app.py

# Imports
import gradio as gr
from sklearn.linear_model import LogisticRegression
import pickle5 as pickle
import re
import string
import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer

# file name
#lr_filename = 'lr_021223.pkl'

# Load model from pickle file
# model = pickle.load(open(lr_filename, 'rb'))


# Process input text, including removing stopwords, converting to lowercase, and removing punctuation
stop = stopwords.words('english')
def process_text(text):
    text = [word for word in text.split() if word not in stop]
    text = str(text).lower()
    text = re.sub(
        f"[{re.escape(string.punctuation)}]", " ", text
    )
    text = " ".join(text.split())
    return text

# Vectorize input text
vec = CountVectorizer()
'''
def vectorize_text(text):
    #text = process_text(text)
    #text = vectorizer.fit_transform([text])
    #return text
'''

# Valid input for the model so number of features match
def predict(text):
    # Load the pickled model
    filename = 'lr_021223.pkl'
    loaded_model = pickle.load(open(filename, 'rb'))
    text = process_text(text)
    text = vec.transform([text])
    prediction = loaded_model.predict(text)
    return prediction

'''
Prediction function
#def predict(text):
    #text = vectorize_text(text)
    #prediction = model.predict(text)
    #return prediction
'''

# Define interface
demo = gr.Interface(fn=predict,
                        title="Text Classification Demo",
                        description="This is a demo of a text classification model using Logistic Regression.",
                        inputs=gr.Textbox(lines=10, placeholder='Input text here...', label="Input Text"),
                        outputs=gr.Textbox(label="Predicted Label: Other: 0, Healthcare: 1, Technology: 2", lines=2, placeholder='Predicted label will appear here...'),
                        allow_flagging='never'
)

demo.launch()