baseline

app.py

@@ -4,7 +4,8 @@ import requests
 import inspect
 import pandas as pd
 import spaces
-
+from langchain_openai import ChatOpenAI
+from langchain.agents import create_agent
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -14,14 +15,73 @@ def zerogpu_function():
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 
+# class BasicAgent:
+#     def __init__(self):
+#         print("BasicAgent initialized.")
+#     def __call__(self, question: str) -> str:
+#         print(f"Agent received question (first 50 chars): {question[:50]}...")
+#         fixed_answer = "This is a default answer."
+#         print(f"Agent returning fixed answer: {fixed_answer}")
+#         return fixed_answer
+
+model = ChatOpenAI(
+    model="qwen-plus",
+    api_key=os.environ["DASHSCOPE_API_KEY"],
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    temperature=0,
+)
+
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
+
+        agent_model = model
+
+        self.agent = create_agent(
+            model=agent_model,
+            tools=[],
+            system_prompt="""
+You are solving GAIA benchmark questions.
+
+You have NO external tools.
+You cannot browse the web.
+You cannot access files or attachments.
+You cannot use a calculator or Python.
+
+Solve each question using only your internal knowledge and reasoning.
+
+IMPORTANT:
+- Return only the final answer.
+- Do not explain your reasoning.
+- Do not include phrases such as "FINAL ANSWER".
+- Follow the exact output format requested by the question.
+"""
+        )
+
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        print(f"Agent received question: {question[:100]}...")
+
+        result = self.agent.invoke({
+            "messages": [
+                {
+                    "role": "user",
+                    "content": question
+                }
+            ]
+        })
+
+        answer = result["messages"][-1].content
+
+        print(f"Agent answer: {answer}")
+
+        return answer
+
+
+
+
+
+
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

show_questions.py

@@ -0,0 +1,19 @@
+import requests
+
+API_URL = "https://agents-course-unit4-scoring.hf.space"
+QUESTIONS_URL = f"{API_URL}/questions"
+
+response = requests.get(QUESTIONS_URL, timeout=30)
+response.raise_for_status()
+
+questions = response.json()
+
+print(f"总题数: {len(questions)}\n")
+
+for i, item in enumerate(questions, start=1):
+    print("=" * 80)
+    print(f"第 {i} 题")
+    print(f"task_id: {item.get('task_id')}")
+    print(f"question: {item.get('question')}")
+    print(f"file_name: {item.get('file_name', '')}")
+    print()