app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the tokenizer and model from local path (or HF if internet is available)
model = AutoModelForSequenceClassification.from_pretrained("ogflash/yelp_review_classifier")
tokenizer = AutoTokenizer.from_pretrained("ogflash/yelp_review_classifier")

# Prediction function
def classify(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

    # Remove token_type_ids if using DistilBERT
    if "token_type_ids" in inputs:
        inputs.pop("token_type_ids")

    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class_id = torch.argmax(logits, dim=1).item()
    score = torch.softmax(logits, dim=1)[0][predicted_class_id].item()

    # Map labels using if-elif-else
    label = f"LABEL_{predicted_class_id}"
    if label == "LABEL_0":
        label_name = "Negative"
    elif label == "LABEL_1":
        label_name = "Neutral"
    elif label == "LABEL_2":
        label_name = "Positive"
    else:
        label_name = label  # fallback

    return f"{label_name} ({score * 100:.2f}%)"

# Gradio UI
iface = gr.Interface(fn=classify,
                     inputs=gr.Textbox(lines=2, placeholder="Enter your review here..."),
                     outputs="text",
                     title="Sentiment Classifier",
                     description="Classifies text into Positive, Neutral, or Negative.")

iface.launch()