Delete app.py

app.py

@@ -1,38 +0,0 @@
-import torch
-import torchaudio
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-import gradio as gr
-
-# Load model and processor
-model = Wav2Vec2ForCTC.from_pretrained("tacab/tacab_asr_somali")
-processor = Wav2Vec2Processor.from_pretrained("tacab/tacab_asr_somali")
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-# Transcription function
-def transcribe(audio_path):
-    waveform, sample_rate = torchaudio.load(audio_path)
-    if sample_rate != 16000:
-        waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
-    if waveform.shape[0] > 1:
-        waveform = waveform.mean(dim=0, keepdim=True)
-    inputs = processor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt")
-    input_values = inputs.input_values.to(device)
-    with torch.no_grad():
-        logits = model(input_values).logits
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.batch_decode(predicted_ids)[0]
-    return transcription.lower()
-
-# Setup Gradio Interface
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=gr.Audio(type="filepath", label="🎙️ Somali Audio"),
-    outputs=gr.Text(label="📄 Transcription"),
-    title="Tacab Somali ASR",
-    description="Speak Somali and get transcription back!",
-)
-
-# ✅ Critical: This exposes /api/predict
-iface.launch(server_name="0.0.0.0")