Map language code to inali_name and update UI label

app.py

@@ -8,6 +8,7 @@ from transformers import WhisperProcessor, AutoConfig, AutoModel, WhisperConfig,
 from transformers.models.whisper.modeling_whisper import WhisperEncoder
 import torch.nn as nn
 import psutil
+import json
 
 # --- CONFIGURATION ---
 MAX_AUDIO_SECONDS = 30
@@ -132,6 +133,16 @@ model.eval()
 # Initialize LabelExtractor to build text mappings
 label_extractor = LabelExtractor(processor.tokenizer)
 
+# Load languages mapping
+print("Loading language mappings...")
+try:
+    with open("languages.json", "r", encoding="utf-8") as f:
+        languages_data = json.load(f)
+    CODE_TO_NAME = {item.get("code"): item.get("inali_name") for item in languages_data if item.get("code") and item.get("inali_name")}
+except Exception as e:
+    print(f"Warning: Could not load languages.json: {e}")
+    CODE_TO_NAME = {}
+
 print("Model loaded successfully!")
 
 def get_mem_usage():
@@ -195,7 +206,10 @@ def predict_language(audio_path):
         # Strip the "<|" and "|>" tags if present for a cleaner UI
         fam_text = label_extractor.family_labels[fam_idx].strip("<|>") if fam_idx < len(label_extractor.family_labels) else f"Unknown Fam ({fam_idx})"
         super_text = label_extractor.super_labels[super_idx].strip("<|>") if super_idx < len(label_extractor.super_labels) else f"Unknown Super ({super_idx})"
-        code_text = label_extractor.code_labels[code_idx].strip("<|>") if code_idx < len(label_extractor.code_labels) else f"Unknown Code ({code_idx})"
+        code_raw = label_extractor.code_labels[code_idx].strip("<|>") if code_idx < len(label_extractor.code_labels) else f"Unknown Code ({code_idx})"
+        
+        # Apply inali_name mapping
+        code_text = f"{CODE_TO_NAME[code_raw]} ({code_raw})" if code_raw in CODE_TO_NAME else code_raw
         
         print(f"[LOG] Final Memory: {get_mem_usage():.2f} MB")
         print(f"--- [LOG] Request Finished ---\n")
@@ -236,7 +250,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo", secondary_hue="blue"))
             gr.Markdown("### 📊 2. Classification Results")
             fam_output = gr.Label(num_top_classes=1, label="🌍 Language Family")
             super_output = gr.Label(num_top_classes=1, label="🗣️ Superlanguage")
-            code_output = gr.Label(num_top_classes=1, label="🔤 Language Code")
+            code_output = gr.Label(num_top_classes=1, label="🔤 Language")
 
     submit_btn.click(
         fn=predict_language,