Create llm_utils.py

llm_utils.py

@@ -0,0 +1,90 @@
+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
+
+