src/backend/service.py

import os
import io
import sys
import json
import base64
import certifi
import requests
from PIL import Image
from bson import ObjectId
from gridfs import GridFS
from pymongo import MongoClient
from fastapi import FastAPI, File, UploadFile, HTTPException, Form
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
import uvicorn
from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter, A4
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
from reportlab.lib.units import inch
from datetime import datetime
import hashlib
import random
import string

API_URL = "https://n0bcq3t2emgrp7ip.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
    "Accept": "application/json",
    "Authorization": f"Bearer {os.getenv('HF_TOKEN')}",
    "Content-Type": "application/json" 
}

reportsaved= []


# Disable verbose tracebacks
sys.tracebacklimit = 0

# Configure Python path
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(parent_dir)

def get_reports_with_empty_findings():
    reportsaved = []
    try:
        # Use environment variable for MongoDB connection string
        mongo_uri = os.getenv('MONGODB_URI', 
            "mongodb://localhost:27017/radiologyDB"  # Fallback to local MongoDB
        )
        client = MongoClient(
            mongo_uri,
            tlsCAFile=certifi.where(),
            serverSelectionTimeoutMS=5000
        )
        
        db = client.radiologyDB
        reports = [str(doc["_id"]) for doc in db.reports.find({"findings": ""})]
        reportsaved = reports

        # ✅ First do all processing (analyze, reconstruct, etc.)
        for report_id in reportsaved:
            analyze(report_id)  # this includes image reconstruction and update

        # ✅ Then print JSON as final stdout response
        print(json.dumps({
            "success": True,
            "count": len(reportsaved),
            "reports": reportsaved
        }), flush=True)

    except Exception as e:
        print(json.dumps({
            "success": False,
            "error": str(e),
            "count": 0,
            "reports": []
        }), flush=True)
    finally:
        if 'client' in locals():
            client.close()


def update_findings(report_id, new_findings_text):
    """Updates findings for a specific report"""
    try:
        mongo_uri = os.getenv('MONGODB_URI', 
            "mongodb://localhost:27017/radiologyDB"  # Fallback to local MongoDB
        )
        client = MongoClient(
            mongo_uri,
            tlsCAFile=certifi.where()
        )
        db = client.radiologyDB

        result = db.reports.update_one(
            {"_id": ObjectId(report_id)},
            {"$set": {"findings": new_findings_text.strip()}}
        )

        return {
            "success": result.modified_count > 0,
            "modified_count": result.modified_count,
            "message": "Updated" if result.modified_count else "No document found"
        }

    except Exception as e:
        return {
            "success": False,
            "error": str(e),
            "message": f"Error: {str(e)}"
        }
    finally:
        if 'client' in locals():
            client.close()


def reconstruct_image_if_not_exists(report_id):
    try:
        mongo_uri = os.getenv('MONGODB_URI', 
            "mongodb://localhost:27017/radiologyDB"  # Fallback to local MongoDB
        )
        client = MongoClient(
            mongo_uri,
            tlsCAFile=certifi.where()
        )

        db = client["radiologyDB"]
        fs = GridFS(db, collection="images")  # ✅ Correct bucket


        report = db.reports.find_one({"_id": ObjectId(report_id)})
        if not report:
            print(f"❌ Report not found for ID: {report_id}", file=sys.stderr)
            return

        if "images" not in report or len(report["images"]) == 0:
            print(f"❌ No image ID found in report: {report_id}", file=sys.stderr)
            return

        image_id = report["images"][0]  # Assuming one image per report

        output_dir = os.path.join(os.path.dirname(__file__), "images")
        os.makedirs(output_dir, exist_ok=True)

        output_path = os.path.join(output_dir, f"{image_id}.jpg")

        # Check if image already exists
        if os.path.exists(output_path):
            print(f"✅ Image already exists: {output_path}", file=sys.stderr)
            return

        # Get the file from GridFS
        grid_out = fs.get(ObjectId(image_id))
        with open(output_path, "wb") as f:
            f.write(grid_out.read())

        print(f"✅ Reconstructed and saved image: {output_path}", file=sys.stderr)


    except Exception as e:
        print(f"❌ Error reconstructing image: {e}", file=sys.stderr)

    finally:
        if 'client' in locals():
            client.close()

