app.py

import joblib
import gradio as gr
import pickle
import numpy
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report

# Load the model and vectorizer
with open('sentiment_classifier_model.pickle', 'rb') as f:
    model = pickle.load(f)

with open('tfidf_vectorizer.pickle', 'rb') as f:
    vectorizer = pickle.load(f)


# model = joblib.load('sentiment_classifier_model.joblib')
# vectorizer = joblib.load('tfidf_vectorizer.joblib')

def predict(text):
    text_vector = vectorizer.transform([text])
    sentiment = model.predict(text_vector)
    return sentiment[0]

interface = gr.Interface(fn=predict, inputs='text', outputs='text', title="Sentiment Classifier")

# Launch the interface
interface.launch()