import tkinter as tk
from tkinter import filedialog, ttk
import pandas as pd
import os

def load_and_analyze_file():
    file_path = filedialog.askopenfilename(title="Select DICOM Discovery Output File", filetypes=[("Text Files", "*.txt")])
    if not file_path:
        return

    df = pd.read_csv(file_path, delimiter='|', dtype=str).fillna('')

    # Normalize modalities to upper case and flatten list-like strings
    def normalize_modalities(val):
        if val.startswith("[") and val.endswith("]"):
            try:
                return ", ".join(eval(val))
            except:
                return val
        return val

    df['ModalitiesInStudy'] = df['ModalitiesInStudy'].apply(normalize_modalities)

    # Prepare metrics
    stats = {
        "Distinct StudyInstanceUIDs": df['StudyInstanceUID'].nunique(),
        "Distinct AccessionNumbers": df['AccessionNumber'].nunique(),
        "AccessionNumbers with 2 StudyInstanceUIDs": (df.groupby('AccessionNumber')['StudyInstanceUID'].nunique() == 2).sum(),
        "AccessionNumbers with 3 StudyInstanceUIDs": (df.groupby('AccessionNumber')['StudyInstanceUID'].nunique() == 3).sum(),
        "AccessionNumbers with >3 StudyInstanceUIDs": (df.groupby('AccessionNumber')['StudyInstanceUID'].nunique() > 3).sum(),
        "Missing AccessionNumbers": (df['AccessionNumber'] == '').sum(),
        "Missing StudyDescriptions": (df['StudyDescription'] == '').sum(),
        "*QA* in PatientID": df['PatientID'].str.contains('QA', case=False, na=False).sum(),
        "*QA* in StudyDescription": df['StudyDescription'].str.contains('QA', case=False, na=False).sum(),
        "*QA* in PatientName": df['PatientName'].str.contains('QA', case=False, na=False).sum(),
        "*TEST* in PatientID": df['PatientID'].str.contains('TEST', case=False, na=False).sum(),
        "*TEST* in StudyDescription": df['StudyDescription'].str.contains('TEST', case=False, na=False).sum(),
        "*TEST* in PatientName": df['PatientName'].str.contains('TEST', case=False, na=False).sum()
    }

    # Modality breakdown
    modality_counts = {
        "CR": 0, "DX": 0, "CT": 0, "MR": 0, "NM": 0, "US": 0, "XA": 0, "PR": 0, "SC": 0, "OT": 0, "Other": 0
    }

    for val in df['ModalitiesInStudy']:
        for modality in [m.strip().upper() for m in val.split(',') if m.strip()]:
            if modality in modality_counts:
                modality_counts[modality] += 1
            else:
                modality_counts["Other"] += 1

    # Display results
    output_window = tk.Toplevel(root)
    output_window.title("DICOM Discovery Analysis")

    row = 0
    for key, val in stats.items():
        tk.Label(output_window, text=f"{key}:").grid(row=row, column=0, sticky='w', padx=5, pady=2)
        tk.Label(output_window, text=f"{val}").grid(row=row, column=1, sticky='w', padx=5, pady=2)
        row += 1

    tk.Label(output_window, text="Modality Breakdown:").grid(row=row, column=0, sticky='w', padx=5, pady=10)
    row += 1
    for modality, count in modality_counts.items():
        tk.Label(output_window, text=f"{modality}:").grid(row=row, column=0, sticky='w', padx=20, pady=2)
        tk.Label(output_window, text=f"{count}").grid(row=row, column=1, sticky='w', padx=5, pady=2)
        row += 1

# GUI setup
root = tk.Tk()
root.title("DICOM Discovery Analyzer")

frame = tk.Frame(root, padx=10, pady=10)
frame.pack()

ttk.Button(frame, text="Select and Analyze Output File", command=load_and_analyze_file).pack()

root.mainloop()