from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

import pandas as pd
from fastapi import FastAPI
app=FastAPI()
splits = {'train': 'train_df.csv', 'validation': 'val_df.csv', 'test': 'test_df.csv'}
df = pd.read_csv("hf://datasets/Sp1786/multiclass-sentiment-analysis-dataset/" + splits["train"])

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

def predict_sentiment(texts):
    inputs = tokenizer(texts, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    sentiment_map = {0: "Very Negative", 1: "Negative", 2: "Neutral", 3: "Positive", 4: "Very Positive"}
    return [sentiment_map[p] for p in torch.argmax(probabilities, dim=-1).tolist()]

texts = [
    # English
    "I absolutely love the new design of this app!", "Cooking microwave pizzas, yummy.", "The weather is fine, nothing special.",
]

print(predict_sentiment(texts))