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# Prerequisites
import os
os.environ["OMP_NUM_THREADS"] = "1" # Set 1, 2, or 4 depending on CPU usage
from transformers import pipeline
import json
import pandas as pd
import gradio as gr
# get candidate labels
with open("packing_label_structure.json", "r") as file:
candidate_labels = json.load(file)
keys_list = list(candidate_labels.keys())
# Load packing item data
with open("packing_templates_self_supported_offgrid_expanded.json", "r") as file:
packing_items = json.load(file)
# function and gradio app
def classify(model_name, trip_descr, cut_off = 0.5):
classifier = pipeline("zero-shot-classification", model=model_name)
## Create and fill dataframe with class predictions
df = pd.DataFrame(columns=['superclass', 'pred_class'])
for i, key in enumerate(keys_list):
if key == 'activities':
result = classifier(trip_descr, candidate_labels[key], multi_label=True)
indices = [i for i, score in enumerate(result['scores']) if score > cut_off]
classes = [result['labels'][i] for i in indices]
else:
result = classifier(trip_descr, candidate_labels[key])
classes = result["labels"][0]
df.loc[i] = [key, classes]
## Look up and return list of items to pack based on class predictions
# make list from dataframe column
all_classes = [elem for x in df["pred_class"] for elem in (x if isinstance(x, list) else [x])]
# look up packing items for each class/key
list_of_list_of_items = [packing_items.get(k, []) for k in all_classes]
# combine lists and remove doubble entries
flat_unique = []
for sublist in list_of_list_of_items:
for item in sublist:
if item not in flat_unique:
flat_unique.append(item)
# sort alphabetically to notice duplicates
sorted_list = sorted(flat_unique)
return df, "\n".join(sorted_list)
demo = gr.Interface(
fn=classify,
inputs=[
gr.Textbox(label="Model name", value = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"),
gr.Textbox(label="Trip description"),
gr.Number(label="Activity cut-off", value = 0.5),
],
# outputs="dataframe",
outputs=[gr.Dataframe(label="DataFrame"), gr.Textbox(label="List of words")],
title="Trip classification",
description="Enter a text describing your trip",
)
# Launch the Gradio app
if __name__ == "__main__":
demo.launch() |