEECC", 35, 5),  # Okay: 35-40%
+                    ("#F8A8A8", 40, 10),  # Bad: 40-50%
+                ]
+            else:  # 40-59 and 60-79 have same ranges for females
+                segments = [
+                    ("#F8A8A8", 0, 20),  # Bad: 0-20%
+                    ("#FFEECC", 20, 5),  # Okay: 20-25%
+                    ("#D0F0C0", 25, 10),  # Good: 25-35%
+                    ("#FFEECC", 35, 5),  # Okay: 35-40%
+                    ("#F8A8A8", 40, 10),  # Bad: 40-50%
+                ]
         else:  # male
-            segments = [
-                ("#F8A8A8", 0, 5),  # Muted Red: 0% to 5%
-                ("#FFEECC", 5, 5),  # Pale Yellow: 5% to 10%
-                ("#D0F0C0", 10, 10),  # Pale Green: 10% to 20%
-                ("#FFEECC", 20, 5),  # Pale Yellow: 20% to 25%
-                ("#F8A8A8", 25, 25),  # Muted Red: 25% to 50%
-            ]
+            if age_group == "20-39":
+                segments = [
+                    ("#F8A8A8", 0, 5),  # Bad: 0-5%
+                    ("#FFEECC", 5, 5),  # Okay: 5-10%
+                    ("#D0F0C0", 10, 10),  # Good: 10-20%
+                    ("#FFEECC", 20, 5),  # Okay: 20-25%
+                    ("#F8A8A8", 25, 25),  # Bad: 25-50%
+                ]
+            elif age_group == "40-59":
+                segments = [
+                    ("#F8A8A8", 0, 5),  # Bad: 0-5%
+                    ("#FFEECC", 5, 5),  # Okay: 5-10%
+                    ("#D0F0C0", 10, 10),  # Good: 10-20%
+                    ("#FFEECC", 20, 10),  # Okay: 20-30%
+                    ("#F8A8A8", 30, 20),  # Bad: 30-50%
+                ]
+            else:  # 60-79
+                segments = [
+                    ("#F8A8A8", 0, 5),  # Bad: 0-5%
+                    ("#FFEECC", 5, 5),  # Okay: 5-10%
+                    ("#D0F0C0", 10, 15),  # Good: 10-25%
+                    ("#FFEECC", 25, 5),  # Okay: 25-30%
+                    ("#F8A8A8", 30, 20),  # Bad: 30-50%
+                ]
 
         fig, ax = plt.subplots(figsize=(10, 2))
 
@@ -779,10 +905,40 @@ class GraphGenerator:
         Returns:
             Base64 string or file path
         """
-        # Coerce numeric columns
-        for col in ["Best", "LLN", "Pred.", "%Pred.", "ZScore"]:
-            if col in spirometry_df.columns:
-                spirometry_df[col] = pd.to_numeric(spirometry_df[col], errors="coerce")
+        # Coerce numeric columns - handle various column name formats
+        # Map standard column names to possible variations
+        column_aliases = {
+            "Best": ["Best", "best", "BEST"],
+            "LLN": ["LLN", "lln"],
+            "Pred.": ["Pred.", "Pred", "pred", "Predicted", "predicted"],
+            "%Pred.": [
+                "%Pred.",
+                "%Pred",
+                "%pred",
+                "% Pred.",
+                "% Pred",
+                "Pred %",
+                "Pred%",
+            ],
+            "ZScore": ["ZScore", "Z-Score", "z-score", "Zscore", "zscore", "Z Score"],
+        }
+
+        # Find and normalize column names
+        column_mapping = {}
+        for target_col, possible_names in column_aliases.items():
+            for col_name in possible_names:
+                if col_name in spirometry_df.columns:
+                    column_mapping[target_col] = col_name
+                    # Convert to numeric
+                    spirometry_df[col_name] = pd.to_numeric(
+                        spirometry_df[col_name], errors="coerce"
+                    )
+                    break
+
+        # If standard columns don't exist, create aliases
+        for target_col, source_col in column_mapping.items():
+            if target_col not in spirometry_df.columns and source_col != target_col:
+                spirometry_df[target_col] = spirometry_df[source_col]
 
