Nahrát soubory do „hatespeechapp“

hatespeechapp/app.py

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+
+
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import json
+
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from flask_caching import Cache
+
+
+import hashlib
+
+import time
+
+
+
+app = Flask(__name__)
+CORS(app)
+
+app.config['CACHE_TYPE'] = 'SimpleCache' 
+cache = Cache(app)
+
+
+
+
+
+
+
+
+
+
+model_path = "./hate_speech_model/final_model"
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+model.eval()
+
+
+def generate_text_hash(text):
+    return hashlib.md5(text.encode('utf-8')).hexdigest()
+
+@app.route("/api/predict", methods=["POST"])
+def predict():
+    try:
+        data = request.json
+        text = data.get("text", "")
+
+        text_hash = generate_text_hash(text)
+        cached_result = cache.get(text_hash)
+        if cached_result:
+            return jsonify({"prediction": cached_result}), 200
+
+
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+            predictions = torch.argmax(outputs.logits, dim=1).item()
+        
+        prediction_label = "Pravdepodobne toxický" if predictions == 1 else "Neutrálny text"
+        
+        cache.set(text_hash, prediction_label)
+        
+        
+
+
+        response = app.response_class(
+            response=json.dumps({"prediction": prediction_label}, ensure_ascii=False),
+            status=200,
+            mimetype="application/json"
+        )
+        
+        
+
+
+        return response
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+def run_flask():
+    app.run(host="127.0.0.1", port=5000, threaded=True)