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    print(f"Hugging Face API Response: {response.text}", file=sys.stderr)
    return response.json()

def analyze_image(image_path: str):
    """
    Analyze medical image using Hugging Face API.
    Takes an image path and a prompt, then returns AI-generated findings.
    """
    try:
        # Load the image using PIL
        image = Image.open(image_path)
        
        # Convert image to base64 string
        buffer = io.BytesIO()
        image.save(buffer, format="PNG")
        img_str = base64.b64encode(buffer.getvalue()).decode("utf-8")
        
        # Send to Hugging Face API
        output = query({
            "inputs": img_str,
        })
        
        return output
        
    except Exception as e:
        # Fallback response in case of API errors
        return f"Error analyzing image: {str(e)}. Using fallback analysis."

def analyze(id):
    """
    Analyze medical images using Hugging Face API for findings extraction.
    """
    try:
        # First reconstruct the image
        reconstruct_image_if_not_exists(id)
        
        # Get the path to the reconstructed image
        image_path = os.path.join(os.path.dirname(__file__), "images", f"{id}.jpg")
        
        # Analyze the image using Hugging Face API
        findings = analyze_image(image_path)
        
        # If we got a valid response, update the findings
        if isinstance(findings, str):
            template_response = findings
        else:
            # Format the API response into a readable string (no prefix)
            template_response = json.dumps(findings, indent=2)
        
        # Update the findings in the database
        update_findings(id, template_response)
        
        return template_response
        
    except Exception as e:
        error_msg = f"Error during analysis: {str(e)}"
        print(error_msg, file=sys.stderr)
        return error_msg

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Mount static files directory
# Mount static files directory - removed since using remote image
# app.mount("/static", StaticFiles(directory=os.path.dirname(__file__)), name="static")

@app.get("/health")
async def health_check():
    """Health check endpoint"""
    return {"status": "healthy", "service": "radiology-backend"}

@app.get("/")
async def root():
    """Root endpoint with service info"""
    return {
        "service": "MediVision Radiology Backend",
        "version": "1.0.0",
        "status": "running",
        "endpoints": {
            "analyze": "/analyze/",
            "health": "/health"
        }
    }

@app.post("/analyze/")
async def analyze_endpoint(file: UploadFile = File(...)):
    """
    Accepts an uploaded image file and returns AI-generated findings.
    """
    try:
        # Validate file type
        if not file.content_type.startswith('image/'):
            return {"error": "Invalid file type. Please upload an image file.", "findings": "File type error"}
        
        contents = await file.read()
        if len(contents) == 0:
            return {"error": "Empty file uploaded.", "findings": "Empty file error"}
            
        # Process image
        image = Image.open(io.BytesIO(contents))
        
        # Convert to RGB if necessary
        if image.mode != 'RGB':
            image = image.convert('RGB')
        
        # Resize image if too large (optional optimization)
        max_size = (1024, 1024)
        if image.size[0] > max_size[0] or image.size[1] > max_size[1]:
            image.thumbnail(max_size, Image.Resampling.LANCZOS)
        
        buffer = io.BytesIO()
        image.save(buffer, format="PNG", optimize=True)
        img_str = base64.b64encode(buffer.getvalue()).decode("utf-8")
        
        # Get response from Hugging Face API
        print(f"Sending image to Hugging Face API (size: {len(img_str)} chars)", file=sys.stderr)
        output = query({"inputs": img_str})
        
        # Enhanced response processing
        findings = ""
        if isinstance(output, dict):
            if "error" in output:
                findings = f"API Error: {output['error']}"
            elif "generated_text" in output:
                findings = output["generated_text"]
            else:
                findings = str(output)
        elif isinstance(output, list) and len(output) > 0:
            first_result = output[0]
            if isinstance(first_result, dict) and "generated_text" in first_result:
                findings = first_result["generated_text"]
            else:
                findings = str(first_result)
        elif isinstance(output, str):
            findings = output
        else:
            findings = f"Unexpected response format: {type(output)}"
        
        print(f"Analysis complete. Findings length: {len(findings)} chars", file=sys.stderr)
        
