first working app

app.py

@@ -1,51 +1,37 @@
+from __future__ import annotations
 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_turkish.classifiers import NSPZeroshotClassifier, NLIZeroshotClassifier
 
-st.title("Zero-shot Turkish Text Classification")
+if "current_model" not in st.session_state:
+    st.session_state["current_model"] = None
 
-method_selection = st.radio(
-    "Select a zero-shot classification method.",
-    [
-        METHOD_OPTIONS["nli"],
-        METHOD_OPTIONS["nsp"],
-    ],
-)
+if "current_model_option" not in st.session_state:
+    st.session_state["current_model_option"] = None
 
-if method_selection == METHOD_OPTIONS["nli"]:
-    model = st.selectbox(
-        "Select a natural language inference model.", NLI_MODEL_OPTIONS
-    )
-if method_selection == METHOD_OPTIONS["nsp"]:
-    model = st.selectbox(
-        "Select a BERT model for next sentence prediction.", NSP_MODEL_OPTIONS
-    )
+if "current_method_option" not in st.session_state:
+    st.session_state["current_method_option"] = None
 
-st.header("Configure prompts and labels")
-col1, col2 = st.columns(2)
 
-with col1:
-    st.subheader("Candidate labels")
-    labels = st.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="spor,dünya,siyaset,ekonomi,kültür ve sanat",
-    )
-    st.header("Make predictions")
-    st.text_area("", value="Enter some text to classify.")
-    st.button("Predict")
+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!")
 
-with col2:
-    st.subheader("Prompt template")
-    prompt_template = st.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="Bu metin {} kategorisine aittir",
+
+def visualize_output(labels: list[str], probabilities: list[float]):
+    data = pd.DataFrame({"labels": labels, "probability": probabilities}).sort_values(
+        by="probability", ascending=False
     )
-    st.header("")
-    probs = [0.86, 0.10, 0.01, 0.02, 0.01]
-    data = pd.DataFrame(
-        {"labels": labels.split(","), "probability": probs}
-    ).sort_values(by="probability", ascending=False)
     chart = px.bar(
         data,
         x="probability",
@@ -67,4 +53,69 @@ with col2:
             "showlegend": False,
         }
     )
-    st.plotly_chart(chart)
+    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,
+    )
+if method_option == METHOD_OPTIONS["nsp"]:
+    model_option = st.selectbox(
+        "Select a BERT model for next sentence prediction.",
+        NSP_MODEL_OPTIONS,
+    )
+
+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="spor,dünya,siyaset,ekonomi,sanat",
+    key="current_labels",
+)
+
+col1.header("Make predictions")
+text = col1.text_area(
+    "Enter a sentence or a paragraph to classify.",
+    value="Ian Anderson, Jethro Tull konserinde yan flüt çalarak zeybek oynadı.",
+    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="Bu metin {} kategorisine aittir",
+    key="current_template",
+)
+col2.header("")
+make_pred = col1.button("Predict")
+if make_pred:
+    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 prediction[0]:
+        chart = visualize_output(prediction[0]["labels"], prediction[0]["scores"])
+    elif "probabilities" in prediction[0]:
+        chart = visualize_output(
+            prediction[0]["labels"], prediction[0]["probabilities"]
+        )
+    col2.plotly_chart(chart)

models.py

@@ -1,6 +1,6 @@
 METHOD_OPTIONS = {
     "nli": "Natural Language Inference",
-    "nsp": "Next Sentence Prediction (not supported yet)",
+    "nsp": "Next Sentence Prediction",
 }
 
 NLI_MODEL_OPTIONS = [

requirements.txt

@@ -24,7 +24,7 @@ catalogue==2.0.6
 certifi==2021.10.8
 cffi==1.15.0
 charset-normalizer==2.0.7
-click==8.0.3
+click
 codecarbon==1.2.0
 commonmark==0.9.1
 configparser==5.1.0