| import os | |
| import time | |
| import pandas as pd | |
| import gradio as gr | |
| def download_sheet_df(doc_id, sheet_id=0, header=0): | |
| sheet_url = f'https://docs.google.com/spreadsheets/d/{doc_id}/export?format=csv&gid={sheet_id}' | |
| return pd.read_csv(sheet_url, header=header) | |
| df = download_sheet_df(os.environ["SHEET_ID_SECRET"]) | |
| def find_reviews(uid): | |
| review_list = [] | |
| rows = [] | |
| try: | |
| for reviewer_n in [1, 2, 3, 4]: | |
| df_u = df[df[f"Reviewer_{reviewer_n}_UID_NUMBER_myucla"].astype(int) == int(uid)].copy() | |
| if len(df_u) == 1 and str(df_u.iloc[0]["Question 1 Response Edited"]) != "nan": | |
| rows.append(df_u.iloc[0]["Unnamed: 0"]) | |
| review_list.append(df_u.iloc[0][["Full_Name_myucla", "Question 1 Response Edited"]]) | |
| except ValueError: | |
| pass | |
| except Exception as e: | |
| raise e | |
| if len(review_list) == 0: | |
| review_list = pd.DataFrame([{"Error": "UID Not Found"}]) | |
| print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), f"Not Found uid: [{uid}]") | |
| else: | |
| print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), "Found rows:", sorted(rows)) | |
| return review_list[:3] | |
| demo = gr.Interface( | |
| fn=find_reviews, | |
| inputs="text", | |
| outputs=gr.Dataframe(headers=["Name", "Presentation Path"]), | |
| ) | |
| demo.launch(debug=False) |