main.py

from fastapi import FastAPI
from pydantic import BaseModel
import torch
import torch.nn as nn
import pickle
from transformers import DebertaModel, DebertaTokenizer
import uvicorn

LABEL_COLUMNS = ['Red_Flag_Reason', 'Maker_Action', 'Escalation_Level',
                 'Risk_Category', 'Risk_Drivers', 'Investigation_Outcome']
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class InputText(BaseModel):
    text: str

with open("app/deberta_model.pkl", "rb") as f:
    checkpoint = pickle.load(f)

tokenizer = checkpoint['tokenizer']
label_encoders = checkpoint['label_encoders']

class DebertaMultiOutput(nn.Module):
    def __init__(self, num_labels_per_output):
        super().__init__()
        self.deberta = DebertaModel.from_pretrained("microsoft/deberta-base")
        self.dropout = nn.Dropout(0.3)
        self.classifiers = nn.ModuleList([
            nn.Linear(self.deberta.config.hidden_size, n_labels) for n_labels in num_labels_per_output
        ])

    def forward(self, input_ids, attention_mask):
        outputs = self.deberta(input_ids=input_ids, attention_mask=attention_mask)
        pooled = self.dropout(outputs.last_hidden_state[:, 0])
        return [classifier(pooled) for classifier in self.classifiers]

num_labels = [len(le.classes_) for le in label_encoders.values()]
model = DebertaMultiOutput(num_labels)
model.load_state_dict(checkpoint['model_state_dict'])
model.to(DEVICE)
model.eval()

app = FastAPI()

@app.get("/")
def root():
    return {"message": "🟢 DeBERTa multi-output classifier ready."}

@app.post("/predict")
def predict(input: InputText):
    inputs = tokenizer(input.text, return_tensors="pt", truncation=True, padding=True, max_length=128)
    input_ids = inputs['input_ids'].to(DEVICE)
    attention_mask = inputs['attention_mask'].to(DEVICE)

    with torch.no_grad():
        outputs = model(input_ids, attention_mask)

    preds = {}
    for output, col, le in zip(outputs, LABEL_COLUMNS, label_encoders.values()):
        pred_idx = torch.argmax(output, dim=1).item()
        pred_label = le.inverse_transform([pred_idx])[0]
        preds[col] = pred_label

    return preds