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

MODEL_NAME = "Ak47-model-ml/Bert-Sentiment"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    
    with torch.no_grad():
        outputs = model(**inputs)
    
    probs = torch.softmax(outputs.logits, dim=1)[0].tolist()
    labels = ["Negative", "Positive"]
    
    return {labels[i]: probs[i] for i in range(len(labels))}

gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=4, placeholder="Enter text here..."),
    outputs=gr.Label(num_top_classes=2),
    title="BERT Sentiment Analyzer",
    description="Real-time sentiment prediction using fine-tuned BERT model"
).launch()