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app.py

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+import os
+os.system("pip install torch")
+os.system("pip install transformers")
+os.system("pip install sentencepiece")
+os.system("pip install plotly")
+
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import sentencepiece
+import torch
+import plotly.graph_objects as go
+import streamlit as st
+
+text_1 = """Avec la Ligue 1 qui reprend ses droits à partir de vendredi 5 août, et un premier match pour ce qui les concerne samedi soir, à Clermont-Ferrand, l’heure est désormais arrivée pour les Parisiens d’apporter les preuves que ce changement d’ère est bien une réalité."""
+
+text_2 = """Créées en 1991 sur un modèle inspiré de la Fête de la musique, les Nuits des étoiles ont pour thème en 2022 l’exploration spatiale, en partenariat avec l’Agence spatiale européenne."""
+
+@st.cache(allow_output_mutation=True)
+def list2text(label_list):
+    labels = ""
+    for label in label_list:
+        labels = labels + label + ","
+    labels = labels[:-1]
+    return labels
+
+label_list_1 = ["monde", "économie", "sciences", "culture", "santé", "politique", "sport", "technologie"]
+label_list_2 = ["positif", "négatif", "neutre"]
+
+st.title("French Zero-Shot Text Classification \
+    with CamemBERT and XLM-R")
+
+# Body
+st.markdown(
+    """
+    
+    This application makes use of [CamemBERT](https://camembert-model.fr/) and [XLM-R](https://arxiv.org/abs/1911.02116) models that were fine-tuned on the XNLI corpus. While CamemBERT was fine-tuned only on the French part of the corpus by [Baptiste Doyen](https://huggingface.co/BaptisteDoyen), XLM-R was done so on all parts of it by [Joe Davison](https://huggingface.co/joeddav), including French. Therefore, in this app, both of these two models are intended to be used and made comparison of each other for zero-shot classification in French. 
+
+    """
+)
+
+model_list = ['BaptisteDoyen/camembert-base-xnli',
+             'joeddav/xlm-roberta-large-xnli']
+
+st.sidebar.header("Select Model")
+model_checkpoint = st.sidebar.radio("", model_list)
+
+st.sidebar.write("For the full descriptions of the models:")
+st.sidebar.write("[camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli)")
+st.sidebar.write("[xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli)")
+
+st.sidebar.write("For the XNLI Dataset:")
+st.sidebar.write("[XNLI](https://huggingface.co/datasets/xnli)")
+
+st.subheader("Select Text and Label List")
+st.text_area("Text #1", text_1, height=128)
+st.text_area("Text #2", text_2, height=128)
+st.write(f"Label List #1: {list2text(label_list_1)}")
+st.write(f"Label List #2: {list2text(label_list_2)}")
+
+text = st.radio("Select Text", ("Text #1", "Text #2", "New Text"))
+labels = st.radio("Select Label List", ("Label List #1", "Label List #2", "New Label List"))
+
+if text == "Text #1": selected_text = text_1
+elif text == "Text #2": selected_text = text_2
+elif text == "New Text":
+    selected_text = st.text_area("New Text", value="", height=128)
+
+if labels == "Label List #1": selected_labels = label_list_1
+elif labels == "Label List #2": selected_labels = label_list_2
+elif labels == "New Label List":
+    selected_labels = st.text_area("New Label List (Pls Input as comma-separated)", value="", height=16).split(",")
+
+@st.cache(allow_output_mutation=True)
+def setModel(model_checkpoint):
+    model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+    return pipeline("zero-shot-classification", model=model, tokenizer=tokenizer)
+        
+Run_Button = st.button("Run", key=None)
+if Run_Button == True:
+    
+    zstc_pipeline = setModel(model_checkpoint)
+    output = zstc_pipeline(sequences=selected_text, candidate_labels=selected_labels)
+    output_labels = output["labels"]
+    output_scores = output["scores"]
+
+    st.header("Result")
+    import plotly.graph_objects as go
+    fig = go.Figure([go.Bar(x=output_labels, y=output_scores)])
+    st.plotly_chart(fig, use_container_width=False, sharing="streamlit")