Oracle/SmolLM.py

from transformers import AutoTokenizer, AutoModelForCausalLM

class SmolLM:
    def __init__(self, model_path="HuggingFaceTB/SmolLM2-1.7B-Instruct"):
        self.available = True
        try:
            print(f"[INFO] Loading Oracle tokenizer from {model_path}")
            self.tokenizer = AutoTokenizer.from_pretrained(model_path)
            print(f"[INFO] Loading Oracle from {model_path}")
            self.model = AutoModelForCausalLM.from_pretrained(model_path)
            print("[INFO] Oracle loaded successfully")
        except Exception as e:
            print(f"[ERROR] Failed to load model '{model_path}': {e}")
            self.available = False

    def predict(self, prompt,max_length=512,max_new_tokens=150):
        if not self.available:
            print("[WARN] Oracle unavailable, returning default weight 0.5")
            return "0.5"
        try:
            print(f"[INFO] Generating response for prompt: {prompt[:100]}...", flush=True)
            inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=max_length)
            outputs = self.model.generate(**inputs, max_length=inputs["input_ids"].shape[1]+max_new_tokens,num_return_sequences=1)
            response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            print(f"[INFO] Generated response: {response[:100]}...", flush=True)
            return response
        except Exception as e:
            print(f"[ERROR] Oracle has failed: {e}")
            return "0.5"