Add HTML templates for medical report generator with navigation, upload, edit, and preview functionalities

- Created base template with navigation and layout structure
- Implemented upload.html for patient data and file uploads
- Developed edit.html for editing calculated metrics
- Added preview.html for displaying generated report previews
- Enhanced user experience with Tailwind CSS styling

app/services/context_generator.py

@@ -101,41 +101,108 @@ class ContextGenerator:
                 }
         return self.patient_info
 
-    def calculate_spirometry_metrics(self) -> 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%"]:
-            row = self.spirometry_df.loc[
-                self.spirometry_df["Parameters"].str.strip() == param
-            ]
-            if not row.empty:
-                param_key = param.lower().replace("/", "_").replace("%", "_pct")
-                metrics[f"{param_key}_best"] = row["Best"].values[0]
-                metrics[f"{param_key}_pred"] = row["%Pred."].values[0]
+            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"])
+            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]
+            
+            if f"{param_key}_pred" in metric_overrides:
+                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]
         return metrics
 
-    def calculate_pnoe_metrics(self) -> Dict:
+    def calculate_pnoe_metrics(self, metric_overrides: Optional[Dict] = None) -> Dict:
         """Calculate all Pnoe-derived metrics"""
+        if metric_overrides is None:
+            metric_overrides = {}
+        
         metrics = {}
-        metrics["vo2_max"] = self.pnoe_df["VO2(ml/min)_smoothed"].max()
-        metrics["vo2_max_per_kg"] = metrics["vo2_max"] / self.patient_info["weight"]
+        
+        # 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:
+            metrics["vo2_max_per_kg"] = metrics["vo2_max"] / self.patient_info["weight"]
 
-        peak_vt_idx = self.pnoe_df["VT(l)_smoothed"].idxmax()
-        peak_vt_row = self.pnoe_df.loc[peak_vt_idx]
-        metrics["peak_vt"] = peak_vt_row["VT(l)_smoothed"]
-        metrics["peak_vt_hr"] = peak_vt_row["HR(bpm)_smoothed"]
+        # Peak VT metrics
+        if "peak_vt" in metric_overrides:
+            metrics["peak_vt"] = float(metric_overrides["peak_vt"])
+            # Need to get HR from override or calculate
+            if "peak_vt_hr" in metric_overrides:
+                metrics["peak_vt_hr"] = float(metric_overrides["peak_vt_hr"])
+            else:
+                peak_vt_idx = self.pnoe_df["VT(l)_smoothed"].idxmax()
+                peak_vt_row = self.pnoe_df.loc[peak_vt_idx]
+                metrics["peak_vt_hr"] = peak_vt_row["HR(bpm)_smoothed"]
+        else:
+            peak_vt_idx = self.pnoe_df["VT(l)_smoothed"].idxmax()
+            peak_vt_row = self.pnoe_df.loc[peak_vt_idx]
+            metrics["peak_vt"] = peak_vt_row["VT(l)_smoothed"]
+            metrics["peak_vt_hr"] = peak_vt_row["HR(bpm)_smoothed"]
 
-        fat_max_idx = self.pnoe_df["FAT_smoothed"].idxmax()
-        fat_max_row = self.pnoe_df.loc[fat_max_idx]
-        metrics["fat_max_value"] = fat_max_row["FAT_smoothed"]
-        metrics["fat_max_hr"] = fat_max_row["HR(bpm)_smoothed"]
+        # Fat Max metrics
+        if "fat_max_value" in metric_overrides:
+            metrics["fat_max_value"] = float(metric_overrides["fat_max_value"])
+            if "fat_max_hr" in metric_overrides:
+                metrics["fat_max_hr"] = float(metric_overrides["fat_max_hr"])
+            else:
+                fat_max_idx = self.pnoe_df["FAT_smoothed"].idxmax()
+                fat_max_row = self.pnoe_df.loc[fat_max_idx]
+                metrics["fat_max_hr"] = fat_max_row["HR(bpm)_smoothed"]
+        else:
+            fat_max_idx = self.pnoe_df["FAT_smoothed"].idxmax()
+            fat_max_row = self.pnoe_df.loc[fat_max_idx]
+            metrics["fat_max_value"] = fat_max_row["FAT_smoothed"]
+            metrics["fat_max_hr"] = fat_max_row["HR(bpm)_smoothed"]
 
-        vt1, vt2 = self._detect_thresholds()
-        metrics["vt1"] = vt1
-        metrics["vt2"] = vt2
+        # VT1 and VT2 thresholds
+        if "vt1" in metric_overrides:
+            metrics["vt1"] = metric_overrides["vt1"]
+        else:
+            vt1, _ = self._detect_thresholds()
+            metrics["vt1"] = vt1
+        
+        if "vt2" in metric_overrides:
+            metrics["vt2"] = metric_overrides["vt2"]
+        else:
+            _, vt2 = self._detect_thresholds()
+            metrics["vt2"] = vt2
 
-        zones = self._calculate_hr_zones(vt1, vt2, fat_max_row)
-        metrics.update(zones)
+        # Heart rate zones
+        if any(f"zone{i}_bpm" in metric_overrides for i in range(1, 6)):
+            for i in range(1, 6):
+                zone_key = f"zone{i}_bpm"
+                if zone_key in metric_overrides:
+                    metrics[zone_key] = metric_overrides[zone_key]
+        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)
+            metrics.update(zones)
+        
         return metrics
 
     def _detect_thresholds(self) -> Tuple[Optional[Dict], Optional[Dict]]:
@@ -195,12 +262,20 @@ class ContextGenerator:
         return zones
 
     def generate_all_contexts(
-        self, patient_name: str, graphs: Dict[str, str]
+        self, patient_name: str, graphs: Dict[str, str], metric_overrides: Optional[Dict] = None
     ) -> List[Dict]:
         """Main method to generate all page contexts"""
+        if metric_overrides is None:
+            metric_overrides = {}
+        
         self.extract_patient_info(patient_name)
-        spirometry_metrics = self.calculate_spirometry_metrics()
-        pnoe_metrics = self.calculate_pnoe_metrics()
+        
+        # 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)
 
         contexts = []
         contexts.append(