app.py

import gradio as gr
from transformers import AutoTokenizer
import onnxruntime as ort
import numpy as np

# Load tokenizer and ONNX quantized model
tokenizer = AutoTokenizer.from_pretrained("onnx/")
session = ort.InferenceSession("onnx/model_quantized.onnx")

# Softmax function
def softmax(x):
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum()

# Prediction function
def classify_sentiment(text):
    # Tokenize the input text
    inputs = tokenizer(text, return_tensors="np")
    #print(inputs)
    # Run inference
    outputs = session.run(None, {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"]
    })

    # Process logits
    logits = outputs[0][0]
    probs = softmax(logits)
    pred_class = int(np.argmax(probs))
    
    label_map = {0: "Negative", 1: "Positive"}
    print(label_map[pred_class])
    return label_map[pred_class]
     

# Gradio Interface
interface = gr.Interface(
    fn=classify_sentiment,
    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
    outputs='label',
    title="Sentiment Classifier",
    description="Enter a sentence to classify its sentiment",
)

# Launch the app
if __name__ == "__main__":
    interface.launch(share=True)