Update app.py

app.py

@@ -1,73 +1,30 @@
-# app.py
-
 import gradio as gr
-import torch
-import librosa
-import time
-import os
-import tempfile
-from pydub import AudioSegment
-from transformers import pipeline, AutoFeatureExtractor, AutoModelForAudioClassification
-
-# إعداد Whisper
-whisper_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-base")
-
-# تحميل نموذج تصنيف اللهجات
-MODEL_ID = "ylacombe/accent-classifier"
-feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
-accent_model = AutoModelForAudioClassification.from_pretrained(MODEL_ID)
-
-label_map = {
-    0: "Australia",
-    1: "Canada",
-    2: "England",
-    3: "India",
-    4: "Ireland",
-    5: "New Zealand",
-    6: "Scotland",
-    7: "South Africa",
-    8: "US",
-    16: "British Slang / Unknown Region"
-}
-
-
-def transcribe_audio(audio_path):
-    result = whisper_pipeline(audio_path)
-    return result['text']
-
-def predict_accent(audio_path):
-    audio, sr = librosa.load(audio_path, sr=16000)
-    inputs = feature_extractor(audio, sampling_rate=16000, return_tensors="pt")
-    with torch.no_grad():
-        logits = accent_model(**inputs).logits
-        predicted_id = torch.argmax(logits, dim=-1).item()
-    return label_map.get(predicted_id, f"Unknown (ID: {predicted_id})")
-
-def agent_run(audio_path):
-    start_time = time.time()
-    accent = predict_accent(audio_path)
-    transcription = transcribe_audio(audio_path)
-    elapsed = round(time.time() - start_time, 2)
-    return audio_path, accent, transcription, f"{elapsed} sec"
-
+from transformers import pipeline
+
+# تحميل النموذج الجاهز من Hugging Face
+accent_classifier = pipeline("audio-classification", model="Yactayo/AccentClassifier")
+
+def classify_accent(audio_path):
+    try:
+        results = accent_classifier(audio_path)
+        top_result = results[0]
+        label = top_result["label"]
+        score = round(top_result["score"] * 100, 2)
+        return f"{label} ({score}%)"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# واجهة Gradio
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 Accent Detection Agent")
-    gr.Markdown("Record or upload audio and let the AI detect the accent and transcribe your speech.")
-
-    # ❌ source="microphone" تمت إزالته
-    audio_input = gr.Audio(type="filepath", label="🎙️ Upload or Record Audio")
-    run_button = gr.Button("Analyze")
-
-    audio_output = gr.Audio(label="Audio")
-    accent_output = gr.Textbox(label="Detected Accent")
-    transcription_output = gr.Textbox(label="Transcription")
-    time_output = gr.Textbox(label="Processing Time")
-
-    run_button.click(
-        fn=agent_run,
-        inputs=[audio_input],
-        outputs=[audio_output, accent_output, transcription_output, time_output]
-    )
-
+    gr.Markdown("## 🎙️ تصنيف اللهجات باستخدام AI")
+    
+    with gr.Row():
+        audio_input = gr.Audio(type="filepath", label="🔊 سجل أو حمّل ملف صوتي", sources=["upload", "microphone"])
+        output_text = gr.Textbox(label="اللهجة المتوقعة")
+    
+    analyze_btn = gr.Button("🔍 تحليل اللهجة")
+    analyze_btn.click(fn=classify_accent, inputs=audio_input, outputs=output_text)
+
+# تشغيل التطبيق
 if __name__ == "__main__":
     demo.launch()