qwen

agents/agent.py

@@ -16,29 +16,6 @@ from dotenv import load_dotenv
 load_dotenv()
 
 
-# Question type analyzer function
-def analyze_question_type(question: str):
-    """Analyze question type and determine appropriate tool strategy"""
-    question_lower = question.lower()
-    
-    # Questions requiring web search
-    if any(word in question_lower for word in ["wikipedia", "arxiv", "article", "paper", "website"]):
-        return "web_search_required"
-    
-    # Math questions
-    if any(word in question_lower for word in ["calculate", "how many", "percentage", "multiply", "divide"]):
-        return "math_required"
-    
-    # File processing questions
-    if any(word in question_lower for word in ["file", "spreadsheet", "csv", "excel", "attached"]):
-        return "file_processing_required"
-    
-    # Image analysis questions
-    if any(word in question_lower for word in ["image", "picture", "photo", "visual"]):
-        return "image_analysis_required"
-    
-    return "general"
-
 
 # load the system prompt from the file
 with open("system_prompt.txt", "r", encoding="utf-8") as f:
@@ -84,71 +61,31 @@ def build_graph():
                     max_tokens=8192,
                     timeout=120
                     ) 
-
     # Bind tools to LLM
     llm_with_tools = llm.bind_tools(tools) 
 
-    # Node
+
+
+    # Nodes
     def assistant(state: MessagesState):
         """Assistant node with GAIA format compliance"""
         response = llm_with_tools.invoke(state["messages"])
         return {"messages": [response]}
-        
-        # Extract pure answer for GAIA format
-        content = response.content
-        
-        # Look for final answer in various formats
-        if "Final Answer:" in content or "Final answer:" in content:
-            parts = content.split("Final Answer:" if "Final Answer:" in content else "Final answer:")
-            final_answer = parts[-1].strip()
-        elif "The answer is:" in content:
-            final_answer = content.split("The answer is:")[-1].strip()
-        elif "Therefore:" in content:
-            final_answer = content.split("Therefore:")[-1].strip()
-        else:
-            # If no explicit format, try to extract the last line/sentence
-            lines = content.strip().split('\n')
-            final_answer = lines[-1].strip()
-        
-        # Clean the answer (remove explanations)
-        if '.' in final_answer:
-            # Take only first sentence if multiple sentences
-            final_answer = final_answer.split('.')[0].strip() + '.'
-        
-        # Return clean answer
-        response.content = final_answer
-        return {"messages": [response]}
     
     
 
     def retriever(state: MessagesState):
-        """Retriever node with question type analysis"""
-        question = state["messages"][0].content
-        question_type = analyze_question_type(question)
-        
-        similar_question = vector_store.similarity_search(question)
-        
-        # Add specific instructions based on question type
-        type_instructions = {
-            "web_search_required": "Use web_search tool to find specific information. Be precise with search queries.",
-            "math_required": "Use math tools (add, subtract, multiply, divide) for ALL calculations. Show your work.",
-            "file_processing_required": "Use file reading tools to process attached files. Extract exact data.",
-            "image_analysis_required": "Use image analysis tools to examine visual content carefully.",
-            "general": "Think step by step and use appropriate tools."
-        }
-        
-        instruction_msg = HumanMessage(
-            content=f"Question type: {question_type}. {type_instructions[question_type]}"
-        )
-        
-        if similar_question:
+        """Retriever node"""
+        similar_question = vector_store.similarity_search(state["messages"][0].content)
+
+        if similar_question:  # Check if the list is not empty
             example_msg = HumanMessage(
-                content=f"Similar question reference: \n\n{similar_question[0].page_content}"
+                content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
             )
-            return {"messages": [sys_msg] + state["messages"] + [instruction_msg, example_msg]}
+            return {"messages": [sys_msg] + state["messages"] + [example_msg]}
         else:
-            return {"messages": [sys_msg] + state["messages"] + [instruction_msg]}
-    
+            # Handle the case when no similar questions are found
+            return {"messages": [sys_msg] + state["messages"]}
     
     
 
@@ -164,8 +101,9 @@ def build_graph():
     )
     builder.add_edge("tools", "assistant")
 
+
     # Compile graph
     return builder.compile()
 
     # Add recursion limit
-    return builder.compile(checkpointer=None, recursion_limit=50)
+    return builder.compile() #checkpointer=None, recursion_limit=20

app.py

@@ -67,42 +67,32 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
-    # 2. Fetch Questions with retry mechanism
+
+
+    # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
-    
-    # Retry 
-    session = requests.Session()
-    retry_strategy = Retry(
-        total=5,  
-        status_forcelist=[429, 500, 502, 503, 504],  
-        backoff_factor=1,  
-        respect_retry_after_header=True
-    )
-    adapter = HTTPAdapter(max_retries=retry_strategy)
-    session.mount("http://", adapter)
-    session.mount("https://", adapter)
-    
     try:
-        # rate limiter
-        time.sleep(2) 
-        response = session.get(questions_url, timeout=30)
+        response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-            print("Fetched questions list is empty.")
-            return "Fetched questions list is empty or invalid format.", None
+             print("Fetched questions list is empty.")
+             return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
     except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
     except requests.exceptions.JSONDecodeError as e:
-        print(f"Error decoding JSON response from questions endpoint: {e}")
-        print(f"Response text: {response.text[:500]}")
-        return f"Error decoding server response for questions: {e}", None
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for questions: {e}", None
     except Exception as e:
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
+
+
+
     # 3. Run your Agent
     results_log = []
     answers_payload = []
@@ -126,18 +116,17 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
 
+    # --- Submission Process ---
     # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
 
-    # 5. Submit with retry
+    # 5. Submit
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        # Rate limiting 
-        time.sleep(2)
-        response = session.post(submit_url, json=submission_data, timeout=120)
+        response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
         result_data = response.json()
         final_status = (
@@ -179,6 +168,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
 
 
+
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")