Update app.py

app.py

@@ -3,20 +3,8 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from typing import TypedDict, Annotated, Sequence, Dict, Any, List, Optional
-from langchain_core.messages import BaseMessage, HumanMessage
-from langchain_core.tools import tool
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, StateGraph
-from langgraph.prebuilt import ToolNode
-from langchain_community.tools import DuckDuckGoSearchResults
-from langchain_community.utilities import WikipediaAPIWrapper
-from langchain.agents import create_tool_calling_agent, AgentExecutor
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import operator
-from langchain_experimental.utilities import PythonREPL
-from functools import wraps
-import logging
+from langchain_core.messages import HumanMessage
+from agent import build_graph
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -26,168 +14,29 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 
 
-
-# --- Configure logging ---
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-DEFAULT_MODEL = "gpt-3.5-turbo"
-MAX_RESPONSE_LENGTH = 2000  # Prevent overly long responses
-
-def handle_errors(func):
-    """Decorator to handle common errors in agent operations."""
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        try:
-            return func(*args, **kwargs)
-        except Exception as e:
-            logger.error(f"Error in {func.__name__}: {str(e)}")
-            return {"error": str(e)}
-    return wrapper
-
-class AgentState(TypedDict):
-    messages: Annotated[Sequence[BaseMessage], operator.add]
-    sender: str
-
-@tool
-def wikipedia_search(query: str) -> str:
-    """Search Wikipedia for information. Useful for historical facts, scientific concepts, and general knowledge."""
-    try:
-        return WikipediaAPIWrapper().run(query)[:MAX_RESPONSE_LENGTH]
-    except Exception as e:
-        return f"Wikipedia search failed: {str(e)}"
-
-@tool
-def web_search(query: str, num_results: int = 3) -> list:
-    """Search the web for current information. Useful for news, recent events, and up-to-date data."""
-    try:
-        results = DuckDuckGoSearchResults(num_results=num_results).run(query)
-        return [str(r)[:500] for r in results][:num_results]  # Limit result size
-    except Exception as e:
-        return [f"Web search failed: {str(e)}"]
-
-@tool
-def calculate(expression: str) -> str:
-    """Evaluate mathematical expressions. Supports basic arithmetic and complex formulas."""
-    try:
-        python_repl = PythonREPL()
-        result = python_repl.run(expression)
-        return str(result)[:100]  # Limit numeric output length
-    except Exception as e:
-        return f"Calculation failed: {str(e)}"
-
 class BasicAgent:
-    """An enhanced LangGraph agent with better error handling and response processing."""
-    
-    def __init__(self, model_name: str = DEFAULT_MODEL, temperature: float = 0.7):
-        """Initialize the agent with tools and workflow."""
-        self.model_name = model_name
-        self.temperature = temperature
-        self.tools = [wikipedia_search, web_search, calculate]
-        self.llm = ChatOpenAI(model=model_name, temperature=temperature)
-        self.agent_executor = self._build_agent_executor()
-        self.workflow = self._build_workflow()
-        logger.info(f"AdvancedAgent initialized with model: {model_name}")
-
-    def _build_agent_executor(self) -> AgentExecutor:
-        """Build the agent executor with proper prompt and tools."""
-        prompt = ChatPromptTemplate.from_messages([
-            ("system", self._get_system_prompt()),
-            MessagesPlaceholder(variable_name="messages"),
-            MessagesPlaceholder(variable_name="agent_scratchpad"),
-        ])
-        agent = create_tool_calling_agent(self.llm, self.tools, prompt)
-        return AgentExecutor(
-            agent=agent,
-            tools=self.tools,
-            verbose=True,
-            handle_parsing_errors=True
-        )
-
-    def _get_system_prompt(self) -> str:
-        """Return a comprehensive system prompt for the agent."""
-        return """You are an advanced AI assistant with access to tools. Follow these rules:
-1. Be precise and factual
-2. Use tools when needed
-3. Cite your sources
-4. Break complex problems into steps
-5. Admit when you don't know something"""
-
-    def _build_workflow(self) -> StateGraph:
-        """Build and compile the agent workflow."""
-        workflow = StateGraph(AgentState)
-        
-        workflow.add_node("agent", self._run_agent)
-        workflow.add_node("tools", ToolNode(self.tools))
-        
-        workflow.set_entry_point("agent")
-        workflow.add_conditional_edges(
-            "agent",
-            self._should_continue,
-            {"continue": "tools", "end": END}
-        )
-        workflow.add_edge("tools", "agent")
-        
-        return workflow.compile()
+    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
 
-    @handle_errors
-    def _run_agent(self, state: AgentState) -> Dict[str, Any]:
-        """Execute the agent with error handling."""
-        response = self.agent_executor.invoke({"messages": state["messages"]})
-        return {"messages": [response["output"]]}
-
-    def _should_continue(self, state: AgentState) -> str:
-        """Determine if the workflow should continue based on tool calls."""
-        last_message = state["messages"][-1]
-        return "continue" if last_message.additional_kwargs.get("tool_calls") else "end"
-
-    @handle_errors
-    def __call__(self, query: str) -> Dict[str, Any]:
-        """Process a user query and return a structured response."""
-        if not query or len(query.strip()) == 0:
-            return {"error": "Empty query provided"}
-            
-        logger.info(f"Processing query: {query[:50]}...")
-        state = AgentState(messages=[HumanMessage(content=query)], sender="user")
-        
-        for output in self.workflow.stream(state):
-            for key, value in output.items():
-                if key == "messages":
-                    for message in value:
-                        if isinstance(message, BaseMessage):
-                            response = message.content[:MAX_RESPONSE_LENGTH]
-                            return {
-                                "response": response,
-                                "sources": self._extract_sources(state["messages"]),
-                                "steps": self._extract_steps(state["messages"]),
-                                "model": self.model_name
-                            }
-        return {"response": "No response generated", "sources": [], "steps": []}
-
-    def _extract_sources(self, messages: Sequence[BaseMessage]) -> List[str]:
-        """Extract and format sources from tool messages."""
-        sources = []
-        for msg in messages:
-            if hasattr(msg, 'additional_kwargs') and 'name' in msg.additional_kwargs:
-                source_name = msg.additional_kwargs.get('name', 'unknown')
-                content = str(msg.content)[:200]  # Truncate long content
-                sources.append(f"{source_name}: {content}")
-        return sources
-
-    def _extract_steps(self, messages: Sequence[BaseMessage]) -> List[str]:
-        """Extract and format the reasoning steps."""
-        steps = []
-        for msg in messages:
-            if hasattr(msg, 'additional_kwargs') and 'tool_calls' in msg.additional_kwargs:
-                for call in msg.additional_kwargs['tool_calls']:
-                    tool_name = call['function']['name']
-                    args = call['function']['arguments'][:100]  # Truncate long args
-                    steps.append(f"Used {tool_name} with args: {args}")
-        return steps
+class BasicAgent:
+    """A langgraph agent."""
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.graph = build_graph()
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
 
-        
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
@@ -315,11 +164,9 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
-
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
         ---
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).