llm_utils.py

import requests
import yaml

# === Load theorems.yaml and render it into clean context text for LLM ===
def load_theorem_context(yaml_path="theorems.yaml"):
    with open(yaml_path, 'r') as f:
        data = yaml.safe_load(f)

    if not isinstance(data, dict):
        return "⚠️ Invalid theorems format in YAML."

    context_lines = []
    for idx, th in enumerate(data.get('theorems', []), 1):
        context_lines.append(
            f"**Theorem {idx}: {th.get('name', 'Unnamed')}**\n"
            f"- **Statement**: {th.get('statement', 'N/A')}\n"
            f"- **Tags**: {', '.join(th.get('tags', []))}\n"
            f"- **When to Use**: {th.get('when_to_use', 'N/A')}\n"
            f"- **Short Explanation**: {th.get('short_explanation', 'N/A')}\n"
        )
        context_lines.append('---')

    return "\n".join(context_lines)

# === Build prompt using user steps and theorem context ===
def build_prompt(equation_type, solution_text, theorem_context):
    return (
        f"You are a helpful math tutor. Below is a theorem reference database.\n\n"
        f"Each theorem includes:\n"
        f"- Name\n- Statement\n- Tags\n- When to use\n- Short Explanation\n\n"
        f"---\n\n"
        f"### 📘 Theorem Database:\n\n"
        f"{theorem_context}\n\n"
        f"---\n\n"
        f"### 🧮 User Steps for solving a {equation_type} equation:\n\n"
        f"{solution_text}\n\n"
        f"---\n\n"
        f"### 🎯 Task:\n"
        f"Explain each solution step clearly.\n"
        f"Use relevant theorems by number or name.\n"
        f"Make it understandable to a smart high school student.\n"
        f"Focus on reasoning, not just restating the steps or theorems."
    )

# === Request LLM explanation ===
def explain_with_llm(solution_text, equation_type, llm_url, yaml_path="theorems.yaml"):
    try:
        if not llm_url or not llm_url.strip().startswith("http"):
            return "❌ Invalid or missing LLM URL."

        theorem_context = load_theorem_context(yaml_path)
        prompt = build_prompt(equation_type, solution_text, theorem_context)

        response = requests.post(
            f"{llm_url.strip()}/explain",
            json={"prompt": prompt},
            timeout=90
        )

        if response.status_code == 200:
            result = response.json()

            if isinstance(result, dict):
                return result.get("explanation", "❌ No explanation returned.")
            elif isinstance(result, list):
                return result[0] if result else "❌ Empty response list."
            else:
                return f"❌ Unexpected LLM response format: {type(result)}"

        return f"❌ LLM request failed: {response.status_code}"

    except Exception as e:
        return f"❌ LLM Error: {e}"

# === Request fallback if parsing failed ===
def request_llm_fallback(bad_input, llm_url):
    try:
        response = requests.post(
            f"{llm_url.strip()}/clean",
            json={"prompt": bad_input},
            timeout=90
        )
        result = response.json()
        if isinstance(result, dict):
            return result.get("cleaned_latex", bad_input)
        return bad_input
    except:
        return bad_input