         # Select rows of interest
         rows_map = {
@@ -793,20 +949,49 @@ class GraphGenerator:
 
         records = []
         for label, param in rows_map.items():
+            # Try exact match first
             row = spirometry_df.loc[spirometry_df["Parameters"].str.strip() == param]
             if row.empty:
+                # Try case-insensitive match
+                row = spirometry_df.loc[
+                    spirometry_df["Parameters"].str.strip().str.upper() == param.upper()
+                ]
+            if row.empty:
+                # Try matching without % sign
+                if "%" in param:
+                    param_no_pct = param.replace("%", "")
+                    row = spirometry_df.loc[
+                        spirometry_df["Parameters"].str.strip() == param_no_pct
+                    ]
+            if row.empty:
+                print(f"Warning: Could not find parameter '{param}' in spirometry data")
+                print(f"Available parameters: {spirometry_df['Parameters'].tolist()}")
                 continue
             row = row.iloc[0]
+            # Get values with fallbacks for column name variations
+            best_val = row.get("Best", row.get("best", pd.NA))
+            pct_val = row.get(
+                "%Pred.", row.get("%Pred", row.get("Pred %", row.get("Pred%", pd.NA)))
+            )
+            z_val = row.get("ZScore", row.get("Z-Score", row.get("Zscore", pd.NA)))
+
             records.append(
                 {
                     "label": label,
                     "param": param,
-                    "best": row["Best"],
-                    "pct": row["%Pred."],
-                    "z": row["ZScore"],
+                    "best": best_val,
+                    "pct": pct_val,
+                    "z": z_val,
                 }
             )
 
+        # Validate we have exactly 3 records
+        if len(records) != 3:
+            raise ValueError(
+                f"Expected 3 spirometry parameters (FVC, FEV1, FEV1/FVC%), "
+                f"but found {len(records)}. Found: {[r['param'] for r in records]}"
+            )
+
         # Figure setup
         fig, axes = plt.subplots(
             nrows=3,
@@ -936,3 +1121,187 @@ class GraphGenerator:
         plt.close()
 
