modify examples

.gradio/cached_examples/13/log.csv

@@ -0,0 +1,5 @@
+Predicted Dialect,timestamp
+"{""label"": ""Gulf Arabic"", ""confidences"": [{""label"": ""Gulf Arabic"", ""confidence"": 0.9943048357963562}, {""label"": ""Levantine Arabic"", ""confidence"": 0.004683974664658308}, {""label"": ""Maghrebi Arabic"", ""confidence"": 0.0003852946974802762}, {""label"": ""Modern Standard Arabic"", ""confidence"": 0.0003597271570470184}, {""label"": ""Egyptian Arabic"", ""confidence"": 0.0002661938196979463}]}",2025-03-04 14:57:07.478940
+"{""label"": ""Levantine Arabic"", ""confidences"": [{""label"": ""Levantine Arabic"", ""confidence"": 0.8999205827713013}, {""label"": ""Gulf Arabic"", ""confidence"": 0.09826569259166718}, {""label"": ""Maghrebi Arabic"", ""confidence"": 0.001049569109454751}, {""label"": ""Modern Standard Arabic"", ""confidence"": 0.0004323236644268036}, {""label"": ""Egyptian Arabic"", ""confidence"": 0.0003318020317237824}]}",2025-03-04 14:57:32.843399
+"{""label"": ""Gulf Arabic"", ""confidences"": [{""label"": ""Gulf Arabic"", ""confidence"": 0.9867829084396362}, {""label"": ""Levantine Arabic"", ""confidence"": 0.011104526929557323}, {""label"": ""Maghrebi Arabic"", ""confidence"": 0.0016229108441621065}, {""label"": ""Modern Standard Arabic"", ""confidence"": 0.0003496674180496484}, {""label"": ""Egyptian Arabic"", ""confidence"": 0.00014002238458488137}]}",2025-03-04 14:57:54.273625
+"{""label"": ""Levantine Arabic"", ""confidences"": [{""label"": ""Levantine Arabic"", ""confidence"": 0.9568566083908081}, {""label"": ""Gulf Arabic"", ""confidence"": 0.03988657519221306}, {""label"": ""Modern Standard Arabic"", ""confidence"": 0.002475168788805604}, {""label"": ""Egyptian Arabic"", ""confidence"": 0.0006239291978999972}, {""label"": ""Maghrebi Arabic"", ""confidence"": 0.00015768631419632584}]}",2025-03-04 14:58:14.103717

app.py

@@ -1,17 +1,13 @@
 import gradio as gr
 from transformers import pipeline
-import numpy as np
 import os
 
 # Load the model
 print("Loading model...")
 model_id = "badrex/mms-300m-arabic-dialect-identifier"
-try:
-    classifier = pipeline("audio-classification", model=model_id)
-    print("Model loaded successfully")
-except Exception as e:
-    print(f"Error loading model: {e}")
-    
+classifier = pipeline("audio-classification", model=model_id)
+print("Model loaded successfully")
+
 # Define dialect mapping
 dialect_mapping = {
     "MSA": "Modern Standard Arabic",
@@ -22,54 +18,41 @@ dialect_mapping = {
 }
 
 def predict_dialect(audio):
-    try:
-        # The audio input from Gradio is a tuple of (sample_rate, audio_array)
-        if audio is None:
-            return {"Error": 1.0}
-            
-        sr, audio_array = audio
-        
-        # Process the audio input
-        if len(audio_array.shape) > 1:
-            audio_array = audio_array.mean(axis=1)  # Convert stereo to mono
-        
-        print(f"Processing audio: sample rate={sr}, shape={audio_array.shape}")
-        
-        # Classify the dialect
-        predictions = classifier({"sampling_rate": sr, "raw": audio_array})
-        
-        # Format results for display
-        results = {}
-        for pred in predictions:
-            dialect_name = dialect_mapping.get(pred['label'], pred['label'])
-            results[dialect_name] = float(pred['score'])
-        
-        return results
-    except Exception as e:
-        print(f"Error in prediction: {e}")
+    if audio is None:
         return {"Error": 1.0}
+    
+    # The audio input from Gradio is a tuple of (sample_rate, audio_array)
+    sr, audio_array = audio
+    
+    # Process the audio input
+    if len(audio_array.shape) > 1:
+        audio_array = audio_array.mean(axis=1)  # Convert stereo to mono
+    
+    print(f"Processing audio: sample rate={sr}, shape={audio_array.shape}")
+    
+    # Classify the dialect
+    predictions = classifier({"sampling_rate": sr, "raw": audio_array})
+    
+    # Format results for display
+    results = {}
+    for pred in predictions:
+        dialect_name = dialect_mapping.get(pred['label'], pred['label'])
+        results[dialect_name] = float(pred['score'])
+    
+    return results
 
-# Find example files
-example_files = []
+# Manually prepare example file paths without metadata
+examples = []
 examples_dir = "examples"
 if os.path.exists(examples_dir):
     for filename in os.listdir(examples_dir):
         if filename.endswith((".wav", ".mp3", ".ogg")):
-            example_files.append(os.path.join(examples_dir, filename))
-            
-    print(f"Found {len(example_files)} example files")
+            examples.append([os.path.join(examples_dir, filename)])
+    
+    print(f"Found {len(examples)} example files")
 else:
     print("Examples directory not found")
 
-# Examples with labels
-examples = []
-if example_files:
-    for file in example_files:
-        basename = os.path.basename(file)
-        dialect = basename.split("_")[0] if "_" in basename else basename.split(".")[0]
-        label = dialect_mapping.get(dialect, dialect.capitalize())
-        examples.append([file, f"{label} Sample"])
-
 # Create the Gradio interface
 demo = gr.Interface(
     fn=predict_dialect,
@@ -80,8 +63,8 @@ demo = gr.Interface(
     Upload an audio file or record your voice speaking Arabic to see which dialect it matches.
     The model identifies: Modern Standard Arabic (MSA), Egyptian, Gulf, Levantine, and Maghrebi dialects.""",
     examples=examples if examples else None,
-    examples_per_page=5,
-    flagging_mode=None  # Updated from allow_flagging
+    cache_examples=False,  # Disable caching to avoid issues
+    flagging_mode=None
 )
 
 # Launch the app