Delete new.py

new.py

@@ -1,354 +0,0 @@
-#!/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 sojka
-MODEL_PATH = os.getenv("MODEL_PATH", "AndromedaPL/sojka")
-TOKENIZER_PATH = os.getenv("TOKENIZER_PATH", "sdadas/mmlw-roberta-base") 
-
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-LABELS = ["self-harm", "hate", "vulgar", "sex", "crime"]
-MAX_SEQ_LENGTH = 512
-
-
-HF_TOKEN = os.getenv('HF_TOKEN')
-
-# 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, tokenizer_path: str, device: str) -> Tuple[AutoModelForSequenceClassification, AutoTokenizer]:
-    """Load the trained model and tokenizer"""
-    logger.info(f"Loading tokenizer from {tokenizer_path}")
-
-    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, use_fast=True)
-    logger.info(f"Tokenizer loaded: {tokenizer.name_or_path}")
-
-    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"
-
-    logger.info(f"Loading model from {model_path}")
-
-    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.")
-        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, TOKENIZER_PATH, DEVICE)
-    model_loaded = True
-except Exception as e:
-    logger.error(f"FATAL: Failed to load the model from {MODEL_PATH} or tokenizer from {TOKENIZER_PATH}: {e}", 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)
-        # Using sigmoid for multi-label classification outputs
-        probabilities = torch.sigmoid(outputs.logits)
-        predicted_values = probabilities.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:
-        highest_unsafe_category = max(unsafe_categories, key=unsafe_categories.get)
-        verdict = f"⚠️ Wykryto potencjalnie szkodliwe treści\n: {highest_unsafe_category.upper()}"
-    
-    return verdict, predictions
-
-# --- Gradio Interface ---
-
-theme = gr.themes.Default(
-    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://sojka.m31ai.pl/sojka.png"
-
-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="https://sojka.m31ai.pl/projekt.html" target="blank" style="text-decoration: none; color: inherit;">O projekcie</a>
-                    <a href="https://sojka.m31ai.pl/kategorie.html" target="blank" style="text-decoration: none; color: inherit;">Opis kategorii</a>
-                    <button id="test-sojka-btn" 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=2): # Increased scale for more width
-            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);">
-                Przetestuj <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..."
-            )
-
-            # Define outputs first so they can be referenced by gr.Examples
-            # They will be placed in the layout in the nested Row below
-            output_verdict = gr.Label(label="Wynik analizy", value="Czekam na tekst do analizy...")
-            output_scores = gr.Label(label="Szczegółowe wyniki", visible=False)
-            
-            # Examples are now placed above the button
-            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=lambda text: analyze_and_update(text), # Use a lambda to match fn signature
-                cache_examples=False,
-            )
-
-            # A nested Row for the button and the outputs
-            with gr.Row():
-                submit_btn = gr.Button("Analizuj tekst", variant="primary", scale=1)
-                with gr.Column(scale=2):
-                    # The output components are defined above, but rendered here.
-                    # Gradio renders components where they are defined.
-                    # To solve this, we will re-declare them, which is not ideal,
-                    # but the simplest way to manage layout and callbacks.
-                    # The previous declarations are now just for the Examples callback.
-                    # Let's clean this up by defining them once.
-                    pass # The components are already in the layout from the definitions above.
-                    # The above comment is slightly incorrect for Gradio's declarative style.
-                    # The final working solution is to define components and then have them as outputs.
-                    # Let's revert to a cleaner structure that works.
-                    
-        # 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, width=200)
-    
-    # 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)
-
-    # The click function is now tied to the button defined in the nested Row
-    submit_btn.click(
-        fn=analyze_and_update,
-        inputs=[input_text],
-        outputs=[output_verdict, output_scores]
-    )
-
-# Final corrected and working version of the interface layout
-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="https://sojka.m31ai.pl/projekt.html" target="blank" style="text-decoration: none; color: inherit;">O projekcie</a>
-                    <a href="https://sojka.m31ai.pl/kategorie.html" target="blank" style="text-decoration: none; color: inherit;">Opis kategorii</a>
-                    <button id="test-sojka-btn" 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():
-        # Left column for controls and description
-        with gr.Column(scale=2):
-            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);">
-                Przetestuj <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..."
-            )
-            
-            # Note: Output components are defined in the right column
-            # We will define Examples after the right column is created.
-
-        # Right column for the image and RESULTS
-        with gr.Column(scale=1):
-            gr.Image(JAY_IMAGE_URL, label="Ilustracja sójki", show_label=False, show_download_button=False, container=False, width=200)
-            gr.Markdown("---") # Separator
-            output_verdict = gr.Label(label="Wynik analizy", value="Czekam na tekst do analizy...")
-            output_scores = gr.Label(label="Szczegółowe wyniki", visible=False)
-
-    # Interactive elements are defined last, after all components are created
-    # This places the examples above the button
-    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,
-    )
-    
-    submit_btn = gr.Button("Analizuj tekst", variant="primary")
-
-    # Define actions
-    def analyze_and_update(text):
-        verdict, scores = gradio_predict(text)
-        return verdict, gr.update(value=scores, visible=True)
-
-    submit_btn.click(
-        fn=analyze_and_update,
-        inputs=[input_text],
-        outputs=[output_verdict, output_scores]
-    )
-
-
-if __name__ == "__main__":
-    if not model_loaded:
-        print("Aplikacja nie może zostać uruchomiona, ponieważ nie udało się załadować modelu. Sprawdź logi błędów.")
-    else:
-        # The final, corrected demo object is launched
-        demo.launch()