         return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
+
+    def generate_metabolism_chart(
+        self, rmr_kcal: float, save_as_base64: bool = True
+    ) -> str:
+        """
+        Generate metabolism chart (Slow vs Fast Metabolism).
+
+        Args:
+            rmr_kcal: Resting metabolic rate in kcal/day
+            save_as_base64: If True, return base64 string, else return file path
+
+        Returns:
+            Base64 string or file path
+        """
+        from matplotlib.patches import FancyBboxPatch
+
+        fig, ax = plt.subplots(figsize=(10, 2.5))
+
+        # Chart data and positions
+        categories = ["Very Slow", "Slow", "Average", "Fast", "Very Fast"]
+        positions = [1500, 3000, 4500, 6000, 7500]
+        indicator_pos = rmr_kcal
+        highlight_end = rmr_kcal
+
+        # Main Bar (Background)
+        main_bar = FancyBboxPatch(
+            (0, 0.4),
+            9000,
+            0.2,
+            boxstyle="round,pad=0,rounding_size=0.1",
+            ec="none",
+            fc="#E0E0E0",
+        )
+        ax.add_patch(main_bar)
+
+        # Highlighted Bar
+        highlight_bar = FancyBboxPatch(
+            (0, 0.4),
+            highlight_end,
+            0.2,
+            boxstyle="round,pad=0,rounding_size=0.1",
+            ec="none",
+            fc="#B2FFC8",
+        )
+        ax.add_patch(highlight_bar)
+
+        # Text and Labels
+        ax.text(
+            highlight_end / 2,
+            0.5,
+            f"{rmr_kcal:.0f}kCals",
+            ha="center",
+            va="center",
+            color="#006400",
+            fontsize=14,
+            weight="bold",
+        )
+
+        # Indicator Triangle
+        ax.plot(indicator_pos, 0.65, "v", markersize=15, color="#606060", clip_on=False)
+
+        # Ticks and Labels
+        for pos, label in zip(positions, categories):
+            ax.text(
+                pos, 0.15, label, ha="center", va="center", fontsize=12, color="#333333"
+            )
+            ax.plot([pos, pos], [0.35, 0.39], color="grey", lw=5)
+
+        # Chart Styling
+        ax.set_title("Slow vs Fast Metabolism", fontsize=18, weight="bold", loc="left")
+        ax.set_xlim(0, 9000)
+        ax.set_ylim(0, 1)
+        ax.axis("off")
+
+        plt.tight_layout()
+
+        chart_path = self.charts_dir / "metabolism_chart.png"
+        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
+        plt.close()
+
+        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)
+
+    def generate_fuel_source_chart(
+        self, fat_percentage: float, save_as_base64: bool = True
+    ) -> str:
+        """
+        Generate fuel source chart (Fats vs Carbs).
+
+        Args:
+            fat_percentage: Fat percentage at rest
+            save_as_base64: If True, return base64 string, else return file path
+
+        Returns:
+            Base64 string or file path
+        """
+        from matplotlib.patches import FancyBboxPatch
+
+        fig, ax = plt.subplots(figsize=(10, 2.5))
+
+        carb_percentage = 100 - fat_percentage
+        optimal_point = 75
+
+        # Main Bars (Fats and Carbs)
+        # Fats bar (yellow)
+        fats_bar = FancyBboxPatch(
+            (0, 0.4),
+            fat_percentage,
+            0.2,
+            boxstyle="round,pad=0,rounding_size=0.1",
+            ec="none",
+            fc="#FEEAAB",
+        )
+        ax.add_patch(fats_bar)
+
+        # Carbs bar (blue) - starts where the fats bar ends
+        carbs_bar = FancyBboxPatch(
+            (fat_percentage, 0.4),
+            carb_percentage,
+            0.2,
+            boxstyle="round,pad=0,rounding_size=0.1",
+            ec="none",
+            fc="#A7F5FF",
+        )
+        ax.add_patch(carbs_bar)
+
+        # Text and Labels
+        ax.text(
+            fat_percentage / 2,
+            0.5,
+            f"Fats\n{fat_percentage:.1f}%",
+            ha="center",
+            va="center",
+            color="#333333",
+            fontsize=12,
+            weight="bold",
+        )
+        ax.text(
+            fat_percentage + carb_percentage / 2,
+            0.5,
+            f"Carbs\n{carb_percentage:.1f}%",
+            ha="center",
+            va="center",
+            color="#333333",
+            fontsize=12,
+            weight="bold",
+        )
+
+        # Add 'Optimal' label
+        ax.text(optimal_point, 0.75, "Optimal", ha="center", va="center", fontsize=12)
+
+        # Indicator Triangle
+        ax.plot(
+            fat_percentage, 0.65, "v", markersize=15, color="#606060", clip_on=False
+        )
+
+        # Ticks and Labels
+        positions = [0, 25, 50, 75, 100]
+        for pos in positions:
+            ax.text(
+                pos,
+                0.15,
+                str(pos),
+                ha="center",
+                va="center",
+                fontsize=12,
+                color="#333333",
+            )
+            ax.plot([pos, pos], [0.35, 0.39], color="grey", lw=5)
+
+        # Add a special tick for the 'Optimal' point
+        ax.plot([optimal_point, optimal_point], [0.6, 0.7], color="black", lw=2)
+
+        # Chart Styling
+        ax.set_title("Fuel Source", fontsize=18, weight="bold", loc="left")
+        ax.set_ylim(0, 1)
+        ax.axis("off")
+
+        plt.tight_layout()
+
+        chart_path = self.charts_dir / "fuel_source_chart.png"
+        plt.savefig(chart_path, bbox_inches="tight", dpi=300)
+        plt.close()
+
+        return self._image_to_base64(chart_path) if save_as_base64 else str(chart_path)

app/services/report_generator.py

@@ -151,7 +151,7 @@ class ReportGeneratorService:
         }
 
     def generate_html(
-        self, patient_info: Dict[str, Any], context_list: List[Dict[str, Any]]
+        self, patient_info: Dict[str, Any], contexts: Dict[str, Dict[str, Any]]
     ) -> str:
         """
         Generate HTML content for the report.
@@ -159,7 +159,7 @@ class ReportGeneratorService:
         Args:
             patient_info: Dictionary containing patient information
                 (patient_name, age, height, weight, focus)
-            context_list: List of context dictionaries for each page
+            contexts: Dictionary with keys 'page_1', 'page_2', etc., each containing context data
 
