File size: 1,049 Bytes
5608c5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import re
import nltk
import joblib
import gradio as gr
from nltk.corpus import stopwords
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
# Load saved model
model = joblib.load("sentiment_model.pkl")
vectorizer = joblib.load("tfidf_vectorizer.pkl")
# Clean text
def clean_text(text):
text = text.lower()
text = re.sub(r'[^a-z\s]', '', text)
words = text.split()
words = [w for w in words if w not in stop_words]
return " ".join(words)
# Prediction
def predict_sentiment(text):
cleaned = clean_text(text)
vectorized = vectorizer.transform([cleaned])
return model.predict(vectorized)[0]
# Gradio UI
demo = gr.Interface(
fn=predict_sentiment,
inputs=gr.Textbox(
lines=4,
placeholder="Enter tweet text here...",
label="Tweet Text"
),
outputs=gr.Label(label="Predicted Sentiment"),
title="Tweet Sentiment Analysis",
description="Sentiment classification using a pre-trained ML model."
)
demo.launch()
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