Update app.py

app.py

@@ -5,7 +5,7 @@ import requests
 import pandas as pd
 
 from huggingface_hub import InferenceClient
-from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -70,53 +70,59 @@ SYSTEM_PROMPT = (
 # =========================================================
 
 class BasicAgent:
-    def __init__(self):
-        print("Initializing GAIA Agent...")
-
-        HF_TOKEN = os.getenv("HF_TOKEN")
-        print("HF_TOKEN exists?", HF_TOKEN is not None)
+    """
+    Agente usando smolagents + HfApiModel + DuckDuckGoSearchTool,
+    ajustado para o cenário do GAIA (EXACT MATCH).
+    """
 
-        if not HF_TOKEN:
-            raise ValueError("❌ HF_TOKEN not found — create a Secret in your Space!")
+    def __init__(self):
+        print("Initializing GAIA agent with HfApiModel + DuckDuckGoSearchTool...")
 
+        # ferramenta de busca da própria smolagents (usa ddgs por baixo)
         self.search_tool = DuckDuckGoSearchTool()
 
-        self.client = InferenceClient(
-            model="mistralai/Mixtral-8x7B-Instruct-v0.1",
-            token=HF_TOKEN,
-        )
-
-        self.model = InferenceClientModel(
-            client=self.client,
-            system_prompt="Answer ONLY with the final answer. No steps, no explanation."
+        # Modelo chamado via API do Hugging Face
+        # Você pode trocar o model_id se o curso recomendar outro
+        self.model = HfApiModel(
+            model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+            max_new_tokens=256,
+            temperature=0.2,
         )
 
+        # CodeAgent: agente que consegue chamar tools e escrever código
         self.agent = CodeAgent(
             model=self.model,
             tools=[self.search_tool],
-            max_steps=5,
+            add_base_tools=False,  # deixamos só a search tool pra ficar simples
         )
 
     def __call__(self, question: str) -> str:
-        print(f"\n=== NEW QUESTION ===\n{question}\n")
+        print(f"Processing question: {question[:80]}...")
+
+        # Instruções de EXACT MATCH embutidas no prompt
+        prompt = (
+            "You are solving GAIA Level 1 questions.\n"
+            "You have access to a web search tool and MUST use it whenever needed "
+            "to obtain accurate, up-to-date information.\n"
+            "RULES:\n"
+            " - Return ONLY the final answer as a short string.\n"
+            " - NO explanations.\n"
+            " - NO 'Final answer', 'Answer:', etc.\n"
+            " - If the answer is a number, output just the number.\n"
+            "Question:\n"
+            f"{question}\n"
+        )
 
         try:
-            raw = self.agent.run(
-                f"Answer this question exactly. Only answer:\n{question}"
-            )
-            print("RAW MODEL RESPONSE:", raw)
-
-            if not raw or raw.strip() == "":
-                print("⚠️ MODEL RETURNED EMPTY STRING ⚠️")
-                return ""
-
-            return clean_answer(raw)
-
+            raw = self.agent.run(prompt)
+            final = clean_answer(raw)
+            print(f"Raw answer: {raw!r}")
+            print(f"Final cleaned answer: {final!r}")
+            return final
         except Exception as e:
-            print("ERROR:", e)
+            print(f"Error while running agent: {e}")
             return ""
 
-
 # =========================================================
 #  Runner + submit (mantido do template, usando BasicAgent novo)
 # =========================================================