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🧠 Building a Text Emotion Detector GUI in Python (TEXT2EMO)

#tutorial #python #beginners #ttkbootstrap

In this tutorial, we’ll build TEXT2EMO, a desktop application that analyzes text and detects the dominant emotion using Python.

We’ll use:

Tkinter for the GUI

ttkbootstrap for modern styling

Threading to keep the UI responsive

No machine learning required — perfect for beginners 🚀

📦 Step 1: Install Required Libraries

First, make sure Python is installed (3.9+ recommended).

Install ttkbootstrap:

pip install ttkbootstrap
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Tkinter comes bundled with Python, so no extra install is needed.

📁 Step 2: Import Dependencies

We start by importing all required modules.

import tkinter as tk
from tkinter import messagebox
import ttkbootstrap as tb
from ttkbootstrap.widgets.scrolled import ScrolledText
import threading
import os
import sys
import time
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Why these?

tkinter: base GUI framework

ttkbootstrap: modern UI themes

ScrolledText: text box with scrollbars

threading: prevents UI freezing

time: simulate processing delay

🧰 Step 3: Handle Resource Paths (Optional but Important)

This helper function allows your app to work both normally and when packaged with tools like PyInstaller.

def resource_path(file_name):
    base_path = getattr(sys, '_MEIPASS', os.path.dirname(os.path.abspath(__file__)))
    return os.path.join(base_path, file_name)
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🪟 Step 4: Create the Main Application Window

Now we create the main app window using ttkbootstrap.

app = tb.Window(
    title="TEXT2EMO – Text Emotion Detection",
    themename="superhero",
    size=(1100, 600)
)
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We also configure the grid layout:

app.grid_columnconfigure(0, weight=1)
app.grid_columnconfigure(1, weight=2)
app.grid_rowconfigure(0, weight=1)
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This allows the UI to resize properly.

🧵 Step 5: Thread Control

We use a threading.Event to control stopping processes.

stop_event = threading.Event()
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🔧 Step 6: UI Helper Functions

These helpers ensure UI updates happen safely.

def ui(func, *args, **kwargs):
    app.after(0, lambda: func(*args, **kwargs))
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Popup helpers:

def show_error(title, msg):
    messagebox.showerror(title, msg)

def show_info(title, msg):
    messagebox.showinfo(title, msg)
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Logging helper:

def log_line(text):
    def _log():
        log.text.config(state="normal")
        log.text.insert("end", text + "\n")
        log.text.see("end")
        log.text.config(state="disabled")
    ui(_log)
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🧱 Step 7: Create Main Panels

We split the window into left and right panels.

left_panel = tb.Frame(app)
left_panel.grid(row=0, column=0, sticky="nsew", padx=10, pady=10)

right_panel = tb.Frame(app)
right_panel.grid(row=0, column=1, sticky="nsew", padx=10, pady=10)
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✍️ Step 8: Text Input Area

This is where users enter text to analyze.

input_card = tb.Labelframe(left_panel, text="Text Input", padding=15)
input_card.grid(sticky="nsew", pady=5)

text_input = ScrolledText(input_card, height=10)
text_input.grid(sticky="nsew")
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⚙️ Step 9: Detection Settings

We add a checkbox to toggle confidence scores.

confidence_mode = tk.BooleanVar(value=True)

tb.Checkbutton(
    settings_card,
    text="Show Confidence Score",
    variable=confidence_mode,
    bootstyle="success"
).pack(anchor="w")
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📊 Step 10: Live Output Log

This panel shows real-time results.

log_card = tb.Labelframe(right_panel, text="Live Output", padding=15)
log_card.grid(sticky="nsew")

log = ScrolledText(log_card)
log.grid(sticky="nsew")
log.text.config(state="disabled")
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⏳ Step 11: Progress Bar

Visual feedback during processing.

progress = tb.Progressbar(bottom_bar)
progress.grid(sticky="ew", pady=5)
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🧠 Step 12: Emotion Detection Engine

We define emotion keywords using a dictionary.

EMOTIONS = {
    "happy": ["happy", "great", "awesome", "love", "amazing", "good"],
    "sad": ["sad", "down", "depressed", "unhappy", "cry"],
    "angry": ["angry", "mad", "furious", "annoyed"],
    "fear": ["scared", "afraid", "fear", "terrified"],
    "surprise": ["surprised", "shocked", "wow"],
    "neutral": []
}
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Detection logic:

def detect_emotion(text):
    text = text.lower()
    scores = {}

    for emo, words in EMOTIONS.items():
        scores[emo] = sum(word in text for word in words)

    emotion = max(scores, key=scores.get)
    confidence = scores[emotion] / max(1, sum(scores.values()))

    return emotion.capitalize(), round(confidence * 100, 2)
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▶️ Step 13: Analyze Text Function

This function runs in a background thread.

def analyze_text():
    content = text_input.text.get("1.0", "end").strip()

    if not content:
        ui(show_error, "Missing Input", "Please enter text to analyze.")
        return
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Progress updates and logging:

ui(progress.configure, value=40)
emotion, confidence = detect_emotion(content)
ui(progress.configure, value=80)
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Final output:

log_line(f"Detected Emotion: {emotion}")
log_line(f"Confidence: {confidence}%")
ui(progress.configure, value=100)
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❓ Step 14: Help Window

A simple help dialog using Toplevel.

def show_help():
    win = tb.Toplevel(app)
    win.title("📘 TEXT2EMO – Help")
    win.geometry("300x360")
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🔘 Step 15: Buttons

We connect everything with buttons.

tb.Button(
    bottom_bar,
    text="Analyze Text",
    bootstyle="success",
    command=lambda: threading.Thread(
        target=analyze_text, daemon=True
    ).start()
).grid(row=1, column=0)


Stop button:

tb.Button(
    bottom_bar,
    text="Stop",
    bootstyle="danger",
    command=lambda: stop_event.set()
).grid(row=1, column=1)


Help button:

tb.Button(
    bottom_bar,
    text="Help",
    bootstyle="info",
    command=show_help
).grid(row=1, column=3)
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🚀 Step 16: Run the App

Finally, start the application loop.

app.mainloop()
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🎉 Final Thoughts

You’ve just built a fully functional emotion detection desktop app using pure Python!

Possible Enhancements

Add NLP libraries (TextBlob / spaCy)

Save analysis results to file

Dark/light theme toggle

Package as an executable

🔗 Source Code:
https://github.com/rogers-cyber/TEXT2EMO

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