try groq audio again

app.py

@@ -13,19 +13,6 @@ if not GROQ_API_KEY:
     raise ValueError("GROQ API NOT FOUND!")
 gclient = Groq(api_key=GROQ_API_KEY)
 
-HF_API_KEY = os.getenv("HF_API_KEY")
-
-if not HF_API_KEY:
-    raise ValueError(
-        "API Key Hugging Face tidak ditemukan. Pastikan file .env berisi HF_API_KEY."
-    )
-
-# Inisialisasi klien API Hugging Face
-huggingface_client = InferenceClient(api_key=HF_API_KEY)
-
-# Load Faster Whisper model versi large
-model = faster_whisper.WhisperModel("turbo", device="cpu", compute_type="int8")
-
 
 def chat_with_groq(message):
     """Handles conversation with Groq LLM."""
@@ -51,20 +38,22 @@ def save_to_file(content, filename):
     return filename
 
 
-def transcribe_audio(audio_path):
+def transcribe_audio(audio_file):
     """Transkripsi audio menggunakan Faster Whisper tanpa koreksi model Hugging Face."""
-    # segments, _ = model.transcribe(audio_path)
+    # segments, _ = model.transcribe(audio_file)
     # raw_transcription = " ".join(segment.text for segment in segments)
-    raw_transcription = gclient.audio.transcriptions.create(
-        file=(audio_path, audio_path.read()),
-        model="whisper-large-v3-turbo",
-        language="id",
-    )
-    print(raw_transcription)
+    with open(audio_file, "rb") as file:
+        res = gclient.audio.transcriptions.create(
+            file=(audio_file, audio_file.read()),
+            model="whisper-large-v3-turbo",
+            language="id",
+        )
+        print(res)
+        raw_transcription = res.text
     return (
         raw_transcription,
         save_to_file(raw_transcription, "transcription_large.txt"),
-        audio_path,
+        audio_file,
     )
 
 
@@ -82,13 +71,7 @@ def generate_soap_summary(transcription_text, selected_model):
 
     ### Catatan SOAP:
     """
-    # messages = [
-    #     {"role": "user", "content": template.format(dialogue=transcription_text)}
-    # ]
-    # response = huggingface_client.chat.completions.create(
-    #     model=selected_model, messages=messages, max_tokens=1000, stream=False
-    # )
-    # soap = response.choices[0].message.content.strip()
+
     soap = chat_with_groq(template.format(dialogue=transcription_text))
     return soap, save_to_file(soap, "soap_summary.txt")
 
@@ -105,13 +88,6 @@ def detect_medical_tags(transcription_text, selected_model):
     ### Percakapan:
     {dialogue}
     """
-    # messages = [
-    #     {"role": "user", "content": template.format(dialogue=transcription_text)}
-    # ]
-    # response = huggingface_client.chat.completions.create(
-    #     model=selected_model, messages=messages, max_tokens=500, stream=False
-    # )
-    # tags = response.choices[0].message.content.strip()
     tags = chat_with_groq(template.format(dialogue=transcription_text))
     return tags, save_to_file(tags, "medical_tags.txt")