Add HuggingFace Inference Endpoint handler

Add handler.py with EndpointHandler class that accepts raw audio bytes
and returns depression/anxiety severity scores. Fix torch.load to use
weights_only=False and correct package names in requirements.txt.

handler.py

@@ -0,0 +1,29 @@
+"""HuggingFace Inference Endpoint handler for the DAM model."""
+
+import io
+from pathlib import Path
+
+from pipeline import Pipeline
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        checkpoint = Path(path) / "dam3.1.ckpt"
+        self.pipeline = Pipeline(checkpoint=checkpoint)
+
+    def __call__(self, data):
+        inputs = data.get("inputs")
+        if isinstance(inputs, bytes):
+            source = io.BytesIO(inputs)
+        else:
+            raise ValueError("Expected raw audio bytes in data['inputs']")
+
+        quantized = self.pipeline.run_on_file(source, quantize=True)
+        source.seek(0)
+        raw = self.pipeline.run_on_file(source, quantize=False)
+
+        return {
+            "depression": quantized["depression"],
+            "anxiety": quantized["anxiety"],
+            "raw_scores": {k: v.mean().item() for k, v in raw.items()},
+        }

pipeline.py

@@ -22,7 +22,7 @@ class Pipeline:
         self.device = device
         self.model = Classifier(**config)
         self.preprocessor = Preprocessor(**self.model.preprocessor_config)
-        state_dict = torch.load(checkpoint, map_location=device)
+        state_dict = torch.load(checkpoint, map_location=device, weights_only=False)
         self.model.load_state_dict(state_dict)
         self.model.to(self.device)
         self.model.eval()

requirements.txt

@@ -1,5 +1,5 @@
-pytorch~=2.6.0
-pysoundfile~=0.13.1
-torchaudio~=2.7.0
+torch~=2.6.0
+soundfile~=0.13.1
+torchaudio~=2.6.0
 transformers~=4.52.3
 peft~=0.15.2