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import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import os
MODEL_NAME = "distilbert-base-uncased"
SAVE_PATH = "./data/best_model"
LABEL_MAP = {0: "low risk", 1: "medium risk", 2: "high risk"}
# Load tokenizer from saved fine-tuned model if available
tokenizer = AutoTokenizer.from_pretrained(SAVE_PATH if os.path.isdir(SAVE_PATH) else MODEL_NAME)
_model = None # ← this stores the model in memory
def load_model():
global _model
if _model is not None:
return _model # ✅ already loaded
model_path = os.path.join(SAVE_PATH, "pytorch_model.bin")
if os.path.exists(model_path):
print("✅ Loading fine-tuned model...")
_model = AutoModelForSequenceClassification.from_pretrained(SAVE_PATH)
else:
print("⚠️ Fine-tuned model not found. Falling back to base model:", MODEL_NAME)
_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=3)
_model.eval()
return _model
def predict(input_data: str):
model = load_model()
with torch.no_grad():
inputs = tokenizer(input_data, return_tensors="pt", truncation=True, padding=True, max_length=256)
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=1).item()
return LABEL_MAP.get(predicted_class, "unknown")
# import torch
# from transformers import AutoTokenizer, AutoModelForSequenceClassification
# import os
# MODEL_NAME = "google/gemma-1.1-2b-it"
# SAVE_PATH = "./data/best_model"
# LABEL_MAP = {0: "low risk", 1: "medium risk", 2: "high risk"}
# # Load tokenizer from saved model if available
# if os.path.exists(SAVE_PATH):
# tokenizer = AutoTokenizer.from_pretrained(SAVE_PATH)
# else:
# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# def load_model():
# model_path = os.path.join(SAVE_PATH, "best_model")
# if os.path.exists(model_path):
# print("✅ Loading fine-tuned model from:", model_path)
# model = AutoModelForSequenceClassification.from_pretrained(model_path)
# else:
# print("🔁 Loading base model (not yet fine-tuned):", MODEL_NAME)
# model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=3)
# return model
# def predict(input_data: str):
# model = load_model()
# model.eval()
# with torch.no_grad():
# inputs = tokenizer(input_data, return_tensors="pt", truncation=True, padding=True, max_length=256)
# outputs = model(**inputs)
# predicted_class = torch.argmax(outputs.logits, dim=1).item()
# return LABEL_MAP.get(predicted_class, "unknown")
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