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