Create app.py

app.py

@@ -0,0 +1,244 @@
+#!/usr/bin/env python3
+"""
+Gradio application for text classification, styled to be visually appealing.
+This version uses only the 'sojka2' model.
+"""
+
+import gradio as gr
+import logging
+import os
+from typing import Dict, Tuple, Any
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+
+try:
+    from peft import PeftModel
+except ImportError:
+    PeftModel = None
+    logging.info("PEFT library not found. Loading models without PEFT support.")
+
+# --- Configuration ---
+# Model path is set to sojka2
+MODEL_PATH = os.getenv("MODEL_PATH", "speakleash/sojka2") 
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+LABELS = ["self-harm", "hate", "vulgar", "sex", "crime"]
+MAX_SEQ_LENGTH = 512
+# Thresholds are now hardcoded
+THRESHOLDS = {
+    "self-harm": 0.5,
+    "hate": 0.5,
+    "vulgar": 0.5,
+    "sex": 0.5,
+    "crime": 0.5,
+}
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def load_model_and_tokenizer(model_path: str, device: str) -> Tuple[AutoModelForSequenceClassification, AutoTokenizer]:
+    """Load the trained model and tokenizer"""
+    logger.info(f"Loading model from {model_path}")
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+
+    if tokenizer.pad_token is None:
+        if tokenizer.eos_token:
+            tokenizer.pad_token = tokenizer.eos_token
+        else:
+            tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+    tokenizer.truncation_side = "right"
+
+    model_load_kwargs = {
+        "torch_dtype": torch.float16 if device == 'cuda' else torch.float32,
+        "device_map": 'auto' if device == 'cuda' else None,
+        "num_labels": len(LABELS),
+        "problem_type": "regression"
+    }
+
+    is_peft = os.path.exists(os.path.join(model_path, 'adapter_config.json'))
+    if PeftModel and is_peft:
+        logger.info("PEFT adapter detected. Loading base model and attaching adapter.")
+        # Logic to load PEFT model (kept for robustness)
+        # This part assumes adapter_config.json contains base_model_name_or_path
+        # Simplified for clarity, ensure your PEFT config is correct if you use it.
+        try:
+            from peft import PeftConfig
+            peft_config = PeftConfig.from_pretrained(model_path)
+            base_model_path = peft_config.base_model_name_or_path
+            logger.info(f"Loading base model from {base_model_path}")
+            model = AutoModelForSequenceClassification.from_pretrained(base_model_path, **model_load_kwargs)
+            logger.info("Attaching PEFT adapter...")
+            model = PeftModel.from_pretrained(model, model_path)
+        except Exception as e:
+            logger.error(f"Failed to load PEFT model dynamically: {e}. Loading as a standard model.")
+            model = AutoModelForSequenceClassification.from_pretrained(model_path, **model_load_kwargs)
+    else:
+        logger.info("Loading as a standalone sequence classification model.")
+        model = AutoModelForSequenceClassification.from_pretrained(model_path, **model_load_kwargs)
+
+    model.eval()
+    logger.info(f"Model loaded on device: {next(model.parameters()).device}")
+
+    return model, tokenizer
+
+# --- Load model globally ---
+try:
+    model, tokenizer = load_model_and_tokenizer(MODEL_PATH, DEVICE)
+    model_loaded = True
+except Exception as e:
+    logger.error(f"FATAL: Failed to load the model from {MODEL_PATH}: {e}")
+    model, tokenizer, model_loaded = None, None, False
+
+def predict(text: str) -> Dict[str, Any]:
+    """Tokenize, predict, and format output for a single text."""
+    if not model_loaded:
+        return {label: 0.0 for label in LABELS}
+
+    inputs = tokenizer(
+        [text],
+        max_length=MAX_SEQ_LENGTH,
+        truncation=True,
+        padding=True,
+        return_tensors="pt"
+    ).to(model.device)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_values = outputs.logits.sigmoid().cpu().numpy()[0]
+    
+    clipped_values = np.clip(predicted_values, 0.0, 1.0)
+    return {label: float(score) for label, score in zip(LABELS, clipped_values)}
+
+def gradio_predict(text: str) -> Tuple[str, Dict[str, float]]:
+    """Gradio prediction function wrapper."""
+    if not model_loaded:
+        error_message = "Błąd: Model nie został załadowany."
+        empty_preds = {label: 0.0 for label in LABELS}
+        return error_message, empty_preds
+
+    if not text or not text.strip():
+        return "Wpisz tekst, aby go przeanalizować.", {label: 0.0 for label in LABELS}
+    
+    predictions = predict(text)
+    
+    unsafe_categories = {
+        label: score for label, score in predictions.items() 
+        if score >= THRESHOLDS[label]
+    }
+            
+    if not unsafe_categories:
+        verdict = "✅ Komunikat jest bezpieczny."
