|
|
from __future__ import annotations |
|
|
from typing import Iterable, List, Dict, Tuple |
|
|
|
|
|
import gradio as gr |
|
|
from gradio.themes.base import Base |
|
|
from gradio.themes.soft import Soft |
|
|
from gradio.themes.monochrome import Monochrome |
|
|
from gradio.themes.default import Default |
|
|
from gradio.themes.utils import colors, fonts, sizes |
|
|
|
|
|
import spaces |
|
|
import torch |
|
|
import os |
|
|
import io |
|
|
import colorsys |
|
|
|
|
|
import numpy as np |
|
|
|
|
|
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification, pipeline |
|
|
|
|
|
import matplotlib.pyplot as plt |
|
|
import plotly.graph_objects as go |
|
|
from wordcloud import WordCloud |
|
|
|
|
|
|
|
|
def hex_to_rgb(hex_color: str) -> tuple[int, int, int]: |
|
|
hex_color = hex_color.lstrip('#') |
|
|
return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) |
|
|
|
|
|
def rgb_to_hex(rgb_color: tuple[int, int, int]) -> str: |
|
|
return "#{:02x}{:02x}{:02x}".format(*rgb_color) |
|
|
|
|
|
def adjust_brightness(rgb_color: tuple[int, int, int], factor: float) -> tuple[int, int, int]: |
|
|
hsv_color = colorsys.rgb_to_hsv(*[v / 255.0 for v in rgb_color]) |
|
|
new_v = max(0, min(hsv_color[2] * factor, 1)) |
|
|
new_rgb = colorsys.hsv_to_rgb(hsv_color[0], hsv_color[1], new_v) |
|
|
return tuple(int(v * 255) for v in new_rgb) |
|
|
|
|
|
monochrome = Monochrome() |
|
|
|
|
|
auth_token = os.environ['HF_TOKEN'] |
|
|
|
|
|
model1 = AutoModelForSequenceClassification.from_pretrained("AlGe/deberta-v3-large_Int_segment", num_labels=1, token=auth_token) |
|
|
tokenizer1 = AutoTokenizer.from_pretrained("AlGe/deberta-v3-large_Int_segment", token=auth_token) |
|
|
|
|
|
model2 = AutoModelForSequenceClassification.from_pretrained("AlGe/deberta-v3-large_seq_ext", num_labels=1, token=auth_token) |
|
|
|
|
|
|
|
|
def process_classification(text: str, model1, model2, tokenizer1) -> Tuple[str, str, str]: |
|
|
inputs1 = tokenizer1(text, max_length=512, return_tensors='pt', truncation=True, padding=True) |
|
|
|
|
|
with torch.no_grad(): |
|
|
outputs1 = model1(**inputs1) |
|
|
outputs2 = model2(**inputs1) |
|
|
|
|
|
prediction1 = outputs1[0].item() |
|
|
prediction2 = outputs2[0].item() |
|
|
score = prediction1 / (prediction2 + prediction1) |
|
|
|
|
|
return f"{round(prediction1, 1)}", f"{round(prediction2, 1)}", f"{round(score, 2)}" |
|
|
|
|
|
@spaces.GPU |
|
|
def all(text: str): |
|
|
classification_output = process_classification(text, model1, model2, tokenizer1) |
|
|
|
|
|
return (classification_output[0], classification_output[1], classification_output[2]) |
|
|
|
|
|
examples = [ |
|
|
['Bevor ich meinen Hund kaufte bin ich immer alleine durch den Park gelaufen. Gestern war ich aber mit dem Hund losgelaufen. Das Wetter war sehr schön, nicht wie sonst im Winter. Ich weiß nicht genau. Mir fällt sonst nichts dazu ein. Wir trafen auf mehrere Spaziergänger. Ein Mann mit seinem Kind. Das Kind hat ein Eis gegessen.'], |
|
|
] |
|
|
|
|
|
iface = gr.Interface( |
|
|
fn=all, |
|
|
inputs=gr.Textbox(lines=5, label="Input Text", placeholder="Write about how your breakfast went or anything else that happened or might happen to you ..."), |
|
|
outputs=[ |
|
|
gr.Label(label="Internal Detail Count"), |
|
|
gr.Label(label="External Detail Count"), |
|
|
gr.Label(label="Approximated Internal Detail Ratio"), |
|
|
], |
|
|
title="Scoring Demo", |
|
|
description="Autobiographical Memory Analysis: This demo combines two text - and two sequence classification models to showcase our automated Autobiographical Interview scoring method. Submit a narrative to see the results.", |
|
|
examples=examples, |
|
|
theme=monochrome |
|
|
) |
|
|
|
|
|
iface.launch() |
