from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch
import gradio as gr
import os

# Load the pre-trained model and tokenizer from the root directory
model_path = "."  # Root directory
print(f"Checking files in {model_path}: {os.listdir(model_path)}")  # Debug: List files in root
tokenizer = DistilBertTokenizer.from_pretrained(model_path)
model = DistilBertForSequenceClassification.from_pretrained(model_path)

# Prediction function
def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    inputs = {key: val.to(device) for key, val in inputs.items()}
    model.to(device)
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()
    return "Positive" if predicted_class == 1 else "Negative"

# Create Gradio interface
interface = gr.Interface(fn=predict_sentiment, inputs="text", outputs="text", title="Amazon Sentiment Analysis Demo")
interface.launch(share=True)