        # Return formatted findings
        return {"findings": findings, "status": "success"}
        
    except Exception as e:
        error_msg = f"Error in analyze_endpoint: {str(e)}"
        print(error_msg, file=sys.stderr)
        return {"error": error_msg, "findings": "Error analyzing image", "status": "error"}

def generate_pdf_report(patient_name, patient_info, findings):
    """Generate a PDF report for the patient with optimized layout"""
    try:
        # Create reports directory if it doesn't exist
        reports_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "pdf_reports")
        os.makedirs(reports_dir, exist_ok=True)
        
        # Create filename
        safe_name = "".join(c for c in patient_name if c.isalnum() or c in (' ', '-', '_')).rstrip()
        pdf_filename = f"{safe_name}_report.pdf"
        pdf_path = os.path.join(reports_dir, pdf_filename)
        
        # Create PDF document with optimized margins
        doc = SimpleDocTemplate(pdf_path, pagesize=A4, 
                              topMargin=0.8*inch, bottomMargin=0.6*inch,
                              leftMargin=0.8*inch, rightMargin=0.8*inch)
        styles = getSampleStyleSheet()
        story = []
        
        # Custom styles for better space utilization
        compact_style = styles['Normal'].clone('CompactStyle')
        compact_style.fontSize = 10
        compact_style.leading = 12
        compact_style.spaceAfter = 6
        
        header_style = styles['Heading1'].clone('CompactHeader')
        header_style.fontSize = 16
        header_style.alignment = 1  # Center
        header_style.spaceAfter = 8
        
        section_style = styles['Heading2'].clone('CompactSection')
        section_style.fontSize = 12
        section_style.spaceAfter = 4
        section_style.spaceBefore = 8
        
        # Compact header
        story.append(Paragraph("🔬 <b>MediVision Radiology Center</b>", header_style))
        story.append(Paragraph("<i>AI-Powered Medical Imaging Analysis</i>", compact_style))
        story.append(Spacer(1, 0.2*inch))
        
        # Patient Information in compact table format
        story.append(Paragraph("<b>RADIOLOGY REPORT</b>", section_style))
        
        # Create a compact patient info section
        current_date = datetime.now().strftime("%m/%d/%Y")
        current_time = datetime.now().strftime("%H:%M")
        
        patient_info_compact = f"""
        <b>Patient:</b> {patient_info.get('name', 'N/A')} &nbsp;&nbsp;&nbsp; 
        <b>MRN:</b> {patient_info.get('medical_record_number', 'N/A')} &nbsp;&nbsp;&nbsp;
        <b>DOB:</b> {patient_info.get('date_of_birth', 'N/A')}<br/>
        <b>Gender:</b> {patient_info.get('gender', 'N/A')} &nbsp;&nbsp;&nbsp;
        <b>Study Date:</b> {patient_info.get('date_of_study', current_date)} &nbsp;&nbsp;&nbsp;
        <b>Physician:</b> Dr. {patient_info.get('referring_physician', 'N/A')}<br/>
        <b>Report Date:</b> {current_date} {current_time} &nbsp;&nbsp;&nbsp;
        <b>Radiologist:</b> MediVision AI
        """
        story.append(Paragraph(patient_info_compact, compact_style))
        story.append(Spacer(1, 0.15*inch))
        
        # Process findings to remove duplicates and optimize content
        if "answer:" in findings.lower():
            parts = findings.split("answer:", 1)
            if len(parts) == 2:
                findings_text = parts[1].strip()
            else:
                findings_text = findings
        else:
            findings_text = findings
        
        # Clean up findings - remove redundant phrases
        findings_text = findings_text.replace("Brief Structured Report:", "")
        findings_text = findings_text.replace("The chest X-ray is a useful tool for diagnosing conditions like pneumonia, lung cancer, or other lung diseases.", "")
        
