app.py

import re
import ast
import operator
import gradio as gr

# LLM: small, CPU-friendly
from transformers import pipeline

LLM = None  # lazy-load to speed up app boot


# ---------- Agent 1: Planner (decides the next action) ----------
def planner_agent(user_msg: str) -> dict:
    """
    Very small heuristic planner:
    - If it's a simple math question/expression => CALCULATE
    - Else => ANSWER (LLM)
    """
    text = user_msg.strip().lower()

    # If it looks like a calculation
    math_like = bool(re.search(r"[0-9][0-9\.\s\+\-\*\/\%\^\(\)]*[0-9\)]", text))
    asks_calc = any(k in text for k in ["calc", "calculate", "evaluate", "what is", "what's"])

    if math_like or asks_calc:
        # Avoid "story" or "explain" with numbers
        if not any(k in text for k in ["story", "poem", "explain why", "compare"]):
            return {"action": "CALCULATE", "reason": "Looks like a numeric expression or calculation request."}

    return {"action": "ANSWER", "reason": "General question; better handled by the LLM."}


# ---------- Tiny safe calculator (tool used by Solver) ----------
# Safe AST-based evaluator supporting +,-,*,/,**,%,// and parentheses
OPS = {
    ast.Add: operator.add,
    ast.Sub: operator.sub,
    ast.Mult: operator.mul,
    ast.Div: operator.truediv,
    ast.Pow: operator.pow,
    ast.Mod: operator.mod,
    ast.FloorDiv: operator.floordiv,
    ast.USub: operator.neg,
    ast.UAdd: operator.pos,
}

def _eval(node):
    if isinstance(node, ast.Num):  # py<3.8 fallback
        return node.n
    if isinstance(node, ast.Constant):
        if isinstance(node.value, (int, float)):
            return node.value
        raise ValueError("Only numeric constants allowed.")
    if isinstance(node, ast.BinOp):
        left = _eval(node.left)
        right = _eval(node.right)
        op_type = type(node.op)
        if op_type in OPS:
            return OPS[op_type](left, right)
        raise ValueError("Unsupported operator.")
    if isinstance(node, ast.UnaryOp) and type(node.op) in OPS:
        return OPS[type(node.op)](_eval(node.operand))
    if isinstance(node, ast.Expression):
        return _eval(node.body)
    raise ValueError("Unsupported expression.")

def safe_calculate(expr: str) -> str:
    try:
        tree = ast.parse(expr, mode="eval")
        val = _eval(tree)
        return str(val)
    except Exception as e:
        return f"Sorry, I couldn't calculate that: {e}"


# ---------- Agent 2: Writer (LLM or Tool) ----------
def get_llm():
    global LLM
    if LLM is None:
        # flan-t5-small is ~80M params, OK on CPU Basic
        LLM = pipeline("text2text-generation", model="google/flan-t5-small")
    return LLM

def writer_agent(user_msg: str, plan: dict) -> str:
    if plan["action"] == "CALCULATE":
        # Extract the most likely expression from the message
        # Keep digits, ops, dots, spaces, and parentheses
        expr = "".join(ch for ch in user_msg if ch in "0123456789.+-*/()%^ //")
        # Clean up accidental double spaces
        expr = re.sub(r"\s+", "", expr.replace("^", "**"))
        if not expr:
            # fallback to LLM if no expression found
            llm = get_llm()
            prompt = f"Answer briefly:\n\nQuestion: {user_msg}\nAnswer:"
            out = llm(prompt, max_new_tokens=128, do_sample=True)[0]["generated_text"]
            return out.strip()
        return safe_calculate(expr)

    # ANSWER with LLM
    llm = get_llm()
    prompt = (
        "You are a concise, friendly assistant. "
        "Answer clearly in 1-4 sentences.\n\n"
        f"Question: {user_msg}\nAnswer:"
    )
    out = llm(prompt, max_new_tokens=192, do_sample=True, temperature=0.6, top_p=0.95)[0]["generated_text"]
    return out.strip()


# ---------- Gradio Chat glue ----------
def chat_fn(message, history, show_agent_trace=False):
    plan = planner_agent(message)
    answer = writer_agent(message, plan)

    if show_agent_trace:
        trace = f"\n\n---\n*Agent trace:* action = **{plan['action']}**, reason = _{plan['reason']}_"
        return answer + trace
    return answer


demo = gr.ChatInterface(
    fn=chat_fn,
    additional_inputs=[
        gr.Checkbox(label="Show agent trace", value=False),
    ],
    theme="soft",
    css="""
    .gradio-container {max-width: 760px !important}
    """,
)

if __name__ == "__main__":
    demo.launch()