app.py

import gradio as gr
from transformers import TFDistilBertForSequenceClassification, DistilBertTokenizerFast
import tensorflow as tf

# Load the model and tokenizer from Hugging Face Hub
model_name = "Buebito/HamOrSpam_Model"  # Replace with your actual model path
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
model = TFDistilBertForSequenceClassification.from_pretrained(model_name)

def classify_text(text):
    # Tokenize the text input and prepare it for the model
    inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True, max_length=512)

    # Get model predictions
    predictions = model(inputs.data)[0]

    # Convert predictions to probabilities using softmax
    probabilities = tf.nn.softmax(predictions, axis=-1)

    # Get the higher probability index
    prediction_index = tf.argmax(probabilities, axis=-1).numpy()[0]

    # Convert the index to label
    label = "ham" if prediction_index == 0 else "spam"

    # Get the probabilities of each class
    ham_prob = probabilities[0][0].numpy()
    spam_prob = probabilities[0][1].numpy()

    # Return the label and the probabilities separately
    return label, {"ham": ham_prob, "spam": spam_prob}

# Create the Gradio interface
iface = gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(lines=2, placeholder="Enter Text Here..."),
    outputs=[
        gr.Label(label="Classification"),
        gr.JSON(label="Probabilities")
    ]
)

# Launch the app
iface.launch()