         Returns:
             Complete HTML document as string
@@ -175,6 +175,9 @@ class ReportGeneratorService:
             "focus": patient_info.get("focus", "Endurance"),
         }
 
+        # Get total number of pages
+        num_pages = len(contexts)
+
         # Footer context
         footer_context = [
             {
@@ -183,7 +186,7 @@ class ReportGeneratorService:
                 "social": "@ishplabs",
                 "page_number": i + 1,
             }
-            for i in range(len(context_list))
+            for i in range(num_pages)
         ]
 
         # Render header
@@ -195,11 +198,13 @@ class ReportGeneratorService:
             for context in footer_context
         ]
 
-        # Render pages
-        for i, context in enumerate(context_list):
-            template = self.env.get_template(f"page_{i + 1}.html").render(context)
+        # Render pages - iterate through pages in order
+        for i in range(1, num_pages + 1):
+            page_key = f"page_{i}"
+            context = contexts.get(page_key, {})
+            template = self.env.get_template(f"page_{i}.html").render(context)
 
-            if (i + 1) > 2:
+            if i > 2:
                 full_html = f"""
                 <div class="page flex flex-col justify-between">
                     <div>
@@ -209,7 +214,7 @@ class ReportGeneratorService:
                         {template}
                     </main>
                     <div class="border-t text-center text-sm text-gray-600">
-                        {footer_html_list[i]}
+                        {footer_html_list[i - 1]}
                     </div>
                 </div>
                 """
@@ -284,10 +289,10 @@ class ReportGeneratorService:
         self,
         spirometry_pdf_path: str,
         pnoe_csv_path: str,
-        seca_excel_path: str,
         patient_info: Dict[str, Any],
         output_filename: str = None,
         metric_overrides: Optional[Dict[str, Any]] = None,
+        oxygenation_csv_path: Optional[str] = None,
     ) -> Dict[str, Any]:
         """
         Generate complete medical report from uploaded files.
@@ -325,69 +330,165 @@ class ReportGeneratorService:
         graphs_generated = self.generate_graphs(df)
 
         # Create graph dictionary with base64 encoded images
+        import base64
+
         graphs_dict = {}
         for graph in graphs_generated:
             # Read the graph file and convert to base64
             graph_path = Path(graph["path"])
             if graph_path.exists():
-                import base64
-
                 with open(graph_path, "rb") as f:
                     graphs_dict[graph["name"]] = base64.b64encode(f.read()).decode(
                         "utf-8"
                     )
 
         # Also generate body composition charts
-        # Extract patient data for these charts
-        patient_name = patient_info.get("patient_name", "").split()[-1]  # Get last name
+        # Use patient info directly (no SECA file needed)
+        fat_pct = patient_info.get("fat_percentage", 0)
+        age = patient_info.get("age", 25)
+        gender = patient_info.get("gender", "female").lower()
 
-        # Load SECA data to get body composition info
-        seca_df = pd.read_excel(seca_excel_path)
-        patient_data = seca_df[
-            seca_df["LastName"].str.contains(patient_name, case=False, na=False)
-        ]
+        # Convert weight to kg if needed
+        weight_str = str(patient_info.get("weight", "0"))
+        # Extract numeric value and unit
+        weight_str_clean = (
+            weight_str.replace("lbs", "").replace("kg", "").replace(" ", "").strip()
+        )
+        try:
+            weight_value = float(weight_str_clean)
+        except ValueError:
+            print(f"Warning: Could not parse weight '{weight_str}', using default 0")
+            weight_value = 0.0
 
