Create app.py

app.py

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+import gradio as gr
+import whisper
+
+# 1. Load the model into memory (Runs once on startup)
+# "base" is a good balance of speed and accuracy for a free CPU
+print("Loading Whisper model...")
+model = whisper.load_model("base")
+
+# 2. Define the inference function
+def transcribe_audio(audio_filepath):
+    print(f"Processing audio: {audio_filepath}")
+    
+    # Run inference, forcing Arabic and extracting timestamps
+    result = model.transcribe(
+        audio_filepath, 
+        language="ar", 
+        word_timestamps=True
+    )
+    
+    # Return a clean dictionary
+    return {
+        "text": result["text"], 
+        "segments": result["segments"]
+    }
+
+# 3. Create the API routing interface
+demo = gr.Interface(
+    fn=transcribe_audio,
+    inputs=gr.Audio(type="filepath", label="Upload Arabic Audio"),
+    outputs=gr.JSON(label="Transcription Data"),
+    title="Arabic Speech-to-Text Microservice"
+)
+
+# 4. Launch the server
+demo.launch()