Update app.py

app.py

@@ -23,7 +23,6 @@ except ImportError:
 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,
@@ -61,9 +60,6 @@ def load_model_and_tokenizer(model_path: str, device: str) -> Tuple[AutoModelFor
     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)
@@ -107,7 +103,9 @@ def predict(text: str) -> Dict[str, Any]:
 
     with torch.no_grad():
         outputs = model(**inputs)
-        predicted_values = outputs.logits.sigmoid().cpu().numpy()[0]
+        # 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)}
@@ -132,7 +130,6 @@ def gradio_predict(text: str) -> Tuple[str, Dict[str, float]]:
     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()}"
     
@@ -140,8 +137,8 @@ def gradio_predict(text: str) -> Tuple[str, Dict[str, float]]:
 
 # --- Gradio Interface ---
 
-# Custom theme inspired by the provided image
-theme = gr.themes.Default.set(
+# Custom theme inspired by the provided image - THIS IS THE CORRECTED LINE
+theme = gr.themes.Default(
     primary_hue=gr.themes.colors.blue,
     secondary_hue=gr.themes.colors.indigo,
     neutral_hue=gr.themes.colors.slate,
@@ -168,7 +165,7 @@ with gr.Blocks(theme=theme, css=".gradio-container {max-width: 960px !important;
                 <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" 
+                    <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>
@@ -200,10 +197,7 @@ with gr.Blocks(theme=theme, css=".gradio-container {max-width: 960px !important;
                 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"
+            submit_btn = gr.Button("Analizuj tekst", variant="primary")
             
             output_verdict = gr.Label(label="Wynik analizy", value="Czekam na tekst do analizy...")
             output_scores = gr.Label(label="Szczegółowe wyniki", visible=False)