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# import pandas as pd
# import streamlit as st
# from transformers import pipeline
# from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, balanced_accuracy_score
# # Charger le modèle pré-entraîné
# classifier_model = "morit/french_xlm_xnli"
import pandas as pd
import streamlit as st
from transformers import pipeline
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, balanced_accuracy_score
# Charger le modèle pré-entraîné
classifier_model = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli"
classifier = pipeline("zero-shot-classification", model=classifier_model)
# Afficher l'entête
st.header("Sentiment Analysis")
df = pd.read_csv("fic.csv", sep=";")
# Récupérer les commentaires en liste
comments = df["text"].tolist()
# Afficher l'entête
st.header("Analyse de Texte")
# Créer une selectbox pour choisir un commentaire
selected_comment = st.selectbox("Veuillez sélectionner un commentaire", comments)
# Afficher le commentaire sélectionné dans l'input text
text = st.text_area('Entrer le texte à analyser', value=selected_comment)
# Labels candidats pour la classification
candidate_labels = [1, 0]
# Modèle de phrase pour la formation de l'hypothèse
hypothesis_template = "This example is a {}."
# Ajouter un bouton pour déclencher l'analyse
if st.button("Analyser le texte"):
if text and candidate_labels:
result = classifier(text, candidate_labels, hypothesis_template=hypothesis_template)
if result['labels'][0] == 1:
st.info(f"Résults:good comments ,accuracy {result['scores'][0]*100:.2f}%")
if result['labels'][0] == 0:
st.info(f"Résults: bad comments,accuracy= {result['scores'][0]*100:.2f}%")
else:
st.write("Text Analysis.")
# Calculer les métriques de performance (vous devez ajuster ces lignes selon votre tâche)
inputs = df["text"].tolist()
true_labels = df["label"].tolist()
predictions = classifier(inputs, candidate_labels, hypothesis_template=hypothesis_template)
predicted_labels = [result['labels'][0] for result in predictions]
accuracy = accuracy_score(true_labels, predicted_labels)
precision = precision_score(true_labels, predicted_labels, average='binary')
recall = recall_score(true_labels, predicted_labels, average='binary')
f1 = f1_score(true_labels, predicted_labels, average='binary')
balanced_accuracy = balanced_accuracy_score(true_labels, predicted_labels)
# Afficher les métriques sous forme de tableau
st.header("Evaluation of our models")
metrics_df = pd.DataFrame({
"Métrique": ["Accuracy", "Precision", "Recall", "F1-score", "Balanced Accuracy"],
"Valeur": [accuracy, precision, recall, f1, balanced_accuracy]
})
st.table(metrics_df) |