        # Format findings with better structure
        story.append(Paragraph("<b>FINDINGS & IMPRESSION:</b>", section_style))
        
        # Split findings into structured sections if possible
        lines = findings_text.strip().split('\n')
        formatted_findings = ""
        
        for line in lines:
            line = line.strip()
            if line:
                # Check if it's a numbered item or bullet point
                if line.startswith(('1.', '2.', '3.', '4.', '5.')):
                    formatted_findings += f"<b>{line}</b><br/>"
                elif line.upper().startswith(('EXAM TYPE:', 'FINDINGS:', 'IMPRESSION:')):
                    formatted_findings += f"<b>{line}</b><br/>"
                else:
                    formatted_findings += f"{line}<br/>"
        
        if not formatted_findings.strip():
            formatted_findings = findings_text.replace('\n', '<br/>')
        
        story.append(Paragraph(formatted_findings, compact_style))
        story.append(Spacer(1, 0.15*inch))
        
        # Compact footer with essential info only
        footer_text = f"""
        <i>Generated by MediVision AI • {current_date} {current_time} • 
        This AI-generated report requires radiologist review for clinical use.</i>
        """
        footer_style = compact_style.clone('FooterStyle')
        footer_style.fontSize = 8
        footer_style.alignment = 1  # Center
        footer_style.textColor = 'grey'
        
        story.append(Paragraph(footer_text, footer_style))
        
        # Build PDF
        doc.build(story)
        
        return pdf_path
        
    except Exception as e:
        print(f"Error generating PDF: {str(e)}", file=sys.stderr)
        return None

@app.get("/generate-mrn/")
async def generate_mrn_endpoint(patient_name: str, date_of_birth: str):
    """Generate MRN for a patient"""
    try:
        mrn = generate_mrn(patient_name, date_of_birth)
        return {
            "success": True,
            "mrn": mrn,
            "patient_name": patient_name,
            "date_of_birth": date_of_birth
        }
    except Exception as e:
        return {
            "success": False,
            "error": f"Error generating MRN: {str(e)}"
        }

@app.get("/patient/{mrn}")
async def get_patient_by_mrn(mrn: str):
    """Get patient information by MRN"""
    result = search_patient_by_mrn(mrn)
    if result["found"]:
        return result["patient"]
    else:
        raise HTTPException(status_code=404, detail=result.get("message", "Patient not found"))

@app.get("/patients")
async def list_patients():
    """List all patients"""
    result = list_all_patients()
    return result

@app.post("/generate-report/")
async def generate_report_endpoint(
    file: UploadFile = File(...),
    patient_name: str = Form(...),
    medical_record_number: str = Form(""),
    date_of_birth: str = Form(""),
    gender: str = Form(""),
    referring_physician: str = Form(""),
    date_of_study: str = Form("")
):
    """Generate report with image analysis"""
    try:
        # First, analyze the image
        findings = ""
        if file:
            # Validate file type
            if not file.content_type.startswith('image/'):
                return {"error": "Invalid file type", "success": False}
            
            contents = await file.read()
            if len(contents) == 0:
                return {"error": "Empty file", "success": False}
            
            # Process image for analysis
            image = Image.open(io.BytesIO(contents))
            if image.mode != 'RGB':
                image = image.convert('RGB')
            
            # Resize if too large
            max_size = (1024, 1024)
            if image.size[0] > max_size[0] or image.size[1] > max_size[1]:
                image.thumbnail(max_size, Image.Resampling.LANCZOS)
            
            buffer = io.BytesIO()
            image.save(buffer, format="PNG", optimize=True)
            img_str = base64.b64encode(buffer.getvalue()).decode("utf-8")
            
            # Get findings from Hugging Face API
            output = query({"inputs": img_str})
            
            # Process findings
            if isinstance(output, dict):
                if "error" in output:
                    findings = f"API Error: {output['error']}"
                elif "generated_text" in output:
                    findings = output["generated_text"]
                else:
                    findings = str(output)
            elif isinstance(output, list) and len(output) > 0:
                first_result = output[0]
                if isinstance(first_result, dict) and "generated_text" in first_result:
                    findings = first_result["generated_text"]
                else:
                    findings = str(first_result)
            elif isinstance(output, str):
                findings = output
            else:
                findings = f"Unexpected response format: {type(output)}"
        
