Refactors:New model and dropped multilingual support

app.py

@@ -1,14 +1,14 @@
+from os import write
+from typing import Sequence
 import streamlit as st
-from hf_model import classifier_zero,load_model,load_model_multil
+from hf_model import classifier_zero,load_model
 from utils import plot_result,examples_load
-import json
 
-classifier_model0=load_model()
-classifier_model1=load_model_multil()
+
+classifier=load_model()
+
 ex_text,ex_labels=examples_load()
 
-# with open("examples.json") as f:
-#     data=json.load(f)
 
 if __name__ == '__main__':
     st.header("Zero Shot Classification")
@@ -16,13 +16,11 @@ if __name__ == '__main__':
 
 
     with st.form(key='my_form'):
-        text_input = st.text_area("Input Text",ex_text)
-        labels = st.text_input('Possible topics (separated by `,`)',ex_labels, max_chars=1000)
+        text_input = st.text_area("Input Text",data['text'])
+        labels = st.text_input('Possible topics (separated by `,`)',data["labels"], max_chars=1000)
         labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0]))
         radio = st.radio("Select Multiclass",('Only one topic can be corect at a time','Multiple topics can be correct at a time'),)
-        radio1 = st.radio("Select Model",('Using English Language model','Using Multilingual Model'),)
         multi_class= True if radio=="Multiple topics can be correct at a time" else False
-        classifier = classifier_model1 if radio1=="Using Multilingual Model" else classifier_model0
         submit_button = st.form_submit_button(label='Submit')
 
     if submit_button:

examples.json

@@ -1,4 +1,4 @@
 {
 "text":"The Democratic president had just signed into law the most significant infrastructure package in generations. And he had done it by bringing Democrats and Republicans together.",
-"labels":"Economy,Politics,Environment,Entertainment"
+"labels":["Economy", "Politics", "Environment", "Entertainment"]
 }

hf_model.py

@@ -2,14 +2,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification,pipel
 import torch
 
 def load_model():
-    model_name = "MoritzLaurer/DeBERTa-v3-base-mnli"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    classifier = pipeline(task='zero-shot-classification', model=model, tokenizer=tokenizer, framework='pt')
-    return classifier
-
-def load_model_multil():
-    model_name = "joeddav/xlm-roberta-large-xnli"
+    model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForSequenceClassification.from_pretrained(model_name)
     classifier = pipeline(task='zero-shot-classification', model=model, tokenizer=tokenizer, framework='pt')

utils.py

@@ -1,8 +1,6 @@
 import streamlit as st
 import numpy as np
 import plotly.express as px
-import json
-
 def plot_result(top_topics, scores):
     top_topics = np.array(top_topics)
     scores = np.array(scores)
@@ -16,10 +14,4 @@ def plot_result(top_topics, scores):
                  color_continuous_scale='GnBu')
     fig.update(layout_coloraxis_showscale=False)
     fig.update_traces(texttemplate='%{text:0.1f}%', textposition='outside')
-    st.plotly_chart(fig)
-
-def examples_load():
-    with open("examples.json") as f:
-        data=json.load(f)
-    
-    return data['text'],data['labels']
+    st.plotly_chart(fig)