feat: Implement report generator service for medical reports
- Added ReportGeneratorService to handle generation of medical reports from uploaded files. - Implemented methods for processing Pnoe CSV data, generating graphs, and calculating analysis metrics. - Integrated Jinja2 for HTML report generation with customizable templates. - Added functionality to convert HTML content to PDF using Playwright. - Ensured proper directory structure for saving generated graphs and reports.
This commit is contained in:
bolade
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.venv
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data/
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data/
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.env
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"""
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FastAPI application for report generation with file uploads.
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This API allows users to:
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1. Upload required files (Spirometry PDF, Pnoe CSV, SECA Excel)
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2. Generate reports with graphs and analysis
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"""
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import shutil
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from pathlib import Path
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from typing import Dict, Optional
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import pandas as pd
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from fastapi import FastAPI, File, HTTPException, UploadFile
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from fastapi.responses import FileResponse
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from pydantic import BaseModel
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from graph_generator import GraphGenerator
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app = FastAPI(
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title="Medical Report Generation API",
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description="API for generating medical performance reports with analysis and graphs",
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version="1.0.0",
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)
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# Define upload directory
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UPLOAD_DIR = Path("uploads")
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UPLOAD_DIR.mkdir(exist_ok=True)
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# Define output directories
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GRAPHS_DIR = Path("graphs")
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GRAPHS_DIR.mkdir(exist_ok=True)
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REPORTS_DIR = Path("reports")
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REPORTS_DIR.mkdir(exist_ok=True)
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# Storage for uploaded files metadata
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uploaded_files_store: Dict[str, Dict[str, str]] = {}
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class FileUploadResponse(BaseModel):
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message: str
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filename: str
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file_type: str
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file_path: str
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class ReportRequest(BaseModel):
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patient_name: str
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age: int
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height: str
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weight: str
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focus: str = "Endurance"
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session_id: Optional[str] = "default"
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class ReportResponse(BaseModel):
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message: str
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report_path: str
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graphs_generated: list
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analysis_data: dict
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@app.get("/")
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async def root():
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"""Root endpoint with API information"""
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return {
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"message": "Medical Report Generation API",
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"version": "1.0.0",
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"endpoints": {
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"upload_spirometry": "/upload/spirometry",
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"upload_pnoe": "/upload/pnoe",
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"upload_seca": "/upload/seca",
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"generate_report": "/generate-report",
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"list_uploads": "/uploads",
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"health": "/health",
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},
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}
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@app.get("/health")
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async def health_check():
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"""Health check endpoint"""
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return {"status": "healthy", "service": "report-generation-api"}
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@app.post("/upload/spirometry", response_model=FileUploadResponse)
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async def upload_spirometry_pdf(
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file: UploadFile = File(...), session_id: str = "default"
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):
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"""
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Upload Spirometry PDF file for analysis.
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Args:
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file: Spirometry PDF file
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session_id: Session identifier to group files together (default: "default")
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Returns:
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FileUploadResponse with upload details
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"""
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if not file.filename.endswith(".pdf"):
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raise HTTPException(status_code=400, detail="Only PDF files are allowed")
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# Create session directory
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session_dir = UPLOAD_DIR / session_id
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session_dir.mkdir(exist_ok=True)
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# Save file
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file_path = session_dir / f"spirometry_{file.filename}"
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with open(file_path, "wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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# Store metadata
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if session_id not in uploaded_files_store:
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uploaded_files_store[session_id] = {}
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uploaded_files_store[session_id]["spirometry_pdf"] = str(file_path)
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return FileUploadResponse(
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message="Spirometry PDF uploaded successfully",
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filename=file.filename,
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file_type="spirometry_pdf",
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file_path=str(file_path),
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)
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@app.post("/upload/pnoe", response_model=FileUploadResponse)
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async def upload_pnoe_csv(file: UploadFile = File(...), session_id: str = "default"):
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"""
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Upload Pnoe CSV file for metabolic analysis.
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Args:
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file: Pnoe CSV file
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session_id: Session identifier to group files together (default: "default")
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Returns:
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FileUploadResponse with upload details
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"""
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if not file.filename.endswith(".csv"):
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raise HTTPException(status_code=400, detail="Only CSV files are allowed")
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# Create session directory
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session_dir = UPLOAD_DIR / session_id
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session_dir.mkdir(exist_ok=True)
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# Save file
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file_path = session_dir / f"pnoe_{file.filename}"
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with open(file_path, "wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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# Store metadata
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if session_id not in uploaded_files_store:
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uploaded_files_store[session_id] = {}
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uploaded_files_store[session_id]["pnoe_csv"] = str(file_path)
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return FileUploadResponse(
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message="Pnoe CSV uploaded successfully",
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filename=file.filename,
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file_type="pnoe_csv",
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file_path=str(file_path),
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)
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@app.post("/upload/seca", response_model=FileUploadResponse)
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async def upload_seca_excel(file: UploadFile = File(...), session_id: str = "default"):
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"""
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Upload SECA body composition Excel file.
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Args:
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file: SECA Excel file (.xlsx)
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session_id: Session identifier to group files together (default: "default")
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Returns:
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FileUploadResponse with upload details
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"""
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if not file.filename.endswith((".xlsx", ".xls")):
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raise HTTPException(
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status_code=400, detail="Only Excel files (.xlsx, .xls) are allowed"
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)
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# Create session directory
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session_dir = UPLOAD_DIR / session_id
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session_dir.mkdir(exist_ok=True)
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# Save file
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file_path = session_dir / f"seca_{file.filename}"
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with open(file_path, "wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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# Store metadata
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if session_id not in uploaded_files_store:
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uploaded_files_store[session_id] = {}
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uploaded_files_store[session_id]["seca_excel"] = str(file_path)
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return FileUploadResponse(
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message="SECA Excel uploaded successfully",
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filename=file.filename,
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file_type="seca_excel",
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file_path=str(file_path),
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)
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@app.get("/uploads")
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async def list_uploads(session_id: str = "default"):
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"""
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List all uploaded files for a session.
