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import gradio as gr
import pandas as pd
import numpy as np
from dotenv import load_dotenv
import os
from utils.utils_config import load_config
from validation_submission.get_json import get_json_one_individual
from validation_submission.submission import save_to_all_individuals
from validation_submission.validation import validate_individual
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):
validate_individual()
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())
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