Update app.py

app.py

@@ -14,9 +14,6 @@ import pytz
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# ==============================================================================
-# RULE BASED SOLVER (FAST + FREE)
-# ==============================================================================
 
 def rule_solver(question: str):
 
@@ -48,20 +45,22 @@ def rule_solver(question: str):
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-# ==============================================================================
-# SMART BASIC AGENT (FREE + STABLE)
-# ==============================================================================
 
 class BasicAgent:
 
     def __init__(self):
         print("Smart Agent initialized.")
 
-        # FREE HuggingFace inference (stable small model)
-        self.client = InferenceClient(
-            model="HuggingFaceH4/zephyr-7b-beta",
-            token=os.getenv("HF_TOKEN")
-        )
+        self.llm_available = True
+
+        try:
+            self.client = InferenceClient(
+                model="microsoft/Phi-3-mini-4k-instruct",
+                token=os.getenv("HF_TOKEN")
+            )
+        except Exception as e:
+            print("LLM init failed:", e)
+            self.llm_available = False
 
     def clean_answer(self, text: str) -> str:
         text = text.strip()
@@ -73,45 +72,42 @@ class BasicAgent:
 
     def llm_fallback(self, question: str):
 
-        messages = [
-            {
-                "role": "system",
-                "content": (
-                    "Return ONLY the final answer. "
-                    "No explanation. One short line."
-                ),
-            },
-            {"role": "user", "content": question},
-        ]
-
         completion = self.client.chat_completion(
-            messages=messages,
+            messages=[
+                {"role": "system",
+                 "content": "Return ONLY the final answer. No explanation."},
+                {"role": "user", "content": question},
+            ],
             max_tokens=80,
             temperature=0.1,
         )
 
         return completion.choices[0].message.content
 
-    def __call__(self, question: str) -> str:
+    def __call__(self, question: str):
 
         print("Solving question...")
 
-        # ✅ STEP 1 — try rule solver first
+        # ✅ RULE ENGINE FIRST
         rule_answer = rule_solver(question)
         if rule_answer:
-            print("Solved by rule engine:", rule_answer)
+            print("Solved by rules:", rule_answer)
             return rule_answer
 
-        # ✅ STEP 2 — LLM fallback
+        # ✅ IF LLM ALREADY FAILED → SKIP
+        if not self.llm_available:
+            return "Unknown"
+
+        # ✅ TRY LLM ONLY ONCE
         try:
             response = self.llm_fallback(question)
             answer = self.clean_answer(response)
-            print("LLM Answer:", answer)
             return answer
-        except Exception as e:
-            print("LLM failed:", e)
-            return "Error"
 
+        except Exception as e:
+            print("LLM disabled (provider unavailable):", e)
+            self.llm_available = False
+            return "Unknown"