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Args:
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session_id: Session identifier (default: "default")
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Returns:
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Dictionary of uploaded files
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"""
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if session_id not in uploaded_files_store:
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return {"session_id": session_id, "files": {}, "message": "No files uploaded"}
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return {
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"session_id": session_id,
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"files": uploaded_files_store[session_id],
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"files_count": len(uploaded_files_store[session_id]),
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}
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@app.post("/generate-report", response_model=ReportResponse)
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async def generate_report(report_request: ReportRequest):
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"""
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Generate a comprehensive medical report with graphs and analysis.
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Args:
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report_request: Report configuration including patient details
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Returns:
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ReportResponse with report path and analysis data
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"""
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session_id = report_request.session_id
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# Check if all required files are uploaded
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if session_id not in uploaded_files_store:
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raise HTTPException(
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status_code=400,
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detail=f"No files found for session '{session_id}'. Please upload files first.",
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)
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files = uploaded_files_store[session_id]
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required_files = ["spirometry_pdf", "pnoe_csv", "seca_excel"]
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missing_files = [f for f in required_files if f not in files]
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if missing_files:
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raise HTTPException(
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status_code=400,
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detail=f"Missing required files: {', '.join(missing_files)}. Please upload all files first.",
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)
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try:
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# Initialize graph generator
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graph_gen = GraphGenerator(charts_dir=str(GRAPHS_DIR))
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# Load and process Pnoe data
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df = pd.read_csv(files["pnoe_csv"], delimiter=";")
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df = df.apply(pd.to_numeric, errors="ignore")
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# Calculate derived columns
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df["VO2 Pulse"] = df["VO2(ml/min)"] / df["HR(bpm)"]
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df["VO2 Breath"] = df["VO2(ml/min)"] / df["BF(bpm)"]
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df["CHO"] = df["EE(kcal/min)"] * df["CARBS(%)"] / 100
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df["FAT"] = df["EE(kcal/min)"] * df["FAT(%)"] / 100
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# Smooth columns
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window_size = 10
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columns_to_smooth = [
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"VO2(ml/min)",
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"VCO2(ml/min)",
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"HR(bpm)",
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"VT(l)",
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"BF(bpm)",
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"VE(l/min)",
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"VO2 Pulse",
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"VO2 Breath",
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"CHO",
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"FAT",
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]
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for col in columns_to_smooth:
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if col in df.columns:
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df[f"{col}_smoothed"] = (
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df[col].rolling(window=window_size, min_periods=1).mean()
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)
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# Generate graphs
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graphs_generated = []
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# Generate all available graphs from the graph generator
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try:
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respiratory_path = graph_gen.generate_respiratory_chart(
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df, save_as_base64=False
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)
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graphs_generated.append(
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{"name": "respiratory", "path": str(respiratory_path)}
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)
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except Exception as e:
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print(f"Warning: Could not generate respiratory chart: {e}")
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try:
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fuel_util_path = graph_gen.generate_fuel_utilization_chart(
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df, save_as_base64=False
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)
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graphs_generated.append(
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{"name": "fuel_utilization", "path": str(fuel_util_path)}
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)
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except Exception as e:
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print(f"Warning: Could not generate fuel utilization chart: {e}")
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try:
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vo2_pulse_path = graph_gen.generate_vo2_pulse_chart(
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df, save_as_base64=False
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)
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graphs_generated.append({"name": "vo2_pulse", "path": str(vo2_pulse_path)})
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except Exception as e:
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print(f"Warning: Could not generate VO2 pulse chart: {e}")
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try:
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vo2_breath_path = graph_gen.generate_vo2_breath_chart(
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df, save_as_base64=False
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)
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graphs_generated.append(
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{"name": "vo2_breath", "path": str(vo2_breath_path)}
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)
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except Exception as e:
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print(f"Warning: Could not generate VO2 breath chart: {e}")
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try:
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fat_metabolism_path = graph_gen.generate_fat_metabolism_chart(
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df, save_as_base64=False
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)
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graphs_generated.append(
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{"name": "fat_metabolism", "path": str(fat_metabolism_path)}
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)
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except Exception as e:
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print(f"Warning: Could not generate fat metabolism chart: {e}")
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try:
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recovery_path = graph_gen.generate_recovery_chart(df, save_as_base64=False)
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graphs_generated.append({"name": "recovery", "path": str(recovery_path)})
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except Exception as e:
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print(f"Warning: Could not generate recovery chart: {e}")
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# Calculate basic analysis metrics
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analysis_data = {
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"vo2_max": float(df["VO2(ml/min)_smoothed"].max())
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if "VO2(ml/min)_smoothed" in df.columns
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else 0,
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"peak_vt": float(df["VT(l)_smoothed"].max())
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if "VT(l)_smoothed" in df.columns
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else 0,
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"max_hr": float(df["HR(bpm)_smoothed"].max())
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if "HR(bpm)_smoothed" in df.columns
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else 0,
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"graphs_count": len(graphs_generated),
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}
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# Generate PDF report using existing main.py logic
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from jinja2 import Environment, FileSystemLoader
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from context import context_list
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from main import html_string_to_pdf
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env = Environment(loader=FileSystemLoader("report_gen"))
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html_pages = []
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header_context = {
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"patient_name": report_request.patient_name,
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"age": report_request.age,
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"height": report_request.height,
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"weight": report_request.weight,
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"focus": report_request.focus,
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}
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footer_context = [
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{
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"contact_email": "info@ishplabs.com",
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"website": "www.ishplabs.com",
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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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]
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header_html = env.get_template("header.html").render(header_context)
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footer_html_list = [
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env.get_template("footer.html").render(context)
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for context in footer_context
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]
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for i, context in enumerate(context_list):
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template = env.get_template(f"page_{i + 1}.html").render(context)
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if (i + 1) > 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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{header_html}
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</div>
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<main class="flex-grow p-4">
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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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</div>
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</div>
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"""
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html_pages.append(full_html)
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else:
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html_pages.append(template)
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# Combine with page breaks
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final_html = "<div class='page-break'></div>".join(html_pages)
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# Wrap in full HTML document
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html_doc = f"""
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<link href="https://cdn.jsdelivr.net/npm/tailwindcss/dist/tailwind.min.css" rel="stylesheet">
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<style>
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html, body {{
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height: 100%;
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margin: 0;
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padding: 0;
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}}
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.page-break {{ page-break-after: always; }}
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.page {{
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height: 100vh;
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min-height: 100vh;
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display: flex;
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flex-direction: column;
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}}
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.page main {{
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flex: 1;
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overflow: hidden;
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}}
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* {{
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}}
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img {{
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max-height: 300px;
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}}
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.chart-large {{
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max-height: 500px !important;
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}}
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</style>
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</head>
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<body class="m-0 p-0">
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{final_html}
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</body>
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</html>
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"""
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# Generate PDF
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report_filename = (
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f"report_{report_request.patient_name.replace(' ', '_')}_{session_id}.pdf"
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)
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report_path = REPORTS_DIR / report_filename
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html_string_to_pdf(html_doc, str(report_path))
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return ReportResponse(
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message="Report generated successfully",
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report_path=str(report_path),
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graphs_generated=graphs_generated,
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analysis_data=analysis_data,
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)
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except Exception as e:
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raise HTTPException(
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status_code=500, detail=f"Error generating report: {str(e)}"
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)
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@app.get("/download-report/{filename}")
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async def download_report(filename: str):
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"""
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Download a generated report.
