File size: 4,206 Bytes
e93bfbd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import os
import questionary
from rich.console import Console
from rich.panel import Panel
from rich.table import Table
import capture
from optimize import (
clear_memory,
get_device,
get_memory_usage,
infer_blip,
infer_fusion,
set_cpu_threads,
)
console = Console()
def run_vision(image_path):
console.print("[cyan]Running Vision analysis...[/cyan]")
try:
caption = infer_blip(image_path)
except Exception as e:
console.print(f"[red]Error: {e}[/red]")
return
console.print()
console.print(Panel(f"[bold green]{caption}[/bold green]", title="Generated Radiology Caption"))
return caption
def run_symptom_check(image_path):
symptoms = questionary.text("Enter patient symptoms / clinical indication:").ask()
if not symptoms:
symptoms = "No symptoms provided"
try:
diagnosis, confidence = infer_fusion(image_path, symptoms)
except Exception as e:
console.print(f"[red]Error: {e}[/red]")
return
if diagnosis is None:
console.print(f"[red]{confidence}[/red]")
return
table = Table(title="Diagnosis Result")
table.add_column("Prediction", style="cyan")
table.add_column("Confidence", style="green")
table.add_row(diagnosis, f"{confidence:.1%}")
console.print(table)
if confidence < 0.75:
console.print("[yellow]Low confidence — consider follow-up.[/yellow]")
return diagnosis, confidence
def show_status():
from training import info as training_info
console.print("[bold cyan]--- System Status ---[/bold cyan]")
mem = get_memory_usage()
console.print(f"Device: [green]{get_device().upper()}[/green]")
console.print(f"Process RAM: {mem['rss_mb']:.0f} MB")
console.print()
console.print("[bold cyan]--- Fusion Model Data ---[/bold cyan]")
training_info()
console.print()
cap_dir = capture.IMAGES_DIR
count = len(list(cap_dir.glob("*"))) if cap_dir.exists() else 0
console.print(f"Captured images: {count} in {cap_dir}")
def install_deps():
deps = []
try:
import cv2
except ImportError:
deps.append("opencv-python")
try:
import pydicom
except ImportError:
deps.append("pydicom")
try:
import nltk
except ImportError:
deps.append("nltk")
if not deps:
console.print("[green]All optional dependencies are already installed.[/green]")
return
import subprocess
import sys
console.print(f"[yellow]Installing: {' '.join(deps)}[/yellow]")
subprocess.check_call([sys.executable, "-m", "pip", "install", *deps])
console.print("[green]Done.[/green]")
def main():
set_cpu_threads()
console.print(Panel.fit("[bold cyan]MedicalAI - Light Weight[/bold cyan]"))
console.print()
while True:
choice = questionary.select(
"What would you like to do?",
choices=[
"Vision — Generate report from X-ray",
"Symptom Check — Diagnose from X-ray + symptoms",
"System Status & Data Info",
"Install optional deps (camera, DICOM, BLEU)",
"Exit",
],
pointer=">",
).ask()
if choice == "Exit":
console.print("[bold red]Exiting...[/bold red]")
clear_memory()
break
if choice == "Install optional deps (camera, DICOM, BLEU)":
install_deps()
continue
if choice == "System Status & Data Info":
show_status()
console.print()
continue
image_path, msg = capture.pick_image()
if image_path is None:
console.print(f"[red]{msg}[/red]")
continue
console.print(f"[dim]{msg}[/dim]")
if choice.startswith("Vision"):
run_vision(image_path)
elif choice.startswith("Symptom Check"):
run_symptom_check(image_path)
clear_memory()
console.print()
again = questionary.confirm("Do another?").ask()
if not again:
break
clear_memory()
if __name__ == "__main__":
main()
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