Update app.py

app.py

@@ -2,12 +2,12 @@ import pandas as pd
 import numpy as np
 import nltk
 import re
-import string
 from nltk.corpus import stopwords
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import PassiveAggressiveClassifier
 import gradio as gr
+from transformers import pipeline
 
 # Download NLTK resources if not already downloaded
 nltk.download('stopwords')
@@ -46,12 +46,16 @@ tfidf_test = tfidf_vectorizer.transform(X_test)
 passive_aggressive = PassiveAggressiveClassifier()
 passive_aggressive.fit(tfidf_train, y_train)
 
-# Function for making predictions
+# Load the Hugging Face model
+classifier = pipeline("text-classification", model="distilbert-base-uncased")
+
+# Function for making predictions using the Hugging Face model
 def predict_disaster_tweets(text):
     cleaned_text = clean_tweet(text)
-    tfidf_text = tfidf_vectorizer.transform([cleaned_text])
-    prediction = passive_aggressive.predict(tfidf_text)[0]
-    return "Disaster Tweet" if prediction == 1 else "Normal Tweet"
+    prediction = classifier(cleaned_text)[0]
+    label = prediction['label']
+    score = prediction['score']
+    return f"Label: {label}, Score: {score}"
 
 # Gradio Interface setup
 iface = gr.Interface(