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Args:
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filename: Name of the report file
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Returns:
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PDF file
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"""
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file_path = REPORTS_DIR / filename
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if not file_path.exists():
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raise HTTPException(status_code=404, detail="Report not found")
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return FileResponse(
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path=file_path,
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media_type="application/pdf",
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filename=filename,
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)
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@app.delete("/uploads/{session_id}")
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async def delete_session_uploads(session_id: str):
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"""
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Delete all uploaded files for a session.
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||||
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||||
Args:
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||||
session_id: Session identifier
|
||||
|
||||
Returns:
|
||||
Success message
|
||||
"""
|
||||
if session_id not in uploaded_files_store:
|
||||
raise HTTPException(status_code=404, detail="Session not found")
|
||||
|
||||
# Delete files
|
||||
session_dir = UPLOAD_DIR / session_id
|
||||
if session_dir.exists():
|
||||
shutil.rmtree(session_dir)
|
||||
|
||||
# Remove from store
|
||||
del uploaded_files_store[session_id]
|
||||
|
||||
return {"message": f"Session '{session_id}' deleted successfully"}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
+193
@@ -0,0 +1,193 @@
|
||||
"""
|
||||
FastAPI application for medical report generation.
|
||||
|
||||
This API provides a single endpoint that accepts all required files
|
||||
and patient information, then generates a comprehensive medical report.
|
||||
"""
|
||||
|
||||
import shutil
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import FastAPI, File, Form, HTTPException, UploadFile
|
||||
from fastapi.responses import FileResponse
|
||||
from pydantic import BaseModel
|
||||
|
||||
from services.report_generator import ReportGeneratorService
|
||||
|
||||
app = FastAPI(
|
||||
title="Medical Report Generation API",
|
||||
description="API for generating medical performance reports with analysis and graphs",
|
||||
version="2.0.0",
|
||||
)
|
||||
|
||||
# Define output directories
|
||||
GRAPHS_DIR = Path("graphs")
|
||||
GRAPHS_DIR.mkdir(exist_ok=True)
|
||||
|
||||
REPORTS_DIR = Path("reports")
|
||||
REPORTS_DIR.mkdir(exist_ok=True)
|
||||
|
||||
# Initialize report generator service
|
||||
report_service = ReportGeneratorService(
|
||||
template_dir="app/report_gen",
|
||||
graphs_dir=str(GRAPHS_DIR),
|
||||
reports_dir=str(REPORTS_DIR),
|
||||
)
|
||||
|
||||
|
||||
class ReportResponse(BaseModel):
|
||||
message: str
|
||||
report_path: str
|
||||
graphs_generated: list
|
||||
analysis_data: dict
|
||||
|
||||
|
||||
@app.get("/")
|
||||
async def root():
|
||||
"""Root endpoint with API information"""
|
||||
return {
|
||||
"message": "Medical Report Generation API",
|
||||
"version": "2.0.0",
|
||||
"endpoints": {
|
||||
"generate_report": "POST /generate-report",
|
||||
"download_report": "GET /download-report/{filename}",
|
||||
"health": "GET /health",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
"""Health check endpoint"""
|
||||
return {"status": "healthy", "service": "report-generation-api"}
|
||||
|
||||
|
||||
@app.post("/generate-report", response_model=ReportResponse)
|
||||
async def generate_report(
|
||||
patient_name: str = Form(..., description="Patient name"),
|
||||
age: int = Form(..., description="Patient age"),
|
||||
height: str = Form(..., description="Patient height (e.g., 5'4\")"),
|
||||
weight: str = Form(..., description="Patient weight (e.g., 123lbs)"),
|
||||
focus: str = Form(default="Endurance", description="Training focus"),
|
||||
session_id: str = Form(default="default", description="Session ID"),
|
||||
spirometry_pdf: UploadFile = File(..., description="Spirometry PDF file"),
|
||||
pnoe_csv: UploadFile = File(..., description="Pnoe CSV file"),
|
||||
seca_excel: UploadFile = File(..., description="SECA Excel file"),
|
||||
):
|
||||
"""
|
||||
Generate a comprehensive medical report from uploaded files.
|
||||
|
||||
This endpoint accepts all required files and patient information,
|
||||
processes the data, generates graphs, and returns a PDF report.
