app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModel
import torch
import os

app = FastAPI()

# Load Hugging Face Token
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("❌ Hugging Face API token not found! Set HF_TOKEN as an environment variable.")

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("mental/mental-bert-base-uncased", token=HF_TOKEN)
model = AutoModel.from_pretrained("mental/mental-bert-base-uncased", token=HF_TOKEN)
model.eval()  # Set model to evaluation mode

# Request body schema
class TextRequest(BaseModel):
    text: str

# Helper function to compute embedding
def compute_embedding(text: str) -> list[float]:
    """Generate a sentence embedding using mean pooling on MentalBERT output."""
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    embedding = outputs.last_hidden_state.mean(dim=1).squeeze()
    return embedding.tolist()

# POST endpoint to return embedding
@app.post("/embed")
def get_embedding(request: TextRequest):
    text = request.text.strip()
    if not text:
        raise HTTPException(status_code=400, detail="Input text cannot be empty.")
    
    try:
        embedding = compute_embedding(text)
        return {"embedding": embedding}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error computing embedding: {str(e)}")