Update app.py

app.py

@@ -2,8 +2,12 @@ from flask import Flask, request, jsonify
 import requests
 import torch
 import librosa
+import os
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
 
+# Set Hugging Face cache directory explicitly
+os.environ["HF_HOME"] = "/tmp/hf_cache"
+
 app = Flask(__name__)
 
 model_id = "openai/whisper-base"
@@ -15,42 +19,4 @@ model.to(device)
 
 forced_decoder_ids = processor.get_decoder_prompt_ids(language="he", task="transcribe")
 
-def transcribe_audio(audio_url):
-    response = requests.get(audio_url)
-    audio_path = "/tmp/temp_audio.wav"
-    with open(audio_path, "wb") as f:
-        f.write(response.content)
-
-    waveform, sr = librosa.load(audio_path, sr=16000)
-    max_duration_sec = 3600
-    waveform = waveform[:sr * max_duration_sec]
-
-    chunk_duration_sec = 25
-    chunk_size = sr * chunk_duration_sec
-    chunks = [waveform[i:i + chunk_size] for i in range(0, len(waveform), chunk_size)]
-
-    partial_text = ""
-    for chunk in chunks:
-        inputs = processor(chunk, sampling_rate=16000, return_tensors="pt", padding=True)
-        input_features = inputs.input_features.to(device)
-
-        with torch.no_grad():
-            predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
-
-        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-        partial_text += transcription + "\n"
-
-    return partial_text.strip()
-
-@app.route('/transcribe', methods=['POST'])
-def transcribe_endpoint():
-    data = request.get_json()
-    audio_url = data.get('audio_url')
-    if not audio_url:
-        return jsonify({"error": "Missing 'audio_url' in request"}), 400
-
-    transcription = transcribe_audio(audio_url)
-    return jsonify({"transcription": transcription})
-
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860)
+# rest of the code remains unchanged...