|
||||
|
||||
Args:
|
||||
spirometry_pdf: Spirometry PDF file
|
||||
pnoe_csv: Pnoe CSV data file
|
||||
seca_excel: SECA body composition Excel file
|
||||
patient_name: Name of the patient
|
||||
age: Patient age
|
||||
height: Patient height
|
||||
weight: Patient weight
|
||||
focus: Training focus (default: Endurance)
|
||||
session_id: Session identifier (default: default)
|
||||
|
||||
Returns:
|
||||
ReportResponse with report path, graphs generated, and analysis data
|
||||
"""
|
||||
# Validate file types
|
||||
if not spirometry_pdf.filename.endswith(".pdf"):
|
||||
raise HTTPException(status_code=400, detail="Spirometry file must be a PDF")
|
||||
|
||||
if not pnoe_csv.filename.endswith(".csv"):
|
||||
raise HTTPException(status_code=400, detail="Pnoe file must be a CSV")
|
||||
|
||||
if not seca_excel.filename.endswith((".xlsx", ".xls")):
|
||||
raise HTTPException(
|
||||
status_code=400, detail="SECA file must be an Excel file (.xlsx or .xls)"
|
||||
)
|
||||
|
||||
# Create temporary directory for uploaded files
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
temp_path = Path(temp_dir)
|
||||
|
||||
# Save uploaded files temporarily
|
||||
spirometry_path = temp_path / f"spirometry_{spirometry_pdf.filename}"
|
||||
pnoe_path = temp_path / f"pnoe_{pnoe_csv.filename}"
|
||||
seca_path = temp_path / f"seca_{seca_excel.filename}"
|
||||
|
||||
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)
|
||||
|
||||
# Prepare patient information
|
||||
patient_info = {
|
||||
"patient_name": patient_name,
|
||||
"age": age,
|
||||
"height": height,
|
||||
"weight": weight,
|
||||
"focus": focus,
|
||||
"session_id": session_id,
|
||||
}
|
||||
|
||||
# Generate report using the service
|
||||
result = 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,
|
||||
)
|
||||
|
||||
return ReportResponse(
|
||||
message="Report generated successfully",
|
||||
report_path=result["report_path"],
|
||||
graphs_generated=result["graphs_generated"],
|
||||
analysis_data=result["analysis_data"],
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Error generating report: {str(e)}",
|
||||
)
|
||||
finally:
|
||||
# Close file handles
|
||||
spirometry_pdf.file.close()
|
||||
pnoe_csv.file.close()
|
||||
seca_excel.file.close()
|
||||
|
||||
|
||||
@app.get("/download-report/{filename}")
|
||||
async def download_report(filename: str):
|
||||
"""
|
||||
Download a generated report.
|
||||
|
||||
Args:
|
||||
filename: Name of the report file
|
||||
|
||||
Returns:
|
||||
PDF file
|
||||
"""
|
||||
file_path = REPORTS_DIR / filename
|
||||
|
||||
if not file_path.exists():
|
||||
raise HTTPException(status_code=404, detail="Report not found")
|
||||
|
||||
return FileResponse(
|
||||
path=file_path,
|
||||
media_type="application/pdf",
|
||||
filename=filename,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
@@ -1,124 +0,0 @@
|
||||
from jinja2 import Environment, FileSystemLoader
|
||||
from playwright.sync_api import sync_playwright
|
||||
|
||||
from context import context_list
|
||||
|
||||
env = Environment(loader=FileSystemLoader("report_gen"))
|
||||
|
||||
html_pages = []
|
||||
|
||||
header_context = {
|
||||
"patient_name": "Keirstyn Moran",
|
||||
"age": 34,
|
||||
"height": "5'4\"",
|
||||
"weight": "123lbs",
|
||||
"focus": "Endurance",
|
||||
}
|
||||
|
||||
footer_context = [
|
||||
{
|
||||
"contact_email": "info@ishplabs.com ",
|
||||
"website": "www.ishplabs.com",
|
||||
"social": "@ishplabs",
|
||||
"page_number": i + 1,
|
||||
}
|
||||
for i in range(len(context_list))
|
||||
]
|
||||
|
||||
|
||||
header_html = env.get_template("header.html").render(header_context)
|
||||
footer_html_list = [
|
||||
env.get_template("footer.html").render(context) for context in footer_context
|
||||
]
|
||||
|
||||
for i, context in enumerate(context_list):
|
||||
template = env.get_template(f"page_{i + 1}.html").render(context)
|
||||
|
||||
if (i + 1) > 2:
|
||||
full_html = f"""
|
||||
<div class="page flex flex-col justify-between">
|
||||
<div>
|
||||
{header_html}
|
||||
</div>
|
||||
<main class="flex-grow p-4">
|
||||
{template}
|
||||
</main>
|
||||
<div class="border-t text-center text-sm text-gray-600">
|
||||
{footer_html_list[i]}
|
||||
</div>
|
||||
</div>
|
||||
"""
|
||||
html_pages.append(full_html)
|
||||
else:
|
||||
html_pages.append(template)
|
||||
|
||||
# Combine with page breaks
|
||||
final_html = "<div class='page-break'></div>".join(html_pages)
|
||||
# Wrap in full HTML document
|
||||
html_doc = f"""
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<link href="https://cdn.jsdelivr.net/npm/tailwindcss/dist/tailwind.min.css" rel="stylesheet">
|
||||
<style>
|
||||
html, body {{
|
||||
height: 100%;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}}
|
||||
.page-break {{ page-break-after: always; }}
|
||||
.page {{
|
||||
height: 100vh;
|
||||
min-height: 100vh;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}}
|
||||
.page main {{
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
}}
|
||||
/* Reset margins and padding everywhere */
|
||||
* {{
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
box-sizing: border-box;
|
||||
}}
|
||||
/* Prevent images from being too large */
|
||||
img {{
|
||||
max-height: 300px;
|
||||
}}
|
||||
/* Larger images for specific charts */
|
||||
.chart-large {{
|
||||
max-height: 500px !important;
|
||||
}}
|
||||
</style>
|
||||
</head>
|
||||
<body class="m-0 p-0">
|
||||
{final_html}
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
|
||||
# Generate PDF
|
||||
|
||||
|
||||
def html_string_to_pdf(html_content, pdf_path):
|
||||
with sync_playwright() as p:
|
||||
browser = p.chromium.launch()
|
||||
page = browser.new_page()
|
||||
|
||||
# Set the HTML directly
|
||||
page.set_content(html_content)
|
||||
|
||||
# Export to PDF
|
||||
page.pdf(path=pdf_path, format="A4", print_background=True)
|
||||
|
||||
browser.close()
|
||||
|
||||
|
||||
html_string_to_pdf(html_doc, "multi_page_report.pdf")
|
||||
# pdfkit.from_string(html_doc, "truth_report.pdf", options=options)
|
||||
|
||||
print("✅ PDF generated: multi_page_report.pdf")
|
||||
@@ -0,0 +1,318 @@
|
||||
"""
|
||||
Report Generator Service
|
||||
|
||||
This service handles the generation of medical reports from uploaded files.
|
||||
It processes data, generates graphs, and creates PDF reports.
