try new mistral

app.py

@@ -9,7 +9,9 @@ load_dotenv()
 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.")
+    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)
@@ -19,22 +21,29 @@ model = faster_whisper.WhisperModel("turbo", device="cpu", compute_type="int8")
 
 # Daftar model yang dapat dipilih
 MODEL_OPTIONS = [
+    "mistralai/Mistral-Small-3.1-24B-Instruct-2503",
     "mistralai/Mistral-7B-Instruct-v0.3",
     "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
-    "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "Qwen/Qwen2.5-Coder-32B-Instruct"
+    "Qwen/Qwen2.5-Coder-32B-Instruct",
 ]
 
+
 def save_to_file(content, filename):
-    with open(filename, 'w', encoding='utf-8') as file:
+    with open(filename, "w", encoding="utf-8") as file:
         file.write(content)
     return filename
 
+
 def transcribe_audio(audio_path):
     """Transkripsi audio menggunakan Faster Whisper tanpa koreksi model Hugging Face."""
     segments, _ = model.transcribe(audio_path)
     raw_transcription = " ".join(segment.text for segment in segments)
-    return raw_transcription, save_to_file(raw_transcription, 'transcription_large.txt'), audio_path
+    return (
+        raw_transcription,
+        save_to_file(raw_transcription, "transcription_large.txt"),
+        audio_path,
+    )
+
 
 def generate_soap_summary(transcription_text, selected_model):
     """Membuat ringkasan SOAP dari teks transkripsi menggunakan model yang dipilih."""
@@ -53,15 +62,15 @@ def generate_soap_summary(transcription_text, selected_model):
 
     ### Catatan SOAP:
     """
-    messages = [{"role": "user", "content": template.format(dialogue=transcription_text)}]
+    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
+        model=selected_model, messages=messages, max_tokens=1000, stream=False
     )
     soap = response.choices[0].message.content.strip()
-    return soap, save_to_file(soap, 'soap_summary.txt')
+    return soap, save_to_file(soap, "soap_summary.txt")
+
 
 def detect_medical_tags(transcription_text, selected_model):
     """Mendeteksi tags Diagnosis, Obat, Hasil Lab, dan Radiologi menggunakan model yang dipilih."""
@@ -75,37 +84,49 @@ def detect_medical_tags(transcription_text, selected_model):
     ### Percakapan:
     {dialogue}
     """
-    messages = [{"role": "user", "content": template.format(dialogue=transcription_text)}]
+    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
+        model=selected_model, messages=messages, max_tokens=500, stream=False
     )
     tags = response.choices[0].message.content.strip()
-    return tags, save_to_file(tags, 'medical_tags.txt')
+    return tags, save_to_file(tags, "medical_tags.txt")
+
 
 # Antarmuka Gradio
-with gr.Blocks(title="AI-based Medical SOAP Summarization and Tag Detection with Faster Whisper Large") as app:
-    gr.Markdown("## Medical SOAP Summarization and Tag Detection with Faster Whisper Large")
+with gr.Blocks(
+    title="AI-based Medical SOAP Summarization and Tag Detection with Faster Whisper Large"
+) as app:
+    gr.Markdown(
+        "## Medical SOAP Summarization and Tag Detection with Faster Whisper Large"
+    )
 
     with gr.Row():
         with gr.Column():
             model_selector = gr.Dropdown(
                 choices=MODEL_OPTIONS,
-                value="mistralai/Mixtral-8x7B-Instruct-v0.1",
-                label="🔍 Pilih Model AI"
+                value="mistralai/Mistral-Small-3.1-24B-Instruct-2503",
+                label="🔍 Pilih Model AI",
             )
             audio_input = gr.Audio("microphone", type="filepath", label="🎙️ Rekam Suara")
             transcribe_button = gr.Button("🎧 Transkripsi dengan Whisper Large")
-            transcription_edit_box = gr.Textbox(label="📄 Hasil Transkripsi (Faster Whisper Large) - Bisa Diedit", lines=12, interactive=True)
+            transcription_edit_box = gr.Textbox(
+                label="📄 Hasil Transkripsi (Faster Whisper Large) - Bisa Diedit",
+                lines=3,
+                interactive=True,
+            )
             update_transcription_button = gr.Button("💾 Simpan Hasil Edit")
             soap_button = gr.Button("📝 Buat SOAP")
             tags_button = gr.Button("🏷️ Deteksi Tags")
 
         with gr.Column():
             soap_output = gr.Textbox(label="📃 Hasil SOAP", lines=10, interactive=False)
-            tags_output = gr.Textbox(label="🏷️ Hasil Tags Diagnosis, Obat, Hasil Lab, Radiologi", lines=10, interactive=False)
+            tags_output = gr.Textbox(
+                label="🏷️ Hasil Tags Diagnosis, Obat, Hasil Lab, Radiologi",
+                lines=10,
+                interactive=False,
+            )
             download_audio = gr.File(label="⬇️ Download Rekaman")
             download_transcription = gr.File(label="⬇️ Download Transkripsi")
             download_soap = gr.File(label="⬇️ Download SOAP")
@@ -115,28 +136,28 @@ with gr.Blocks(title="AI-based Medical SOAP Summarization and Tag Detection with
     transcribe_button.click(
         transcribe_audio,
         inputs=[audio_input],
-        outputs=[transcription_edit_box, download_transcription, download_audio]
+        outputs=[transcription_edit_box, download_transcription, download_audio],
     )
 
     # Tombol Simpan Hasil Edit
     update_transcription_button.click(
-        lambda text: (text, save_to_file(text, 'user_edited_transcription.txt')),
+        lambda text: (text, save_to_file(text, "user_edited_transcription.txt")),
         inputs=[transcription_edit_box],
-        outputs=[transcription_edit_box, download_transcription]
+        outputs=[transcription_edit_box, download_transcription],
     )
 
     # Tombol SOAP
     soap_button.click(
         generate_soap_summary,
         inputs=[transcription_edit_box, model_selector],
-        outputs=[soap_output, download_soap]
+        outputs=[soap_output, download_soap],
     )
 
     # Tombol Tags
     tags_button.click(
         detect_medical_tags,
         inputs=[transcription_edit_box, model_selector],
-        outputs=[tags_output, download_tags]
+        outputs=[tags_output, download_tags],
     )
 
 # Jalankan aplikasi

ya.txt

@@ -1 +0,0 @@
-halo