hatespeechapp/gui.py

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+import customtkinter as ctk
+from PIL import ImageEnhance, ImageFilter,Image
+import requests
+import tkinter as tk
+
+
+
+
+
+# ---------- APP CONFIG ----------
+ctk.set_appearance_mode("dark")
+ctk.set_default_color_theme("dark-blue")
+
+app = ctk.CTk()
+
+app.geometry("1000x650")
+app.minsize(400, 400)
+app.title("Detektor nenávistného jazyka")
+app.configure(bg="transparent") 
+
+# ---------- BACKGROUND ----------
+bg_image_pil = Image.open("images/image.png").convert("RGB")
+bg_image_pil = bg_image_pil.filter(ImageFilter.GaussianBlur(radius=3))  # мягкий блюр
+bg_image_pil = ImageEnhance.Brightness(bg_image_pil).enhance(0.6)   
+
+bg_image = ctk.CTkImage(light_image=bg_image_pil, dark_image=bg_image_pil, size=(1920, 1080))
+
+
+bg_label = ctk.CTkLabel(app, image=bg_image, text="")
+bg_label.place(relx=0, rely=0, relwidth=1, relheight=1)
+
+# ---------- HEADER ----------#1a1a1a
+header_frame = ctk.CTkFrame(app, fg_color="transparent")
+header_frame.pack(side="top", fill="x")
+
+title = ctk.CTkLabel(
+    header_frame,
+    text="Detektor nenávistného jazyka",
+    font=ctk.CTkFont(size=26, weight="bold"),
+    text_color="white"
+)
+title.pack(pady=10)
+
+# ---------- MAIN CARD ----------
+#1a1a1a
+
+#main_card_frame = ctk.CTkFrame(app, fg_color="transparent")
+main_card_frame = ctk.CTkFrame(app, fg_color="#1a1a1a", corner_radius=25)
+main_card_frame.place(relx=0.5, rely=0.5, anchor="center", relwidth=0.9)
+
+
+subtitle = tk.Label(
+    master=main_card_frame,
+    text="Analyzujte text na nenávistný jazyk",
+    font=("Arial", 28, "bold"),
+    bg="#1a1a1a",
+    fg="white",
+    wraplength=700,
+    justify="center"
+)
+subtitle.pack(fill="x", padx=20)
+
+description = tk.Label(
+    master=main_card_frame,
+    text="Tento nástroj využíva umelú inteligenciu na identifikáciu toxického obsahu v textoch.\n"
+         "Stačí zadať text a zistiť, či obsahuje nenávistný jazyk.",
+    font=("Arial", 14),
+    bg="#1a1a1a",
+    fg="white",
+    justify="center",
+    wraplength=600
+)
+description.pack(fill="x", padx=20)
+
+input_box = ctk.CTkTextbox(main_card_frame, height=100, corner_radius=15)
+input_box.pack(fill="x", padx=20)
+
+send_btn = ctk.CTkButton(main_card_frame, text="📤 Analyzovať", command=lambda: send_request(), width=150, height=40)
+send_btn.pack(pady=(10, 30))
+
+# ---------- FOOTER ----------
+footer_frame = ctk.CTkFrame(app, fg_color="transparent", corner_radius=20)
+footer_frame.pack(side="bottom", fill="x")
+
+footer = ctk.CTkLabel(footer_frame, text="Created by Tetiana Mohorian", font=ctk.CTkFont(size=12), text_color="white")
+footer.pack(pady=8)
+
+# ---------- ADAPTIVE PADDING ----------
+def adapt_padding(event=None):
+    h = app.winfo_height()
+    w = app.winfo_width()
+    scale = h / 650
+
+    # Размеры шрифтов в зависимости от ширины окна
+    if w >= 900:
+        desc_font_size = 30
+        subtitle_font_size = 38
+    elif w >= 700:
+        subtitle_font_size = 32
+        desc_font_size = 20
+    elif w >= 500:
+        subtitle_font_size = 26
+        desc_font_size = 20
+    else:
+        subtitle_font_size = 18
+        desc_font_size = 16
+        
+   
+
+    subtitle.configure(font=("Arial", subtitle_font_size, "bold"))
+    description.configure(font=("Arial", desc_font_size))
+
+    # Отступы
+    subtitle.pack_configure(pady=(int(20 * scale), int(10 * scale)))
+    description.pack_configure(pady=(0, int(20 * scale)))
+    input_box.pack_configure(pady=(0, int(20 * scale)))
+    send_btn.pack_configure(pady=(int(10 * scale), int(30 * scale)))
+
+    def update_wrap():
+        wrap = max(int(w * 0.9), 250)
+        subtitle.configure(wraplength=wrap)
+        description.configure(wraplength=wrap)
+
+    app.after(30, update_wrap)
+
+app.bind("<Configure>", adapt_padding)
+
+# ---------- SEND REQUEST ----------
+def send_request():
+    text = input_box.get("1.0", "end-1c").strip()
+    if not text:
+        subtitle.configure(text="Zadajte text", fg="red")
+        description.configure(text="")
+        return
+
+    try:
+        response = requests.post(
+            "http://localhost:5000/api/predict",
+            json={"text": text}
+        )
+        if response.status_code == 200:
+            result = response.json()["prediction"]
+            subtitle.configure(
+                text=result,
+                fg=("orange" if "toxický" in result.lower() else "white")
+            )
+            description.configure(text=f'Váš text bol: "{text}"')
+        else:
+            subtitle.configure(text="Chyba na serveri", fg="orange")
+            description.configure(text="")
+    except Exception as e:
+        subtitle.configure(text=f"Chyba: {e}", fg="red")
+        description.configure(text="")
+
+# ---------- START ----------
+app.mainloop()
+
+

hatespeechapp/run_all.py

@@ -0,0 +1,7 @@
+import subprocess
+
+# Запуск Flask API (если у тебя есть backend)
+subprocess.Popen(["python", "app.py"])
+
+# Запуск GUI
+subprocess.run(["python", "gui.py"])

hatespeechapp/run_all.spec

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+# -*- mode: python ; coding: utf-8 -*-
+
+
+a = Analysis(
+    ['run_all.py'],
+    pathex=[],
+    binaries=[],
+    datas=[],
+    hiddenimports=[],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+    optimize=0,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.datas,
+    [],
+    name='run_all',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)