        # Prepare patient info
        patient_info = {
            "name": patient_name,
            "medical_record_number": medical_record_number,
            "date_of_birth": date_of_birth,
            "gender": gender,
            "referring_physician": referring_physician,
            "date_of_study": date_of_study
        }
        
        # Generate MRN if not provided
        mrn = medical_record_number
        if not mrn or len(mrn) == 0:
            mrn = generate_mrn(patient_name, date_of_birth)
        
        # Save patient record to database
        save_result = save_patient_record(patient_info, findings, mrn)
        
        if not save_result.get("success", False):
            return {
                "success": False,
                "error": "Failed to save patient record"
            }
        
        # Generate PDF report
        pdf_path = generate_pdf_report(patient_name, patient_info, findings)
        
        if pdf_path and os.path.exists(pdf_path):
            return {
                "success": True,
                "message": "Report generated successfully",
                "pdf_filename": os.path.basename(pdf_path),
                "mrn": mrn,
                "findings": findings
            }
        else:
            return {
                "success": False,
                "error": "Failed to generate PDF report"
            }
            
    except Exception as e:
        return {
            "success": False,
            "error": f"Error generating report: {str(e)}"
        }

@app.get("/reports/{patient_name}/pdf")
async def download_pdf_report(patient_name: str):
    """Download PDF report for a patient"""
    try:
        reports_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "pdf_reports")
        safe_name = "".join(c for c in patient_name if c.isalnum() or c in (' ', '-', '_')).rstrip()
        pdf_filename = f"{safe_name}_report.pdf"
        pdf_path = os.path.join(reports_dir, pdf_filename)
        
        if os.path.exists(pdf_path):
            return FileResponse(
                path=pdf_path,
                filename=pdf_filename,
                media_type='application/pdf'
            )
        else:
            raise HTTPException(status_code=404, detail=f"PDF report not found for patient: {patient_name}")
            
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error retrieving PDF: {str(e)}")

@app.get("/reports")
async def list_reports():
    """List all available PDF reports"""
    try:
        reports_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "pdf_reports")
        
        if not os.path.exists(reports_dir):
            return {"reports": []}
        
        pdf_files = [f for f in os.listdir(reports_dir) if f.endswith('.pdf')]
        reports = []
        
        for pdf_file in pdf_files:
            patient_name = pdf_file.replace('_report.pdf', '').replace('_', ' ')
            pdf_path = os.path.join(reports_dir, pdf_file)
            file_stats = os.stat(pdf_path)
            
            reports.append({
                "patient_name": patient_name,
                "filename": pdf_file,
                "created_date": datetime.fromtimestamp(file_stats.st_ctime).strftime('%Y-%m-%d %H:%M:%S'),
                "size": file_stats.st_size
            })
        
        return {"reports": reports}
        
    except Exception as e:
        return {"error": f"Error listing reports: {str(e)}", "reports": []}

def generate_mrn(patient_name, date_of_birth):
    """Generate a unique Medical Record Number (MRN) for a patient"""
    try:
        # Create a hash from patient name and DOB for consistency
        patient_data = f"{patient_name.lower().strip()}{date_of_birth}".encode('utf-8')
        hash_object = hashlib.md5(patient_data)
        hash_hex = hash_object.hexdigest()
        
        # Extract first 8 characters and convert to a medical record format
        # Format: MV-YYYYMMDD-XXXX (MV = MediVision, YYYY = year, MM = month, DD = day, XXXX = hash)
        current_date = datetime.now()
        date_part = current_date.strftime("%Y%m%d")
        hash_part = hash_hex[:4].upper()
        
        mrn = f"MV-{date_part}-{hash_part}"
        return mrn
        
    except Exception as e:
        # Fallback: Generate random MRN
        current_date = datetime.now().strftime("%Y%m%d")
        random_part = ''.join(random.choices(string.ascii_uppercase + string.digits, k=4))
        return f"MV-{current_date}-{random_part}"

def save_patient_record(patient_info, findings, mrn):
    """Save patient record to database with MRN for future searches"""
    try:
        mongo_uri = os.getenv('MONGODB_URI', 
            "mongodb://localhost:27017/radiologyDB"  # Fallback to local MongoDB
        )
        client = MongoClient(
            mongo_uri,
            tlsCAFile=certifi.where(),
            serverSelectionTimeoutMS=5000
        )
        
