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from __future__ import annotations
import psutil
import pandas as pd
import streamlit as st
import plotly.express as px
from models import NLI_MODEL_OPTIONS, NSP_MODEL_OPTIONS, METHOD_OPTIONS
from zeroshot_classification.classifiers import NSPZeroshotClassifier, NLIZeroshotClassifier
print(f"Total mem: {psutil.virtual_memory().total}")
def init_state(key: str):
if key not in st.session_state:
st.session_state[key] = None
for k in [
"current_model",
"current_model_option",
"current_method_option",
"current_prediction",
"current_chart",
]:
init_state(k)
def load_model(model_option: str, method_option: str, random_state: int = 0):
with st.spinner("Loading selected model..."):
if method_option == "Natural Language Inference":
st.session_state.current_model = NLIZeroshotClassifier(
model_name=model_option, random_state=random_state
)
else:
st.session_state.current_model = NSPZeroshotClassifier(
model_name=model_option, random_state=random_state
)
st.success("Model loaded!")
def visualize_output(labels: list[str], probabilities: list[float]):
data = pd.DataFrame({"labels": labels, "probability": probabilities}).sort_values(
by="probability", ascending=False
)
chart = px.bar(
data,
x="probability",
y="labels",
color="labels",
orientation="h",
height=290,
width=500,
).update_layout(
{
"xaxis": {"title": "probability", "visible": True, "showticklabels": True},
"yaxis": {"title": None, "visible": True, "showticklabels": True},
"margin": dict(
l=10, # left
r=10, # right
t=50, # top
b=10, # bottom
),
"showlegend": False,
}
)
return chart
st.title("Zero-shot Turkish Text Classification")
method_option = st.radio(
"Select a zero-shot classification method.",
[
METHOD_OPTIONS["nli"],
METHOD_OPTIONS["nsp"],
],
)
if method_option == METHOD_OPTIONS["nli"]:
model_option = st.selectbox(
"Select a natural language inference model.", NLI_MODEL_OPTIONS, index=3
)
if method_option == METHOD_OPTIONS["nsp"]:
model_option = st.selectbox(
"Select a BERT model for next sentence prediction.", NSP_MODEL_OPTIONS, index=0
)
if model_option != st.session_state.current_model_option:
st.session_state.current_model_option = model_option
st.session_state.current_method_option = method_option
load_model(
st.session_state.current_model_option, st.session_state.current_method_option
)
st.header("Configure prompts and labels")
col1, col2 = st.columns(2)
col1.subheader("Candidate labels")
labels = col1.text_area(
label="These are the labels that the model will try to predict for the given text input. Your input labels should be comma separated and meaningful.",
value="tatli,burger,kebab,diğer,tuzlu",
key="current_labels",
)
col1.header("Make predictions")
text = col1.text_area(
"Enter a sentence or a paragraph to classify.",
value="baklava",
key="current_text",
)
col2.subheader("Prompt template")
prompt_template = col2.text_area(
label="Prompt template is used to transform NLI and NSP tasks into a general-use zero-shot classifier. Models replace {} with the labels that you have given.",
value="{}",
key="current_template",
)
col2.header("")
make_pred = col1.button("Predict")
if make_pred:
st.session_state.current_prediction = (
st.session_state.current_model.predict_on_texts(
[st.session_state.current_text],
candidate_labels=st.session_state.current_labels.split(","),
prompt_template=st.session_state.current_template,
)
)
if "scores" in st.session_state.current_prediction[0]:
st.session_state.current_chart = visualize_output(
st.session_state.current_prediction[0]["labels"],
st.session_state.current_prediction[0]["scores"],
)
elif "probabilities" in st.session_state.current_prediction[0]:
st.session_state.current_chart = visualize_output(
st.session_state.current_prediction[0]["labels"],
st.session_state.current_prediction[0]["probabilities"],
)
col2.plotly_chart(st.session_state.current_chart, use_container_width=True)
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