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| 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=20 | |
| ) | |
| result = response.json() | |
| if isinstance(result, dict): | |
| return result.get("cleaned_latex", bad_input) | |
| return bad_input | |
| except: | |
| return bad_input | |