app.py

import gradio as gr
import pickle
import sys
import subprocess

subprocess.check_call([sys.executable, '-m', 'pip', 'install', 
'xgboost', 'sklearn', 'pymystem3', 'pandas'])

import pandas as pd
from sklearn.utils import shuffle
from sklearn.feature_extraction.text import TfidfVectorizer
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
import pymystem3
from tqdm import tqdm

# Load the model from the file
with open('xgb_model.pkl', 'rb') as file:
    model = pickle.load(file)

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


def predict(text):
    # Transform the text using the loaded vectorizer
    text_transformed = vectorizer.transform([text])

    # Make prediction using the loaded model
    prediction = model.predict(text_transformed)[0]

    return prediction


# Create a Gradio interface
input_text = gr.inputs.Textbox(label="Input Text")
output_text = gr.outputs.Textbox(label="Prediction")

gr.Interface(fn=predict, inputs=input_text, outputs=output_text).launch()