-        if not patient_data.empty:
-            row = patient_data.iloc[0]
-            weight_kg = float(row.get("Weight", 0))
-            fat_pct = float(row.get("Adult_FMP", 0))
-            age = int(row.get("Age", patient_info.get("age", 25)))
-            gender = row.get("Gender", "female").lower()
+        # Convert to kg if weight is in lbs
+        if "lbs" in weight_str.lower():
+            weight_kg = weight_value / 2.20462  # Convert lbs to kg
+        else:
+            weight_kg = weight_value  # Already in kg or assume kg if no unit specified
 
-            fat_mass_lbs = weight_kg * fat_pct / 100 * 2.20462
-            lean_mass_lbs = weight_kg * (1 - fat_pct / 100) * 2.20462
+        # Calculate fat and lean mass in pounds
+        fat_mass_lbs = weight_kg * fat_pct / 100 * 2.20462
+        lean_mass_lbs = weight_kg * (1 - fat_pct / 100) * 2.20462
 
-            # Generate body composition chart
-            body_comp_b64 = self.graph_generator.generate_body_composition_chart(
-                fat_mass_lbs, lean_mass_lbs, save_as_base64=True
+        # Generate body composition chart (save as file first, then convert to base64)
+        try:
+            body_comp_path = self.graph_generator.generate_body_composition_chart(
+                fat_mass_lbs, lean_mass_lbs, save_as_base64=False
             )
-            graphs_dict["body_composition"] = body_comp_b64
-
-            # Generate body fat percent chart
-            body_fat_b64 = self.graph_generator.generate_body_fat_percent_chart(
-                fat_pct, age, gender, save_as_base64=True
+            graphs_generated.append(
+                {"name": "body_composition", "path": str(body_comp_path)}
             )
-            graphs_dict["body_fat_percent"] = body_fat_b64
+            # Convert to base64 for graphs_dict
+            with open(body_comp_path, "rb") as f:
+                graphs_dict["body_composition"] = base64.b64encode(f.read()).decode(
+                    "utf-8"
+                )
+        except Exception as e:
+            print(f"Warning: Could not generate body composition chart: {e}")
+            graphs_dict["body_composition"] = ""
+
+        # Generate body fat percent chart (save as file first, then convert to base64)
+        try:
+            body_fat_path = self.graph_generator.generate_body_fat_percent_chart(
+                fat_pct, age, gender, save_as_base64=False
+            )
+            graphs_generated.append(
+                {"name": "body_fat_percent", "path": str(body_fat_path)}
+            )
+            # Convert to base64 for graphs_dict
+            with open(body_fat_path, "rb") as f:
+                graphs_dict["body_fat_percent"] = base64.b64encode(f.read()).decode(
+                    "utf-8"
+                )
+        except Exception as e:
+            print(f"Warning: Could not generate body fat percent chart: {e}")
+            graphs_dict["body_fat_percent"] = ""
 
         # Generate spirometry chart
         print("Step 4: Generating spirometry chart...")
         try:
             spirometry_df = pd.read_csv(spirometry_csv_path)
             print(f"Spirometry data loaded: {len(spirometry_df)} rows")
+            print(f"Spirometry columns: {spirometry_df.columns.tolist()}")
+            if "Parameters" in spirometry_df.columns:
+                print(f"Available parameters: {spirometry_df['Parameters'].tolist()}")
             spirometry_chart_b64 = self.graph_generator.generate_spirometry_chart(
                 spirometry_df, save_as_base64=True
             )
             graphs_dict["spirometry_chart"] = spirometry_chart_b64
+            print("Spirometry chart generated successfully")
         except Exception as e:
+            import traceback
+            error_details = traceback.format_exc()
             print(f"Warning: Could not generate spirometry chart: {e}")
+            print(f"Error details: {error_details}")
             graphs_dict["spirometry_chart"] = ""
 
