| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| from dotenv import load_dotenv | |
| from utils_config import load_config | |
| from utils_json import get_json_one_individual, save_to_all_individuals | |
| import os | |
| load_dotenv() | |
| PATH = os.getcwd() + "/" | |
| PATH_ASSETS = os.getenv('PATH_ASSETS') | |
| PATH_CONFIG = PATH + PATH_ASSETS + "config/" | |
| def get_headers(): | |
| headers_config = load_config(PATH_CONFIG + "config_headers.json") | |
| headers = headers_config["headers"] | |
| return headers | |
| def get_fields(): | |
| fields_config = load_config(PATH_CONFIG + "config_fields.json") | |
| fields = fields_config["fields"] | |
| return fields | |
| def match_data_to_fields(fields, one_individual): | |
| new_row = {} | |
| for key in fields: | |
| if key in one_individual: | |
| if key=="image": | |
| new_row[key] = one_individual[key] | |
| elif type(one_individual[key])==list: | |
| new_row[key] = ' , '.join(one_individual[key]) | |
| else: | |
| new_row[key] = one_individual[key] | |
| else: | |
| new_row[key] = "NA" | |
| return new_row | |
| def process_animals(all_animals): | |
| processed_animals = [] | |
| for _, animal in all_animals.items(): | |
| image = np.array(animal["image"]) | |
| caption = [] | |
| for key, val in animal.items(): | |
| if key!="image": | |
| if key=="latitude": | |
| caption.extend([ | |
| " | Latitude: " + str(animal["latitude"])]) | |
| elif key=="longitude": | |
| caption.extend([ | |
| " | Longitude: " + str(animal["longitude"])]) | |
| elif key=="wounded" and val=="True": | |
| caption.extend([" | Wounded: " + animal["wounded"]]) | |
| elif key=="dead" and val=="True": | |
| caption.extend([" | Dead: " + animal["dead"]]) | |
| # elif key=="circumstance": | |
| # caption.extend([" | Circumstances: " , | |
| # animal["circumstance"], | |
| # animal["circumstance_dropdown_level1"], | |
| # animal["circumstance_dropdown_level2"], | |
| # animal["circumstance_openfield_level2"], | |
| # animal["circumstance_dropdown_extra_level2"]]) | |
| # elif key=="behavior": | |
| # caption.extend([" | Behavior: ", animal[key]]) | |
| # elif "physical_changes" in key: | |
| # if not(" | Physical Changes: " in caption) : | |
| # caption.extend([" | Physical Changes: ", | |
| # "Beak: " + animal["physical_changes_beak"], | |
| # "Body: " + animal["physical_changes_body"], | |
| # "Head: " + animal["physical_changes_head"], | |
| # "Feathers: " + animal["physical_changes_feathers"], | |
| # "Legs: " + animal["physical_changes_legs"]]) | |
| caption_str = " ".join(caption) | |
| animal = (image, caption_str) | |
| processed_animals.append(animal) | |
| return processed_animals | |
| def set_gallery_size(len_animals): | |
| if len_animals < 10: | |
| num_cols=5 | |
| num_rows=2 | |
| else: | |
| num_cols = len_animals/2 | |
| num_rows = len_animals/(num_cols) | |
| return num_cols, num_rows | |
| def save_individual_to_gallery(gallery): | |
| one_individual = get_json_one_individual() | |
| fields = get_fields() | |
| one_individual_matched = match_data_to_fields(fields, one_individual) | |
| all_animals = save_to_all_individuals(one_individual_matched) | |
| num_cols, num_rows = set_gallery_size(len(all_animals)) | |
| processed_animals = process_animals(all_animals) | |
| gallery = gr.Gallery( | |
| label="Gallery of Records", elem_id="gallery", | |
| columns=[num_cols], rows=[num_rows], | |
| value=processed_animals, | |
| object_fit="contain", height="auto", interactive=False) | |
| return gallery | |
| # def save_individual_to_df(df): | |
| # fields = get_fields() | |
| # one_individual = get_json_one_individual() | |
| # headers = get_headers() | |
| # new_row = match_data_to_fields(fields, one_individual) | |
| # new_row = format_row(new_row, headers) | |
| # new_row_df = pd.DataFrame([new_row], columns=headers) | |
| # df_new = pd.concat([df, new_row_df], ignore_index=True) | |
| # df_gr = gr.DataFrame(visible=True, | |
| # value=df_new, | |
| # headers=headers) | |
| # return df_gr | |
| # def save_and_rest_df(df): | |
| # save_all_animals(df) | |
| # df = gr.Dataframe(fields=get_fields(), | |
| # visible=False) | |
| # return df | |
| # def format_row(new_row, headers): | |
| # formatted_row = {} | |
| # #formatted_row["image"] = new_row["image"] | |
| # for header in headers: | |
| # if header=="location": | |
| # formatted_row[header] = " | ".join(["Latitude: " + str(new_row["latitude"]), | |
| # "Longitude: " + str(new_row["longitude"])]) | |
| # elif header=="state": | |
| # formatted_row[header] = " | ".join(["Wounded: " + new_row["wounded"], | |
| # "Dead: " + new_row["dead"]]) | |
| # elif header=="circumstance": | |
| # formatted_row[header] = " | ".join([new_row["circumstance"], | |
| # new_row["circumstance_dropdown_level1"], | |
| # new_row["circumstance_dropdown_level2"], | |
| # new_row["circumstance_openfield_level2"], | |
| # new_row["circumstance_dropdown_extra_level2"]]) | |
| # elif header=="behavior": | |
| # formatted_row[header] = new_row[header] | |
| # elif header=="physical_changes": | |
| # formatted_row[header] = " | ".join([new_row["physical_changes_beak"], | |
| # new_row["physical_changes_body"], | |
| # new_row["physical_changes_head"], | |
| # new_row["physical_changes_feathers"], | |
| # new_row["physical_changes_legs"]]) | |
| # return list(formatted_row.values()) | |