app.py

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

# Load model
MODEL_NAME = "tabularisai/multilingual-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

# Inference function
def analyze_sentiment(user_input):
    inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    prediction = torch.argmax(probs, dim=-1).item()
    labels = ["Very Negative", "Negative", "Neutral", "Positive", "Very Positive"]
    return labels[prediction]

# Define Gradio Interface
demo = gr.Interface(fn=analyze_sentiment, inputs="text", outputs="text")

# This must be present for Hugging Face to work
demo.launch()