+        # Generate TSI chart if oxygenation CSV is provided
+        if oxygenation_csv_path:
+            print("Step 4.5: Generating TSI chart...")
+            try:
+                oxygenation_df = pd.read_csv(oxygenation_csv_path)
+                tsi_chart_b64 = self.graph_generator.generate_tsi_chart(
+                    oxygenation_df, save_as_base64=True
+                )
+                graphs_dict["tsi_chart"] = tsi_chart_b64
+            except Exception as e:
+                print(f"Warning: Could not generate TSI chart: {e}")
+                graphs_dict["tsi_chart"] = ""
+
+        # Generate metabolism and fuel source charts for page 5
+        print("Step 4.6: Generating metabolism and fuel source charts...")
+        try:
+            # Calculate RMR and fuel source from pnoe data
+            from services.context_generator import ContextGenerator
+
+            temp_context_gen = ContextGenerator()
+            temp_context_gen.load_data(pnoe_csv_path, str(spirometry_csv_path), None)
+            temp_context_gen.patient_info = {
+                "name": patient_info.get("first_name", ""),
+                "last_name": patient_info.get("last_name", ""),
+                "age": patient_info.get("age", 25),
+                "weight": weight_kg,
+                "fat_percentage": fat_pct,
+                "gender": gender,
+            }
+            rmr_metrics = temp_context_gen.calculate_rmr_and_fuel_source()
+
+            # Generate metabolism chart
+            metabolism_chart_b64 = self.graph_generator.generate_metabolism_chart(
+                rmr_metrics["rmr_kcal"], save_as_base64=True
+            )
+            graphs_dict["metabolism_chart"] = metabolism_chart_b64
+
+            # Generate fuel source chart
+            fuel_source_chart_b64 = self.graph_generator.generate_fuel_source_chart(
+                rmr_metrics["rest_fat_percentage"], save_as_base64=True
+            )
+            graphs_dict["fuel_source_chart"] = fuel_source_chart_b64
+        except Exception as e:
+            print(f"Warning: Could not generate metabolism/fuel source charts: {e}")
+            graphs_dict["metabolism_chart"] = ""
+            graphs_dict["fuel_source_chart"] = ""
+
         # Step 5: Generate context for all pages
         print("Step 5: Generating page contexts...")
+        patient_name = patient_info.get("patient_name", "")
         self.context_generator.load_data(
-            pnoe_csv_path, str(spirometry_csv_path), seca_excel_path
+            pnoe_csv_path,
+            str(spirometry_csv_path),
+            None,  # No SECA file
         )
-        context_list = self.context_generator.generate_all_contexts(
+        # Set patient info manually
+        self.context_generator.patient_info = {
+            "name": patient_info.get("first_name", ""),
+            "last_name": patient_info.get("last_name", ""),
+            "age": patient_info.get("age", 25),
+            "weight": weight_kg,
+            "fat_percentage": fat_pct,
+            "gender": gender,
+        }
+        contexts = self.context_generator.generate_all_contexts(
             patient_name, graphs_dict, metric_overrides=metric_overrides
         )
 
@@ -396,7 +497,7 @@ class ReportGeneratorService:
         analysis_data["graphs_count"] = len(graphs_generated)
 
         # Step 6: Generate HTML
-        html_content = self.generate_html(patient_info, context_list)
+        html_content = self.generate_html(patient_info, contexts)
 
         # Step 7: Generate PDF
         if output_filename is None:

app/templates/base.html

@@ -39,3 +39,4 @@
 </html>
 
 
+

app/templates/upload.html

@@ -84,9 +84,16 @@
                                 class="mt-1 block w-full text-sm text-gray-500 file:mr-4 file:py-2 file:px-4 file:rounded-md file:border-0 file:text-sm file:font-semibold file:bg-indigo-50 file:text-indigo-700 hover:file:bg-indigo-100">
                         </div>
                         <div>
-                            <label for="seca_excel" class="block text-sm font-medium text-gray-700">SECA Excel</label>
-                            <input type="file" name="seca_excel" id="seca_excel" accept=".xlsx,.xls" required
+                            <label for="fat_percentage" class="block text-sm font-medium text-gray-700">Body Fat Percentage (%)</label>
+                            <input type="number" step="0.1" name="fat_percentage" id="fat_percentage" required min="0" max="100"
+                                class="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500 sm:text-sm px-3 py-2 border"
+                                placeholder="22.5">
+                        </div>
+                        <div>
+                            <label for="oxygenation_csv" class="block text-sm font-medium text-gray-700">Muscle Oxygenation CSV (Optional)</label>
+                            <input type="file" name="oxygenation_csv" id="oxygenation_csv" accept=".csv"
                                 class="mt-1 block w-full text-sm text-gray-500 file:mr-4 file:py-2 file:px-4 file:rounded-md file:border-0 file:text-sm file:font-semibold file:bg-indigo-50 file:text-indigo-700 hover:file:bg-indigo-100">
+                            <p class="mt-1 text-xs text-gray-500">Upload NIRS muscle oxygen CSV file to generate TSI graph</p>
                         </div>
                     </div>
                 </div>

