main.py

from fastapi import FastAPI, Request
from transformers import AutoModelForSequenceClassification, AutoConfig, RobertaTokenizer
from scipy.special import softmax
import numpy as np
import os

app = FastAPI()

# Set HF cache and home directory to writable path
os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf-cache"
os.environ["HF_HOME"] = "/tmp/hf-home"

# Model and tokenizer setup
MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
TOKENIZER_MODEL = "cardiffnlp/twitter-roberta-base-sentiment"

tokenizer = RobertaTokenizer.from_pretrained(TOKENIZER_MODEL)
config = AutoConfig.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

# Preprocessing
def preprocess(text):
    tokens = []
    for t in text.split():
        if t.startswith("@") and len(t) > 1:
            t = "@user"
        elif t.startswith("http"):
            t = "http"
        tokens.append(t)
    return " ".join(tokens)

# Endpoint
@app.post("/analyze")
async def analyze(request: Request):
    data = await request.json()
    text = preprocess(data.get("text", ""))

    encoded_input = tokenizer(text, return_tensors='pt')
    output = model(**encoded_input)
    scores = output[0][0].detach().numpy()
    scores = softmax(scores)

    ranking = np.argsort(scores)[::-1]
    result = []
    for i in ranking:
        label = config.id2label[i]
        score = round(float(scores[i]), 4)
        result.append({"label": label, "score": score})

    return {"result": result}