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import pandas as pd
|
||||
from jinja2 import Environment, FileSystemLoader
|
||||
from playwright.sync_api import sync_playwright
|
||||
|
||||
from app.services.context import context_list
|
||||
from app.services.graph_generator import GraphGenerator
|
||||
|
||||
|
||||
class ReportGeneratorService:
|
||||
"""Service for generating medical performance reports"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
template_dir: str = "app/report_gen",
|
||||
graphs_dir: str = "graphs",
|
||||
reports_dir: str = "reports",
|
||||
):
|
||||
"""
|
||||
Initialize the report generator service.
|
||||
|
||||
Args:
|
||||
template_dir: Directory containing Jinja2 templates
|
||||
graphs_dir: Directory to save generated graphs
|
||||
reports_dir: Directory to save generated reports
|
||||
"""
|
||||
self.template_dir = template_dir
|
||||
self.graphs_dir = Path(graphs_dir)
|
||||
self.reports_dir = Path(reports_dir)
|
||||
self.graph_generator = GraphGenerator(charts_dir=str(graphs_dir))
|
||||
self.env = Environment(loader=FileSystemLoader(template_dir))
|
||||
|
||||
# Ensure directories exist
|
||||
self.graphs_dir.mkdir(exist_ok=True)
|
||||
self.reports_dir.mkdir(exist_ok=True)
|
||||
|
||||
def process_pnoe_data(self, pnoe_csv_path: str) -> pd.DataFrame:
|
||||
"""
|
||||
Load and process Pnoe CSV data.
|
||||
|
||||
Args:
|
||||
pnoe_csv_path: Path to Pnoe CSV file
|
||||
|
||||
Returns:
|
||||
Processed DataFrame with smoothed columns
|
||||
"""
|
||||
# Load data
|
||||
df = pd.read_csv(pnoe_csv_path, delimiter=";")
|
||||
df = df.apply(pd.to_numeric, errors="ignore")
|
||||
|
||||
# Calculate derived columns
|
||||
df["VO2 Pulse"] = df["VO2(ml/min)"] / df["HR(bpm)"]
|
||||
df["VO2 Breath"] = df["VO2(ml/min)"] / df["BF(bpm)"]
|
||||
df["CHO"] = df["EE(kcal/min)"] * df["CARBS(%)"] / 100
|
||||
df["FAT"] = df["EE(kcal/min)"] * df["FAT(%)"] / 100
|
||||
|
||||
# Smooth columns
|
||||
window_size = 10
|
||||
columns_to_smooth = [
|
||||
"VO2(ml/min)",
|
||||
"VCO2(ml/min)",
|
||||
"HR(bpm)",
|
||||
"VT(l)",
|
||||
"BF(bpm)",
|
||||
"VE(l/min)",
|
||||
"VO2 Pulse",
|
||||
"VO2 Breath",
|
||||
"CHO",
|
||||
"FAT",
|
||||
]
|
||||
|
||||
for col in columns_to_smooth:
|
||||
if col in df.columns:
|
||||
df[f"{col}_smoothed"] = (
|
||||
df[col].rolling(window=window_size, min_periods=1).mean()
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
def generate_graphs(self, df: pd.DataFrame) -> List[Dict[str, str]]:
|
||||
"""
|
||||
Generate all required graphs from processed data.
|
||||
|
||||
Args:
|
||||
df: Processed DataFrame with smoothed columns
|
||||
|
||||
Returns:
|
||||
List of dictionaries containing graph names and paths
|
||||
"""
|
||||
graphs_generated = []
|
||||
|
||||
# List of graphs to generate
|
||||
graph_methods = [
|
||||
("respiratory", self.graph_generator.generate_respiratory_chart),
|
||||
("fuel_utilization", self.graph_generator.generate_fuel_utilization_chart),
|
||||
("vo2_pulse", self.graph_generator.generate_vo2_pulse_chart),
|
||||
("vo2_breath", self.graph_generator.generate_vo2_breath_chart),
|
||||
("fat_metabolism", self.graph_generator.generate_fat_metabolism_chart),
|
||||
("recovery", self.graph_generator.generate_recovery_chart),
|
||||
]
|
||||
|
||||
for name, method in graph_methods:
|
||||
try:
|
||||
path = method(df, save_as_base64=False)
|
||||
graphs_generated.append({"name": name, "path": str(path)})
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not generate {name} chart: {e}")
|
||||
|
||||
return graphs_generated
|
||||
|
||||
def calculate_analysis_metrics(self, df: pd.DataFrame) -> Dict[str, Any]:
|
||||
"""
|
||||
Calculate basic analysis metrics from processed data.
|
||||
|
||||
Args:
|
||||
df: Processed DataFrame with smoothed columns
|
||||
|
||||
Returns:
|
||||
Dictionary containing analysis metrics
|
||||
"""
|
||||
return {
|
||||
"vo2_max": float(df["VO2(ml/min)_smoothed"].max())
|
||||
if "VO2(ml/min)_smoothed" in df.columns
|
||||
else 0,
|
||||
"peak_vt": float(df["VT(l)_smoothed"].max())
|
||||
if "VT(l)_smoothed" in df.columns
|
||||
else 0,
|
||||
"max_hr": float(df["HR(bpm)_smoothed"].max())
|
||||
if "HR(bpm)_smoothed" in df.columns
|
||||
else 0,
|
||||
}
|
||||
|
||||
def generate_html(self, patient_info: Dict[str, Any]) -> str:
|
||||
"""
|
||||
Generate HTML content for the report.
