Update agent.py

agent.py

@@ -20,6 +20,13 @@ load_dotenv()
 DEFAULT_SCORING_API_URL = "https://agents-course-unit4-scoring.hf.space"
 DEFAULT_GEMINI_MODEL_ID = os.getenv("GEMINI_MODEL_ID", "gemini/gemini-2.5-flash")
 GEMINI_OPENAI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
+HTTP_SESSION = requests.Session()
+HTTP_SESSION.headers.update(
+    {
+        "User-Agent": "DukeDDrake1999-AgentCourse/1.0 (Hugging Face Space benchmark client)",
+        "Accept": "application/json, text/plain, */*",
+    }
+)
 GAIA_OUTPUT_PROMPT = """
 You are an evaluation-grade GAIA benchmark AI assistant. Your execution must be perfectly precise and highly deterministic. Your sole purpose is to isolate and output the exact, minimal final answer.
 
@@ -122,7 +129,7 @@ def _decode_text_bytes(payload: bytes) -> str:
 
 
 def _download_task_file(task_id: str) -> tuple[bytes, str]:
-    response = requests.get(f"{DEFAULT_SCORING_API_URL}/files/{task_id}", timeout=60)
+    response = HTTP_SESSION.get(f"{DEFAULT_SCORING_API_URL}/files/{task_id}", timeout=60)
     response.raise_for_status()
     content_type = response.headers.get("content-type", "application/octet-stream")
     return response.content, content_type
@@ -366,7 +373,7 @@ class WikiTitleFinder(Tool):
     output_type = "string"
 
     def forward(self, query: str) -> str:
-        response = requests.get(
+        response = HTTP_SESSION.get(
             "https://en.wikipedia.org/w/api.php",
             params={
                 "action": "query",
@@ -374,6 +381,7 @@ class WikiTitleFinder(Tool):
                 "srsearch": query,
                 "srlimit": 5,
                 "format": "json",
+                "origin": "*",
             },
             timeout=20,
         )
@@ -396,7 +404,7 @@ class WikiContentFetcher(Tool):
     output_type = "string"
 
     def forward(self, page_title: str) -> str:
-        response = requests.get(
+        response = HTTP_SESSION.get(
             "https://en.wikipedia.org/w/api.php",
             params={
                 "action": "query",
@@ -405,6 +413,7 @@ class WikiContentFetcher(Tool):
                 "redirects": 1,
                 "titles": page_title,
                 "format": "json",
+                "origin": "*",
             },
             timeout=20,
         )
@@ -419,7 +428,7 @@ class WikiContentFetcher(Tool):
 
 class GoogleSearchTool(Tool):
     name = "google_search"
-    description = "Search the live web using Gemini grounding with Google Search and return a concise result."
+    description = "Search the live web and return a concise result."
     inputs = {
         "query": {
             "type": "string",
@@ -429,16 +438,7 @@ class GoogleSearchTool(Tool):
     output_type = "string"
 
     def forward(self, query: str) -> str:
-        grounding_tool = genai_types.Tool(google_search=genai_types.GoogleSearch())
-        response = _genai_client().models.generate_content(
-            model=_normalize_model_id(),
-            contents=query,
-            config=genai_types.GenerateContentConfig(
-                temperature=0,
-                tools=[grounding_tool],
-            ),
-        )
-        return _extract_text(response)
+        return DuckDuckGoSearchTool().forward(query)
 
 
 class FileAttachmentQueryTool(Tool):