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2ce0a69
1
Parent(s):
aaf2b16
add dataset
Browse files- README.md +3 -3
- app.py +38 -2
- requirements.txt +3 -1
README.md
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@@ -1,8 +1,8 @@
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---
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title:
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emoji: 👀
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 5.17.0
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app_file: app.py
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---
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title: eHealth Transcribe
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emoji: 👀
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colorFrom: white
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colorTo: blue
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sdk: gradio
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sdk_version: 5.17.0
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app_file: app.py
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app.py
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@@ -5,6 +5,39 @@ import gradio as gr
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from google import genai
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from google.genai import types
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import asyncio
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def audio_from_bytes(audio_file_path: str):
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with open(DATA_FILE, "w") as file:
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json.dump(data, file, indent=4)
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return data
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user_block = gr.Column(visible=False)
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with user_block:
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counter_display = gr.Textbox(label="Status Message", interactive=False)
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gr.Interface(
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fn=transcribe_and_summarize,
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inputs=[gr.Audio(type="filepath", sources="microphone"), session],
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outputs=["text", counter_display, session],
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description="pastikan HP/Laptop memiliki microphone untuk merekam percakapan dokter-pasien menjadi rekam medis SOAP. Akun berlangganan https://ehealth.co.id dapat terintegrasi SATUSEHAT & BPJS secara otomatis",
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allow_flagging="never",
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)
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use_case_description = gr.Markdown(
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from google import genai
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from google.genai import types
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import asyncio
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from datasets import load_dataset, DatasetDict, Dataset
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from huggingface_hub import login
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import datetime
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# Authenticate with HF token
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hf_token = os.getenv("HF_TOKEN")
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login(token=hf_token)
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dataset_name = "spriambada3/ehealth_transcribe"
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def init_dataset():
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try:
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dataset = load_dataset(dataset_name)
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except Exception as e:
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print(e)
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dataset = DatasetDict(
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{"data": Dataset.from_dict({"logintime": [], "email": [], "wa": []})}
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)
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print("init dataset result ")
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print(dataset)
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return dataset
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def add_user(dataset, email, wa):
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new_data = {
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"logintime": datetime.datetime.now(),
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"email": email,
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"wa": wa,
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}
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dataset["data"] = dataset["data"].add_item(new_data)
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dataset.push_to_hub(dataset_name) # Save to HF Hub
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print("add data successful")
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def audio_from_bytes(audio_file_path: str):
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with open(DATA_FILE, "w") as file:
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json.dump(data, file, indent=4)
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wa = username
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dataset = init_dataset()
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add_user(dataset, email, wa)
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return data
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user_block = gr.Column(visible=False)
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with user_block:
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counter_display = gr.Textbox(label="Status Message", interactive=False)
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gr.Interface(
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fn=transcribe_and_summarize,
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inputs=[gr.Audio(type="filepath", sources="microphone"), session],
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outputs=["text", counter_display, session],
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description="Halo, pastikan HP/Laptop memiliki microphone untuk merekam percakapan dokter-pasien menjadi rekam medis SOAP. Akun berlangganan https://ehealth.co.id dapat terintegrasi SATUSEHAT & BPJS secara otomatis",
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allow_flagging="never",
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)
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use_case_description = gr.Markdown(
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requirements.txt
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gradio==5.17.0
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python-dotenv==1.0.1
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google-genai
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gradio==5.17.0
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python-dotenv==1.0.1
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google-genai
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huggingface_hub
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datasets
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