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import streamlit as st
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score, classification_report
def model3():
# Load the CSV file
df = pd.read_csv('Emotion_classify_Data.csv')
# Assuming your CSV has two columns: 'text' and 'label'
X = df['Comment']
y = df['Emotion']
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a text classification pipeline using a bag-of-words model and a Naive Bayes classifier
model = make_pipeline(CountVectorizer(), MultinomialNB())
# Train the model
model.fit(X_train, y_train)
# Function to make predictions
def predict_emotion(text):
prediction = model.predict([text])
return prediction[0]
# Streamlit app
st.title("Emotion Classification App")
# User input for prediction
user_input = st.text_area("Enter a sentence:")
if st.button("Predict"):
if user_input:
# Make prediction
prediction = predict_emotion(user_input)
st.success(f"Predicted Emotion: {prediction}")
else:
st.warning("Please enter a sentence for prediction.")
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