|
||||
|
||||
Args:
|
||||
patient_info: Dictionary containing patient information
|
||||
(patient_name, age, height, weight, focus)
|
||||
|
||||
Returns:
|
||||
Complete HTML document as string
|
||||
"""
|
||||
html_pages = []
|
||||
|
||||
# Header context
|
||||
header_context = {
|
||||
"patient_name": patient_info.get("patient_name", ""),
|
||||
"age": patient_info.get("age", ""),
|
||||
"height": patient_info.get("height", ""),
|
||||
"weight": patient_info.get("weight", ""),
|
||||
"focus": patient_info.get("focus", "Endurance"),
|
||||
}
|
||||
|
||||
# Footer context
|
||||
footer_context = [
|
||||
{
|
||||
"contact_email": "info@ishplabs.com",
|
||||
"website": "www.ishplabs.com",
|
||||
"social": "@ishplabs",
|
||||
"page_number": i + 1,
|
||||
}
|
||||
for i in range(len(context_list))
|
||||
]
|
||||
|
||||
# Render header
|
||||
header_html = self.env.get_template("header.html").render(header_context)
|
||||
|
||||
# Render footers
|
||||
footer_html_list = [
|
||||
self.env.get_template("footer.html").render(context)
|
||||
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)
|
||||
|
||||
if (i + 1) > 2:
|
||||
full_html = f"""
|
||||
<div class="page flex flex-col justify-between">
|
||||
<div>
|
||||
{header_html}
|
||||
</div>
|
||||
<main class="flex-grow p-4">
|
||||
{template}
|
||||
</main>
|
||||
<div class="border-t text-center text-sm text-gray-600">
|
||||
{footer_html_list[i]}
|
||||
</div>
|
||||
</div>
|
||||
"""
|
||||
html_pages.append(full_html)
|
||||
else:
|
||||
html_pages.append(template)
|
||||
|
||||
# Combine with page breaks
|
||||
final_html = "<div class='page-break'></div>".join(html_pages)
|
||||
|
||||
# Wrap in full HTML document
|
||||
html_doc = f"""
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<link href="https://cdn.jsdelivr.net/npm/tailwindcss/dist/tailwind.min.css" rel="stylesheet">
|
||||
<style>
|
||||
html, body {{
|
||||
height: 100%;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}}
|
||||
.page-break {{ page-break-after: always; }}
|
||||
.page {{
|
||||
height: 100vh;
|
||||
min-height: 100vh;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}}
|
||||
.page main {{
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
}}
|
||||
* {{
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
box-sizing: border-box;
|
||||
}}
|
||||
img {{
|
||||
max-height: 300px;
|
||||
}}
|
||||
.chart-large {{
|
||||
max-height: 500px !important;
|
||||
}}
|
||||
</style>
|
||||
</head>
|
||||
<body class="m-0 p-0">
|
||||
{final_html}
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
return html_doc
|
||||
|
||||
def html_to_pdf(self, html_content: str, pdf_path: str) -> None:
|
||||
"""
|
||||
Convert HTML content to PDF file.
|
||||
|
||||
Args:
|
||||
html_content: HTML content as string
|
||||
pdf_path: Path where PDF should be saved
|
||||
"""
|
||||
with sync_playwright() as p:
|
||||
browser = p.chromium.launch()
|
||||
page = browser.new_page()
|
||||
page.set_content(html_content)
|
||||
page.pdf(path=pdf_path, format="A4", print_background=True)
|
||||
browser.close()
|
||||
|
||||
def generate_report(
|
||||
self,
|
||||
spirometry_pdf_path: str,
|
||||
pnoe_csv_path: str,
|
||||
seca_excel_path: str,
|
||||
patient_info: Dict[str, Any],
|
||||
output_filename: str = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate complete medical report from uploaded files.
|
||||
|
||||
Args:
|
||||
spirometry_pdf_path: Path to Spirometry PDF file
|
||||
pnoe_csv_path: Path to Pnoe CSV file
|
||||
seca_excel_path: Path to SECA Excel file
|
||||
patient_info: Dictionary containing patient information
|
||||
output_filename: Optional custom output filename
|
||||
|
||||
Returns:
|
||||
Dictionary containing report path, graphs generated, and analysis data
|
||||
"""
|
||||
# Process data
|
||||
df = self.process_pnoe_data(pnoe_csv_path)
|
||||
|
||||
# Generate graphs
|
||||
graphs_generated = self.generate_graphs(df)
|
||||
|
||||
# Calculate analysis metrics
|
||||
analysis_data = self.calculate_analysis_metrics(df)
|
||||
analysis_data["graphs_count"] = len(graphs_generated)
|
||||
|
||||
# Generate HTML
|
||||
html_content = self.generate_html(patient_info)
|
||||
|
||||
# Generate PDF
|
||||
if output_filename is None:
|
||||
patient_name = patient_info.get("patient_name", "Unknown")
|
||||
session_id = patient_info.get("session_id", "default")
|
||||
output_filename = (
|
||||
f"report_{patient_name.replace(' ', '_')}_{session_id}.pdf"
|
||||
)
|
||||
|
||||
report_path = self.reports_dir / output_filename
|
||||
self.html_to_pdf(html_content, str(report_path))
|
||||
|
||||
return {
|
||||
"report_path": str(report_path),
|
||||
"graphs_generated": graphs_generated,
|
||||
"analysis_data": analysis_data,
|
||||
}
|
||||
@@ -0,0 +1,64 @@
|
||||
import base64
|
||||
import os
|
||||
|
||||
import requests
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
API_KEY_REF = os.getenv("OPENROUTER_API_KEY")
|
||||
|
||||
|
||||
def encode_pdf_to_base64(pdf_path):
|
||||
with open(pdf_path, "rb") as pdf_file:
|
||||
return base64.b64encode(pdf_file.read()).decode("utf-8")
|
||||
|
||||
|
||||
def extract_spirometry_table_from_pdf(pdf_path):
|
||||
url = "https://openrouter.ai/api/v1/chat/completions"
|
||||
headers = {
|
||||
"Authorization": f"Bearer {API_KEY_REF}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
|
||||
# Read and encode the PDF
|
||||
base64_pdf = encode_pdf_to_base64(pdf_path)
|
||||
data_url = f"data:application/pdf;base64,{base64_pdf}"
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "Please extract the Spirometry table from the pdf and return the values in csv format, "
|
||||
"note that it is the unit of parameter that is beside it and it should not be a column. "
|
||||
"The '-' Should be treated as empty values."
