Delete handler.py

handler.py

@@ -1,60 +0,0 @@
-from unsloth import FastModel
-from transformers import WhisperForConditionalGeneration, pipeline
-import torch
-import tempfile
-import os
-
-class EndpointHandler:
-    def __init__(self, model_path):
-        # Load Unsloth Whisper model
-        model, tokenizer = FastModel.from_pretrained(
-            model_name = model_path,
-            dtype = None,
-            load_in_4bit = False,
-            auto_model = WhisperForConditionalGeneration,
-            whisper_language = "Haitian",
-            whisper_task = "transcribe"
-        )
-
-        # Prepare model for inference
-        FastModel.for_inference(model)
-        model.eval()
-
-        # Load ASR pipeline
-        self.pipeline = pipeline(
-            "automatic-speech-recognition",
-            model=model,
-            tokenizer=tokenizer.tokenizer,
-            feature_extractor=tokenizer.feature_extractor,
-            processor=tokenizer,
-            return_language=True,
-            torch_dtype=torch.float16,
-        )
-
-        # ⚠️ Remove forced_decoder_ids from generation config (causes runtime error)
-        if hasattr(self.pipeline.model.generation_config, "forced_decoder_ids"):
-            del self.pipeline.model.generation_config.forced_decoder_ids
-        if hasattr(self.pipeline.model.generation_config, "is_forced_decoder_ids_init"):
-            del self.pipeline.model.generation_config.is_forced_decoder_ids_init
-
-    def __call__(self, data):
-        audio = data.get("inputs")
-        if audio is None:
-            return "Error: No input audio provided."
-
-        try:
-            # Handle byte input (e.g., uploaded or streamed audio)
-            if isinstance(audio, bytes):
-                with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
-                    f.write(audio)
-                    file_path = f.name
-            elif isinstance(audio, str) and os.path.isfile(audio):
-                file_path = audio
-            else:
-                return "Error: Invalid input. Expected audio bytes or file path."
-
-            result = self.pipeline(file_path)
-            return result["text"]
-
-        except Exception as e:
-            return f"Error during transcription: {str(e)}"