Spaces:
Running
Running
Commit
Β·
ec31743
1
Parent(s):
4cee811
simplify UI
Browse files
app.py
CHANGED
@@ -21,8 +21,7 @@ def chat_with_groq(message):
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messages=[
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{
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"role": "system",
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"content": """Anda adalah asisten medis yang membantu dokter dalam menyusun catatan
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Ringkaskan dalam bentuk paragraf tanpa adanya bullet point dan gunakan bahasa Indonesia.""",
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},
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{"role": "user", "content": message},
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],
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@@ -39,7 +38,7 @@ def save_to_file(content, filename):
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def transcribe_audio(audio_file):
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"""Transkripsi audio menggunakan
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# segments, _ = model.transcribe(audio_file)
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# raw_transcription = " ".join(segment.text for segment in segments)
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with open(audio_file, "rb") as file:
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@@ -50,33 +49,36 @@ def transcribe_audio(audio_file):
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)
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print(res)
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raw_transcription = res.text
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return (
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raw_transcription,
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save_to_file(raw_transcription, "transcription_large.txt"),
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audio_file,
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)
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def generate_soap_summary(transcription_text
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"""
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template = """Harap buat ringkasan dalam format berikut:
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Subjective:
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ICD10:
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Objective:
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Assessment:
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Plan:
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### Percakapan:
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{dialogue}
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-
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### Catatan SOAP:
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"""
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soap = chat_with_groq(template.format(dialogue=transcription_text))
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return soap, save_to_file(soap, "soap_summary.txt")
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def detect_medical_tags(transcription_text
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"""Mendeteksi tags Diagnosis, Obat, Hasil Lab, dan Radiologi menggunakan model yang dipilih."""
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template = """
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Identifikasi dan berikan saran dalam bahasa Indonesia tindakan logis selanjutnya dalam format:
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@@ -94,24 +96,16 @@ def detect_medical_tags(transcription_text, selected_model):
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# Antarmuka Gradio
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with gr.Blocks(
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title="AI-based Medical SOAP Summarization and Tag Detection with
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) as app:
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gr.Markdown(
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"## Medical SOAP Summarization and Tag Detection with Faster Whisper Large"
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)
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with gr.Row():
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with gr.Column():
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audio_input = gr.Audio(
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soap_button = gr.Button("π Buat SOAP")
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tags_button = gr.Button("π·οΈ Deteksi Saran Tags")
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transcription_edit_box = gr.Textbox(
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label="π Hasil Transkripsi (Faster Whisper Large) - Bisa Diedit",
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lines=3,
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interactive=True,
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)
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with gr.Column():
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soap_output = gr.Textbox(label="π Hasil SOAP", lines=10, interactive=False)
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@@ -129,28 +123,14 @@ with gr.Blocks(
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transcribe_button.click(
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transcribe_audio,
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inputs=[audio_input],
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outputs=[
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)
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# Tombol SOAP
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soap_button.click(
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generate_soap_summary,
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inputs=[transcription_edit_box],
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outputs=[soap_output, download_soap],
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)
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# Tombol Tags
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tags_button.click(
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detect_medical_tags,
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inputs=[transcription_edit_box],
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outputs=[tags_output, download_tags],
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)
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# Jalankan aplikasi
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messages=[
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{
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"role": "system",
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"content": """Anda adalah asisten medis yang membantu dokter dalam menyusun catatan medis dalam bentuk paragraf menggunakan bahasa Indonesia.""",
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},
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{"role": "user", "content": message},
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],
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def transcribe_audio(audio_file):
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"""Transkripsi audio menggunakan Whisper tanpa koreksi model Hugging Face."""
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# segments, _ = model.transcribe(audio_file)
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# raw_transcription = " ".join(segment.text for segment in segments)
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with open(audio_file, "rb") as file:
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)
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print(res)
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raw_transcription = res.text
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soap_output, download_soap = generate_soap_summary(raw_transcription)
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tags_output, download_tags = detect_medical_tags(raw_transcription)
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return (
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save_to_file(raw_transcription, "raw_transcription.txt"),
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audio_file,
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soap_output,
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download_soap,
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tags_output,
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download_tags,
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)
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def generate_soap_summary(transcription_text):
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template = """Buat ringkasan SOAP berdasarkan percakapan dokter dan pasien dalam format berikut:
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Subjective:
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ICD10:
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Objective:
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Assessment:
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Plan:
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### Percakapan:
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{dialogue}
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"""
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soap = chat_with_groq(template.format(dialogue=transcription_text))
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return soap, save_to_file(soap, "soap_summary.txt")
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def detect_medical_tags(transcription_text):
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"""Mendeteksi tags Diagnosis, Obat, Hasil Lab, dan Radiologi menggunakan model yang dipilih."""
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template = """
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Identifikasi dan berikan saran dalam bahasa Indonesia tindakan logis selanjutnya dalam format:
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# Antarmuka Gradio
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with gr.Blocks(
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title="AI-based Medical SOAP Summarization and Tag Detection with Whisper Large"
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) as app:
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gr.Markdown("## Medical SOAP Summarization and Tag Detection with Whisper Large")
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with gr.Row():
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with gr.Column():
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audio_input = gr.Audio(
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source="microphone", type="filepath", label="ποΈ Rekam Suara"
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)
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transcribe_button = gr.Button("π§ Tulis Rekam Medis")
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with gr.Column():
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soap_output = gr.Textbox(label="π Hasil SOAP", lines=10, interactive=False)
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transcribe_button.click(
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transcribe_audio,
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inputs=[audio_input],
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outputs=[
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download_transcription,
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download_audio,
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soap_output,
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download_soap,
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tags_output,
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download_tags,
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],
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)
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# Jalankan aplikasi
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