<jupyter_start><jupyter_text>Generate dummy data  Recognise texts from 2D floor plan images<jupyter_code>class TextRecognition():
    def __init__(self):
        pass
    
    def readFloorPlan(self):
        pass
    
    def getTexts(self):
        pass
class DemoGUI(tk.Frame):
    def __init__(self, parent=None):
        tk.Frame.__init__(self, parent)
        self.pack()
        self.layout()
        textRecognition = TextRecognition()
    
    def layout(self):
        # interactable definitions
        self.frameUploadDrawings = tk.Frame(self)
        self.frameExtractTexts = tk.Frame(self)
        self.btnUploadFile = tk.Button(self.frameUploadDrawings, text="Upload File", command=self.uploadAction)
        self.btnRecogniseTexts = tk.Button(self.frameExtractTexts, text="Recognise Text", command=self.recogniseTexts)
#         TODO: fix label size to display uploaded file thumbnail
        self.labelFileThumbnail = tk.Label(self.frameUploadDrawings, width=30, height=30, relief=tk.GROOVE)

        # interactable layout
        self.frameUploadDrawings.grid(row=0, column=0, sticky=tk.NSEW, padx=10, pady=10)
        self.frameExtractTexts.grid(row=0, column=1, sticky=tk.NSEW, padx=10, pady=10)
        self.btnUploadFile.grid(row=0, column=0, sticky=tk.NSEW)
        self.labelFileThumbnail.grid(row=1, column=0, sticky=tk.NSEW)
        self.btnRecogniseTexts.grid(row=0, column=0, sticky=tk.NSEW)

    def uploadAction(self, event=None):
        filepath = tk.filedialog.askopenfilename()
        image = Image.open(filepath).resize((30, 30), Image.ANTIALIAS)
        photoImage = ImageTk.PhotoImage(image)
        self.labelFileThumbnail.configure(image=photoImage)
        self.labelFileThumbnail.image = photoImage

    def recogniseTexts(event=None):
        textRecognition.getTexts()
        

if __name__ == "__main__":
    root = tk.Tk()
    root.title("Beyond BIM Demo")
    DemoGUI(root).mainloop()
    root.after(5, lambda: root.focus_force())<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11310413552566019, 'fraction_numerical': 0.013951170901843548, 'mean_word_length': 3.355748373101952, 'pattern_counts': {'":': 1, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '350182', 'license': 'MIT', 'n_tokens_mistral': 691, 'n_tokens_neox': 661, 'n_words': 113, 'path': 'prototype/demo.ipynb', 'repo_name': 'yu-mao/BeyondBIM', 'size': '2007'}