ai summary added

backend/app/main.py

@@ -14,6 +14,13 @@
 from fastapi.middleware.gzip import GZipMiddleware
 from fastapi.responses import JSONResponse
 from fastapi.staticfiles import StaticFiles
+from dotenv import load_dotenv
+load_dotenv()
+import time
+import os
+from collections import defaultdict
+import logging
+from contextlib import asynccontextmanager
 
 from .observability import initialise_app_info, prometheus_metrics_middleware
 from .routers import analyze, auth, chat, collaboration, debugging, explanation

backend/app/routers/explanation.py

@@ -12,4 +12,4 @@
 )
 async def explain(req: CodeRequest):
     lang = detect_language(req.code, req.language)
-    return run_explanation(req.code, lang)
+    return await run_explanation(req.code, lang)

backend/app/services/ai_provider.py

@@ -22,6 +22,9 @@
 LLM_MAX_RETRIES = int(os.getenv("LLM_MAX_RETRIES", "3"))
 LLM_RETRY_BACKOFF = float(os.getenv("LLM_RETRY_BACKOFF", "1.0"))
 
+print("LLM_ENABLED =", LLM_ENABLED)
+print("LLM_API_KEY exists =", bool(LLM_API_KEY))
+
 def _get_provider_name(base_url: str) -> str:
     parsed = urlparse(base_url)
     hostname = parsed.hostname or ""

backend/app/services/code_assistant.py

@@ -9,6 +9,7 @@
 import time
 from .ast_analyzer import analyze as ast_analyze
 from dataclasses import dataclass, field
+from .ai_provider import call_llm, is_enabled
 
 # ── Language Detection ─────────────────────────────────────────────────────────
 LANG_SIGNATURES: dict[str, list[str]] = {
@@ -1130,7 +1131,7 @@ def run_suggestions(code: str, language: str) -> dict:
 
 
 # ── Explanation Engine ─────────────────────────────────────────────────────────
-def run_explanation(code: str, language: str) -> dict:
+async def run_explanation(code: str, language: str) -> dict:
     """Generate a plain-English explanation of the provided source code.
 
     Args:
@@ -1199,10 +1200,40 @@ def run_explanation(code: str, language: str) -> dict:
         "Advanced": f"A well-structured {language} codebase with {len(class_names)} class(es) and {len(funcs)} function(s). Shows advanced design patterns.",
         "Expert": f"A large-scale {language} system ({len(lines)} lines). Expert-level architecture with significant abstraction layers.",
     }
+
+    summary = summaries.get(complexity, f"A {language} code snippet.")
+
+    if is_enabled():
+
+        system_prompt = (
+            "You are a code summarization assistant. "
+            "Generate a concise one-sentence summary of what the code does."
+        )
+
+        user_prompt = f"""
+        Language: {language}
+
+        Code:
+        {code}
+
+        Return only the summary.
+        """
+
+        if is_enabled():
+            ai_summary = await call_llm(system_prompt, user_prompt)
+            print("AI SUMMARY:", ai_summary)        # ← already there
+            print("ENTERED RUN_EXPLANATION")         # ← already there  
+            print("LLM enabled?", is_enabled())      # ← already there
+
+        if ai_summary:
+            summary = ai_summary
+
+
+
 
     return {
         "language": language,
-        "summary": summaries.get(complexity, f"A {language} code snippet."),
+        "summary": summary,
         "key_points": key_points,
         "complexity": complexity,
         "line_count": len(lines),