notebooks/graphs.ipynb

@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "99116a35",
    "metadata": {},
    "outputs": [],
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "470e871e",
    "metadata": {},
    "outputs": [
@@ -290,7 +290,14 @@
     "    # --- Chart data and positions ---\n",
     "    categories = ['Very Slow', 'Slow', 'Average', 'Fast', 'Very Fast']\n",
     "    positions = [1500, 3000, 4500, 6000, 7500]\n",
-    "    kcal_value = 1386\n",
+    "    # Step 1: Filter resting phase (usually lowest VO2 or MET values)\n",
+    "    rest_phase = df[df['MET'] <= 1.1]  # assuming <1.1 MET means rest\n",
+    "\n",
+    "    # Step 2: Compute resting metabolic rate\n",
+    "    rmr = rest_phase['EE(kcal/day)'].mean()\n",
+    "\n",
+    "    print(f\"Estimated RMR from data: {rmr:.0f} kcal/day\")\n",
+    "    kcal_value = rmr\n",
     "    # Position the indicator and highlight based on the kcal value\n",
     "    # For this example, we'll place it in the 'Very Slow' section.\n",
     "    indicator_pos = kcal_value\n",
@@ -349,7 +356,13 @@
     "    fig, ax = plt.subplots(figsize=(10, 2.5))\n",
     "\n",
     "    # --- Chart data and positions ---\n",
-    "    fat_percentage = 33\n",
+    "    rest_phase = df[df['RER'] == 0.9]  # filter rest data\n",
+    "    fat_rest = rest_phase['FAT(%)'].mean()\n",
+    "    carb_rest = rest_phase['CARBS(%)'].mean()\n",
+    "\n",
+    "    print(f\"Resting phase fuel mix: Fats {fat_rest:.1f}%, Carbs {carb_rest:.1f}%\")\n",
+    "\n",
+    "    fat_percentage = fat_rest\n",
     "    carb_percentage = 100 - fat_percentage\n",
     "    optimal_point = 75\n",
     "\n",

notebooks/page_context_gen.ipynb

@@ -18,13 +18,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "da5ac3c1",
    "metadata": {},
    "outputs": [],
    "source": [
     "pnoe_df = pd.read_csv('data/pnoe_data.csv', delimiter=';')\n",
-    "patients_info = pd.read_excel('data/patients_data.xlsx')\n",
     "spirometry_df = pd.read_csv('data/spirometry_data.csv')"
    ]
   },
@@ -254,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "2fa8ff13",
    "metadata": {},
    "outputs": [
@@ -270,15 +269,10 @@
     }
    ],
    "source": [
-    "def body_composition_chart(first_name='Keirstyn', last_name='Moran'):\n",
+    "def body_composition_chart(fat_percentage=22.4, weight_kg=70):\n",
     "\n",
     "    \n",
     "    #=========================== Body Composition Donut Chart ========================#\n",
-    "    patient_data = patients_info[(patients_info['FirstName'].str.contains(first_name, case=False, na=False)) & \n",
-    "                                  (patients_info['LastName'].str.contains(last_name, case=False, na=False))]\n",
-    "# Get the fat mass percentage for Keirstyn\n",
-    "    fat_percentage = patient_data['Adult_FMP'].iloc[0]\n",
-    "    weight_kg = patient_data['Weight'].iloc[0]\n",
     "    lean_percentage = 100 - fat_percentage\n",
     "\n",
     "# Create donut chart\n",