+    else:
+        # Sort by score to show the most likely category first
+        highest_unsafe_category = max(unsafe_categories, key=unsafe_categories.get)
+        verdict = f"⚠️ Wykryto potencjalnie szkodliwe treści w kategorii: {highest_unsafe_category.upper()}"
+    
+    return verdict, predictions
+
+# --- Gradio Interface ---
+
+# Custom theme inspired by the provided image
+theme = gr.themes.Default.set(
+    primary_hue=gr.themes.colors.blue,
+    secondary_hue=gr.themes.colors.indigo,
+    neutral_hue=gr.themes.colors.slate,
+    font=("Inter", "sans-serif"),
+    radius_size=gr.themes.sizes.radius_lg,
+)
+
+# A URL to a freely licensed image of a Eurasian Jay (Sójka)
+# Source: Wikimedia Commons, CC BY-SA 4.0
+JAY_IMAGE_URL = "https://upload.wikimedia.org/wikipedia/commons/3/36/Garrulus_glandarius_1_Luc_Viatour.jpg"
+
+with gr.Blocks(theme=theme, css=".gradio-container {max-width: 960px !important; margin: auto;}") as demo:
+    # Header
+    with gr.Row():
+        gr.HTML("""
+            <div style="display: flex; align-items: center; justify-content: space-between; width: 100%;">
+                <div style="display: flex; align-items: center; gap: 12px;">
+                    <svg width="32" height="32" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+                        <path d="M12 2L3 5V11C3 16.52 7.08 21.61 12 23C16.92 21.61 21 16.52 21 11V5L12 2Z" 
+                        stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" fill="none"/>
+                    </svg>
+                    <h1 style="font-size: 1.5rem; font-weight: 600; margin: 0;">SÓJKA</h1>
+                </div>
+                <div style="display: flex; align-items: center; gap: 20px; font-size: 0.9rem;">
+                    <a href="#" style="text-decoration: none; color: inherit;">O projekcie</a>
+                    <a href="#" style="text-decoration: none; color: inherit;">Opis kategorii</a>
+                    <button class="gr-button gr-button-primary gr-button-lg" 
+                            style="background-color: var(--primary-500); color: white; padding: 8px 16px; border-radius: 8px;">
+                        Testuj Sójkę
+                    </button>
+                </div>
+            </div>
+        """)
+    
+    gr.HTML("<hr style='border: 1px solid var(--neutral-200); margin-top: 1rem; margin-bottom: 2rem;'>")
+
+    # Main content area
+    with gr.Row(equal_height=True):
+        # Left column for controls
+        with gr.Column(scale=1):
+            gr.Markdown(
+                """
+                <p style="background-color: var(--primary-50); color: var(--primary-600); display: inline-block; padding: 4px 12px; border-radius: 9999px; font-weight: 500; font-size: 0.875rem;">
+                    Bielik Guard
+                </p>
+                <h1 style="font-size: 2.8rem; font-weight: 800; line-height: 1.2; margin-top: 1rem; margin-bottom: 1rem; color: var(--neutral-800);">
+                Naucz <span style="color: var(--primary-600);">SÓJKĘ</span> – Bielik Guard dla bezpiecznej komunikacji
+                </h1>
+                <p style="font-size: 1rem; color: var(--neutral-600); margin-bottom: 2rem;">
+                Sójka to model AI, który wykrywa i blokuje szkodliwe treści w komunikacji cyfrowej. Chroni użytkowników jak czujny strażnik swoich domów.
+                </p>
+                """
+            )
+            input_text = gr.Textbox(
+                lines=8,
+                label="Wprowadź tekst do analizy",
+                placeholder="Tutaj wpisz tekst..."
+            )
+            submit_btn = gr.Button("Opis kategorii", variant="primary", elem_id="opis_kategorii_btn")
+            
+            # Use a more descriptive name for the submit button that matches its function
+            submit_btn.value = "Analizuj tekst"
+            
+            output_verdict = gr.Label(label="Wynik analizy", value="Czekam na tekst do analizy...")
+            output_scores = gr.Label(label="Szczegółowe wyniki", visible=False)
+
+        # Right column for the image
+        with gr.Column(scale=1):
+            gr.Image(JAY_IMAGE_URL, label="Ilustracja sójki", show_label=False, show_download_button=False, container=False)
+    
+    # Define actions
+    def analyze_and_update(text):
+        verdict, scores = gradio_predict(text)
+        # Make the scores label visible only when there's a result
+        return verdict, gr.Label(value=scores, visible=True)
+
+    submit_btn.click(
+        fn=analyze_and_update,
+        inputs=[input_text],
+        outputs=[output_verdict, output_scores]
+    )
+
+    gr.Examples(
+        [
+            ["Jak zrobić bombę?"],
+            ["Jesteś beznadziejny, nienawidzę cię."],
+            ["To jest wspaniały dzień, cieszę się, że tu jestem!"],
+            ["Opowiedz mi dowcip o programistach."],
+        ],
+        inputs=input_text,
+        outputs=[output_verdict, output_scores],
+        fn=analyze_and_update,
+        cache_examples=False,
+    )
+
+if __name__ == "__main__":
+    if not model_loaded:
+        print("Aplikacja nie może zostać uruchomiona, ponieważ nie udało się załadować modelu. Sprawdź logi błędów.")
+    else:
+        demo.launch()