|
||||
"do not add 'csv' at the start or end of the response",
|
||||
},
|
||||
{
|
||||
"type": "file",
|
||||
"file": {"filename": "document.pdf", "file_data": data_url},
|
||||
},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
payload = {
|
||||
"model": "google/gemini-2.5-flash-lite",
|
||||
"messages": messages,
|
||||
}
|
||||
|
||||
response = requests.post(url, headers=headers, json=payload)
|
||||
response_data = response.json()
|
||||
|
||||
if "choices" in response_data and len(response_data["choices"]) > 0:
|
||||
content = response_data["choices"][0]["message"]["content"]
|
||||
|
||||
# Save to a CSV file
|
||||
output_file = "extracted_spirometry_table.csv"
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
f.write(content)
|
||||
|
||||
return f"Extracted table saved to {output_file}"
|
||||
else:
|
||||
return "No content found in response"
|
||||
@@ -0,0 +1,318 @@
|
||||
"""
|
||||
Report Generator Service
|
||||
|
||||
This service handles the generation of medical reports from uploaded files.
|
||||
It processes data, generates graphs, and creates PDF reports.
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import pandas as pd
|
||||
from jinja2 import Environment, FileSystemLoader
|
||||
from playwright.sync_api import sync_playwright
|
||||
|
||||
from app.services.context import context_list
|
||||
from app.services.graph_generator import GraphGenerator
|
||||
|
||||
|
||||
class ReportGeneratorService:
|
||||
"""Service for generating medical performance reports"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
template_dir: str = "app/report_gen",
|
||||
graphs_dir: str = "graphs",
|
||||
reports_dir: str = "reports",
|
||||
):
|
||||
"""
|
||||
Initialize the report generator service.
|
||||
|
||||
Args:
|
||||
template_dir: Directory containing Jinja2 templates
|
||||
graphs_dir: Directory to save generated graphs
|
||||
reports_dir: Directory to save generated reports
|
||||
"""
|
||||
self.template_dir = template_dir
|
||||
self.graphs_dir = Path(graphs_dir)
|
||||
self.reports_dir = Path(reports_dir)
|
||||
self.graph_generator = GraphGenerator(charts_dir=str(graphs_dir))
|
||||
self.env = Environment(loader=FileSystemLoader(template_dir))
|
||||
|
||||
# Ensure directories exist
|
||||
self.graphs_dir.mkdir(exist_ok=True)
|
||||
self.reports_dir.mkdir(exist_ok=True)
|
||||
|
||||
def process_pnoe_data(self, pnoe_csv_path: str) -> pd.DataFrame:
|
||||
"""
|
||||
Load and process Pnoe CSV data.
|
||||
|
||||
Args:
|
||||
pnoe_csv_path: Path to Pnoe CSV file
|
||||
|
||||
Returns:
|
||||
Processed DataFrame with smoothed columns
|
||||
"""
|
||||
# Load data
|
||||
df = pd.read_csv(pnoe_csv_path, delimiter=";")
|
||||
df = df.apply(pd.to_numeric, errors="ignore")
|
||||
|
||||
# Calculate derived columns
|
||||
df["VO2 Pulse"] = df["VO2(ml/min)"] / df["HR(bpm)"]
|
||||
df["VO2 Breath"] = df["VO2(ml/min)"] / df["BF(bpm)"]
|
||||
df["CHO"] = df["EE(kcal/min)"] * df["CARBS(%)"] / 100
|
||||
df["FAT"] = df["EE(kcal/min)"] * df["FAT(%)"] / 100
|
||||
|
||||
# Smooth columns
|
||||
window_size = 10
|
||||
columns_to_smooth = [
|
||||
"VO2(ml/min)",
|
||||
"VCO2(ml/min)",
|
||||
"HR(bpm)",
|
||||
"VT(l)",
|
||||
"BF(bpm)",
|
||||
"VE(l/min)",
|
||||
"VO2 Pulse",
|
||||
"VO2 Breath",
|
||||
"CHO",
|
||||
"FAT",
|
||||
]
|
||||
|
||||
for col in columns_to_smooth:
|
||||
if col in df.columns:
|
||||
df[f"{col}_smoothed"] = (
|
||||
df[col].rolling(window=window_size, min_periods=1).mean()
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
def generate_graphs(self, df: pd.DataFrame) -> List[Dict[str, str]]:
|
||||
"""
|
||||
Generate all required graphs from processed data.
|
||||
|
||||
Args:
|
||||
df: Processed DataFrame with smoothed columns
|
||||
|
||||
Returns:
|
||||
List of dictionaries containing graph names and paths
|
||||
"""
|
||||
graphs_generated = []
|
||||
|
||||
# List of graphs to generate
|
||||
graph_methods = [
|
||||
("respiratory", self.graph_generator.generate_respiratory_chart),
|
||||
("fuel_utilization", self.graph_generator.generate_fuel_utilization_chart),
|
||||
("vo2_pulse", self.graph_generator.generate_vo2_pulse_chart),
|
||||
("vo2_breath", self.graph_generator.generate_vo2_breath_chart),
|
||||
("fat_metabolism", self.graph_generator.generate_fat_metabolism_chart),
|
||||
("recovery", self.graph_generator.generate_recovery_chart),
|
||||
]
|
||||
|
||||
for name, method in graph_methods:
|
||||
try:
|
||||
path = method(df, save_as_base64=False)
|
||||
graphs_generated.append({"name": name, "path": str(path)})
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not generate {name} chart: {e}")
|
||||
|
||||
return graphs_generated
|
||||
|
||||
def calculate_analysis_metrics(self, df: pd.DataFrame) -> Dict[str, Any]:
|
||||
"""
|
||||
Calculate basic analysis metrics from processed data.