        db = client.radiologyDB
        
        # Create patient record
        patient_record = {
            "mrn": mrn,
            "patient_name": patient_info.get("name", ""),
            "date_of_birth": patient_info.get("date_of_birth", ""),
            "gender": patient_info.get("gender", ""),
            "referring_physician": patient_info.get("referring_physician", ""),
            "date_of_study": patient_info.get("date_of_study", ""),
            "findings": findings,
            "report_date": datetime.now().isoformat(),
            "status": "completed"
        }
        
        # Check if patient already exists (by MRN)
        existing_patient = db.patient_reports.find_one({"mrn": mrn})
        
        if existing_patient:
            # Update existing record
            result = db.patient_reports.update_one(
                {"mrn": mrn},
                {"$set": patient_record}
            )
            return {"action": "updated", "mrn": mrn, "success": True}
        else:
            # Insert new record
            result = db.patient_reports.insert_one(patient_record)
            return {"action": "created", "mrn": mrn, "success": True}
            
    except Exception as e:
        print(f"Error saving patient record: {str(e)}", file=sys.stderr)
        return {"action": "error", "error": str(e), "success": False}
    finally:
        if 'client' in locals():
            client.close()

def search_patient_by_mrn(mrn):
    """Search for patient records by MRN"""
    try:
        mongo_uri = os.getenv('MONGODB_URI', 
            "mongodb://localhost:27017/radiologyDB"  # Fallback to local MongoDB
        )
        client = MongoClient(
            mongo_uri,
            tlsCAFile=certifi.where(),
            serverSelectionTimeoutMS=5000
        )
        
        db = client.radiologyDB
        
        # Search for patient record
        patient_record = db.patient_reports.find_one({"mrn": mrn})
        
        if patient_record:
            # Convert ObjectId to string for JSON serialization
            patient_record["_id"] = str(patient_record["_id"])
            return {"found": True, "patient": patient_record}
        else:
            return {"found": False, "message": f"No patient found with MRN: {mrn}"}
            
    except Exception as e:
        print(f"Error searching patient: {str(e)}", file=sys.stderr)
        return {"found": False, "error": str(e)}
    finally:
        if 'client' in locals():
            client.close()

def list_all_patients():
    """List all patients in the database"""
    try:
        mongo_uri = os.getenv('MONGODB_URI', 
            "mongodb://localhost:27017/radiologyDB"  # Fallback to local MongoDB
        )
        client = MongoClient(
            mongo_uri,
            tlsCAFile=certifi.where(),
            serverSelectionTimeoutMS=5000
        )
        
        db = client.radiologyDB
        
        # Get all patient records, sorted by report date (newest first)
        patients = list(db.patient_reports.find().sort("report_date", -1))
        
        # Convert ObjectId to string for JSON serialization
        for patient in patients:
            patient["_id"] = str(patient["_id"])
        
        return {"success": True, "patients": patients, "count": len(patients)}
        
    except Exception as e:
        print(f"Error listing patients: {str(e)}", file=sys.stderr)
        return {"success": False, "error": str(e), "patients": []}
    finally:
        if 'client' in locals():
            client.close()

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--get-empty', action='store_true', help='Get empty reports')
    parser.add_argument('--update', nargs=2, metavar=('ID', 'TEXT'), help='Update findings')
    parser.add_argument('--serve', action='store_true', help='Run FastAPI server')

    args = parser.parse_args()
    
    if args.serve:
        uvicorn.run("service:app", host="0.0.0.0", port=8001, reload=True)
        sys.exit(0)
    
    if args.get_empty:
        get_reports_with_empty_findings()  # ✅ DON'T capture or print the return value
        sys.exit(0)
    
    elif args.update:
        report_id, findings_text = args.update
        result = update_findings(report_id, findings_text)
        print(json.dumps(result))
        sys.exit(0)
    
    else:
        print(json.dumps({"error": "No valid command specified"}))
        sys.exit(1)