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| from typing import Dict | |
| from pathlib import Path | |
| import pandas as pd | |
| from io import StringIO | |
| import gradio as gr | |
| # def predict(text: str) -> Dict: | |
| # return {"alive": 0.9, "death": 0.1} | |
| # example_list = [[1.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]] | |
| # # Create title, description and article strings | |
| # title = "This is title." | |
| # description = "This is description." | |
| # article = "This is article." | |
| # default_csv = "Phase,Activity,Start date,End date\n\"Mapping the Field\",\"Literature review\",2024-01-01,2024-01-31" | |
| # def process_csv_text(temp_file): | |
| # if isinstance(temp_file, str): | |
| # print("1") | |
| # df = pd.read_csv(temp_file, header = "infer", sep = ",", encoding = "utf-8") | |
| # else: | |
| # print("2") | |
| # df = pd.read_csv(temp_file.name) | |
| # print("***") | |
| # print(df) | |
| # print("***") | |
| # return df | |
| # with gr.Blocks() as demo: | |
| # upload_button = gr.UploadButton(label="Upload Timetable", file_types = ['.csv'], file_count = "single") | |
| # table = gr.Dataframe(headers=["Phase", "Activity", "Start date", "End date"], type="pandas", col_count=4) | |
| # upload_button.upload(fn=process_csv_text, inputs=upload_button, outputs=table, api_name="upload_csv") | |
| # demo.launch(debug=True) | |
| def predict(): | |
| return {"Death": 0.9, "Alive": 0.1} | |
| def download_patient(patient_id: str) -> str: | |
| my_file = Path(f"Patient{patient_id}.csv") | |
| print(f"File to download [{str(my_file)}].") | |
| if not my_file.is_file(): | |
| raise Exception(f"[{my_file}] not found.") | |
| print(f"Downloading file [{str(my_file)}].") | |
| return str(my_file) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| patient_upload_file = gr.File(label="Upload A Patient", | |
| file_types = ['.csv'], | |
| file_count = "single") | |
| with gr.Row(): | |
| with gr.Column(min_width=100): | |
| patient_1_input_btn = gr.Button("Patient 1") | |
| patient_1_download_btn = gr.DownloadButton(label="Download 1", value="Patient1.csv") | |
| with gr.Column(min_width=100): | |
| patient_2_input_btn = gr.Button("Patient 2") | |
| patient_2_download_btn = gr.DownloadButton(label="Download 2", value="Patient2.csv") | |
| with gr.Column(min_width=100): | |
| patient_3_input_btn = gr.Button("Patient 3") | |
| patient_3_download_btn = gr.DownloadButton(label="Download 3", value="Patient3.csv") | |
| with gr.Column(): | |
| result = gr.Label(num_top_classes=2, label="Predictions") | |
| # Choose a patient to predict. | |
| patient_1_input_btn.click(fn=predict, inputs=None, outputs=result, api_name="predict") | |
| # Download a patient ehr profile. | |
| # patient_1_download_btn.click(fn=download_patient, inputs=patient_1_download_btn, outputs=patient_1_download_btn) | |
| # patient_2_download_btn.click(fn=download_patient, inputs=patient_2_download_btn, outputs=patient_2_download_btn) | |
| # patient_3_download_btn.click(fn=download_patient, inputs=patient_3_download_btn, outputs=patient_3_download_btn) | |
| demo.launch(debug=True) | |