|
||||
|
||||
Args:
|
||||
df: Processed DataFrame with smoothed columns
|
||||
|
||||
Returns:
|
||||
Dictionary containing analysis metrics
|
||||
"""
|
||||
return {
|
||||
"vo2_max": float(df["VO2(ml/min)_smoothed"].max())
|
||||
if "VO2(ml/min)_smoothed" in df.columns
|
||||
else 0,
|
||||
"peak_vt": float(df["VT(l)_smoothed"].max())
|
||||
if "VT(l)_smoothed" in df.columns
|
||||
else 0,
|
||||
"max_hr": float(df["HR(bpm)_smoothed"].max())
|
||||
if "HR(bpm)_smoothed" in df.columns
|
||||
else 0,
|
||||
}
|
||||
|
||||
def generate_html(self, patient_info: Dict[str, Any]) -> str:
|
||||
"""
|
||||
Generate HTML content for the report.
|
||||
|
||||
Args:
|
||||
patient_info: Dictionary containing patient information
|
||||
(patient_name, age, height, weight, focus)
|
||||
|
||||
Returns:
|
||||
Complete HTML document as string
|
||||
"""
|
||||
html_pages = []
|
||||
|
||||
# Header context
|
||||
header_context = {
|
||||
"patient_name": patient_info.get("patient_name", ""),
|
||||
"age": patient_info.get("age", ""),
|
||||
"height": patient_info.get("height", ""),
|
||||
"weight": patient_info.get("weight", ""),
|
||||
"focus": patient_info.get("focus", "Endurance"),
|
||||
}
|
||||
|
||||
# Footer context
|
||||
footer_context = [
|
||||
{
|
||||
"contact_email": "info@ishplabs.com",
|
||||
"website": "www.ishplabs.com",
|
||||
"social": "@ishplabs",
|
||||
"page_number": i + 1,
|
||||
}
|
||||
for i in range(len(context_list))
|
||||
]
|
||||
|
||||
# Render header
|
||||
header_html = self.env.get_template("header.html").render(header_context)
|
||||
|
||||
# Render footers
|
||||
footer_html_list = [
|
||||
self.env.get_template("footer.html").render(context)
|
||||
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)
|
||||
|
||||
if (i + 1) > 2:
|
||||
full_html = f"""
|
||||
<div class="page flex flex-col justify-between">
|
||||
<div>
|
||||
{header_html}
|
||||
</div>
|
||||
<main class="flex-grow p-4">
|
||||
{template}
|
||||
</main>
|
||||
<div class="border-t text-center text-sm text-gray-600">
|
||||
{footer_html_list[i]}
|
||||
</div>
|
||||
</div>
|
||||
"""
|
||||
html_pages.append(full_html)
|
||||
else:
|
||||
html_pages.append(template)
|
||||
|
||||
# Combine with page breaks
|
||||
final_html = "<div class='page-break'></div>".join(html_pages)
|
||||
|
||||
# Wrap in full HTML document
|
||||
html_doc = f"""
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<link href="https://cdn.jsdelivr.net/npm/tailwindcss/dist/tailwind.min.css" rel="stylesheet">
|
||||
<style>
|
||||
html, body {{
|
||||
height: 100%;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}}
|
||||
.page-break {{ page-break-after: always; }}
|
||||
.page {{
|
||||
height: 100vh;
|
||||
min-height: 100vh;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}}
|
||||
.page main {{
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
}}
|
||||
* {{
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
box-sizing: border-box;
|
||||
}}
|
||||
img {{
|
||||
max-height: 300px;
|
||||
}}
|
||||
.chart-large {{
|
||||
max-height: 500px !important;
|
||||
}}
|
||||
</style>
|
||||
</head>
|
||||
<body class="m-0 p-0">
|
||||
{final_html}
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
return html_doc
|
||||
|
||||
def html_to_pdf(self, html_content: str, pdf_path: str) -> None:
|
||||
"""
|
||||
Convert HTML content to PDF file.
|
||||
|
||||
Args:
|
||||
html_content: HTML content as string
|
||||
pdf_path: Path where PDF should be saved
|
||||
"""
|
||||
with sync_playwright() as p:
|
||||
browser = p.chromium.launch()
|
||||
page = browser.new_page()
|
||||
page.set_content(html_content)
|
||||
page.pdf(path=pdf_path, format="A4", print_background=True)
|
||||
browser.close()
|
||||
|
||||
def generate_report(
|
||||
self,
|
||||
spirometry_pdf_path: str,
|
||||
pnoe_csv_path: str,
|
||||
seca_excel_path: str,
|
||||
patient_info: Dict[str, Any],
|
||||
output_filename: str = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate complete medical report from uploaded files.
|
||||
|
||||
Args:
|
||||
spirometry_pdf_path: Path to Spirometry PDF file
|
||||
pnoe_csv_path: Path to Pnoe CSV file
|
||||
seca_excel_path: Path to SECA Excel file
|
||||
patient_info: Dictionary containing patient information
|
||||
output_filename: Optional custom output filename
|
||||
|
||||
Returns:
|
||||
Dictionary containing report path, graphs generated, and analysis data
|
||||
"""
|
||||
# Process data
|
||||
df = self.process_pnoe_data(pnoe_csv_path)
|
||||
|
||||
# Generate graphs
|
||||
graphs_generated = self.generate_graphs(df)
|
||||
|
||||
# Calculate analysis metrics
|
||||
analysis_data = self.calculate_analysis_metrics(df)
|
||||
analysis_data["graphs_count"] = len(graphs_generated)
|
||||
|
||||
# Generate HTML
|
||||
html_content = self.generate_html(patient_info)
|
||||
|
||||
# Generate PDF
|
||||
if output_filename is None:
|
||||
patient_name = patient_info.get("patient_name", "Unknown")
|
||||
session_id = patient_info.get("session_id", "default")
|
||||
output_filename = (
|
||||
f"report_{patient_name.replace(' ', '_')}_{session_id}.pdf"
|
||||
)
|
||||
|
||||
report_path = self.reports_dir / output_filename
|
||||
self.html_to_pdf(html_content, str(report_path))
|
||||
|
||||
return {
|
||||
"report_path": str(report_path),
|
||||
"graphs_generated": graphs_generated,
|
||||
"analysis_data": analysis_data,
|
||||
}
|
||||
Reference in New Issue
Block a user