AdviceGenerator.py

from typing import List, Dict
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    AutoModelForCausalLM,
    pipeline
)

class AdviceGenerator(object):
    def __init__(self, llm):
        self.llm = llm
        self.role = {
            "role": "system",
            "content": (
                "You are a supportive assistant (not a mental health professional). "
                "Be concrete and tailor every response to the user's situation. "
                "Requirements:\n"
                "1) Begin with ONE empathetic sentence that mentions a key detail from the user's text (name, event, constraint).\n"
                "2) Then give 3–5 numbered, practical tips. Each tip must reference the user's situation (use names/keywords when present).\n"
                "3) If the user's text involves talking to someone (crush, friend, teacher, parent, boss), include a short **Script** block "
                "   with two options (in-person and text), customized with any names from the user's text.\n"
                "4) Add a **Try now (2 min)** micro-step.\n"
                "5) End with ONE targeted follow-up question that references the user's situation.\n"
                "Avoid platitudes and generic advice; avoid clinical instructions."
            ),
        }

    def generate_advice(
        self,
        disorder: str,
        user_text: str,
        history: List[Dict[str, str]] = None,
        max_history_msgs: int = 50,
        max_tokens: int = 600,        # give enough headroom
        temperature: float = 0.6,
        top_p: float = 0.9,
    ) -> Dict[str, str]:
        
        msgs = [self.role]

        # preserve rolling chat history if available
        if history:
            msgs.extend(history[-max_history_msgs:])

        # always append the new user input
        msgs.append({
            "role": "user",
            "content": (
                "Use the exact situation below to personalize your advice. "
                "Extract the main goal or barrier from the text and ground each tip in it.\n\n"
                f"Detected context: {disorder}\n"
                f"User text: {user_text}\n\n"
                "Follow the system instructions strictly. Do NOT ask vague questions first."
            ),
        })

        try:
            resp = self.llm.create_chat_completion(
                messages=msgs,
                temperature=temperature,
                top_p=top_p,
                max_tokens=max_tokens,
                stream=False,
            )
            text = resp["choices"][0]["message"]["content"].strip()
            return {"text": text}
        except Exception as e:
            return {"text": f"I'm here to listen. Could you tell me more about how \"{user_text}\" is affecting you?"}