Fix: replace agent factory with manual llm.bind_tools() ReAct loop

Removes dependency on langchain.agents factory functions (not available
in installed version). Custom tool-calling loop works with any recent
langchain-groq version.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app.py

@@ -3,12 +3,11 @@ import gradio as gr
 import requests
 import pandas as pd
 from langchain_groq import ChatGroq
-from langchain.agents import create_openai_tools_agent, AgentExecutor
 from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun
 from langchain_community.utilities import WikipediaAPIWrapper
 from langchain_experimental.tools import PythonREPLTool
-from langchain.tools import tool
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.tools import tool
+from langchain_core.messages import SystemMessage, HumanMessage, ToolMessage
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -23,7 +22,7 @@ Rules for FINAL ANSWER:
 - Strings: no articles (a/an/the), no abbreviations, write digits in plain text
 - Lists: comma-separated, apply above rules per element
 - Be precise — evaluated by exact match
-- Never say "I cannot answer" — always attempt an answer"""
+- Always provide an answer, never say you cannot answer"""
 
 
 @tool
@@ -43,33 +42,48 @@ def fetch_task_file(task_id: str) -> str:
 
 class BasicAgent:
     def __init__(self):
-        llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0)
-        tools = [
+        self.llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0)
+        self.tools = [
             DuckDuckGoSearchRun(),
             WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(top_k_results=3)),
             PythonREPLTool(),
             fetch_task_file,
         ]
-        prompt = ChatPromptTemplate.from_messages([
-            ("system", SYSTEM_PROMPT),
-            ("human", "{input}"),
-            MessagesPlaceholder("agent_scratchpad"),
-        ])
-        agent = create_openai_tools_agent(llm, tools, prompt)
-        self.executor = AgentExecutor(
-            agent=agent,
-            tools=tools,
-            max_iterations=15,
-            handle_parsing_errors=True,
-            verbose=True,
-        )
-        print("BasicAgent initialized with LangChain tool-calling agent + Groq (llama-3.3-70b-versatile).")
+        self.tools_map = {t.name: t for t in self.tools}
+        self.llm_with_tools = self.llm.bind_tools(self.tools)
+        print("BasicAgent initialized with Groq (llama-3.3-70b-versatile) + tool binding.")
 
     def __call__(self, question: str, task_id: str = "") -> str:
         full_question = f"[Task ID: {task_id}]\n\n{question}" if task_id else question
         print(f"Running agent on task {task_id}: {question[:80]}...")
-        result = self.executor.invoke({"input": full_question})
-        raw_answer = result.get("output", "")
+
+        messages = [
+            SystemMessage(content=SYSTEM_PROMPT),
+            HumanMessage(content=full_question),
+        ]
+
+        for iteration in range(15):
+            response = self.llm_with_tools.invoke(messages)
+            messages.append(response)
+
+            if not response.tool_calls:
+                break
+
+            for tool_call in response.tool_calls:
+                tool_name = tool_call["name"]
+                tool_args = tool_call["args"]
+                tool_id = tool_call["id"]
+                print(f"  Tool call: {tool_name}({tool_args})")
+                if tool_name in self.tools_map:
+                    try:
+                        result = self.tools_map[tool_name].invoke(tool_args)
+                    except Exception as e:
+                        result = f"Tool error: {e}"
+                else:
+                    result = f"Unknown tool: {tool_name}"
+                messages.append(ToolMessage(content=str(result), tool_call_id=tool_id))
+
+        raw_answer = response.content
         if "FINAL ANSWER:" in raw_answer:
             answer = raw_answer.split("FINAL ANSWER:")[-1].strip()
         else:
@@ -194,7 +208,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
 # --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Agent Evaluation Runner — LangChain + Groq")
+    gr.Markdown("# Agent Evaluation Runner — Groq + Tool Binding")
     gr.Markdown(
         """
         **Instructions:**
@@ -202,7 +216,7 @@ with gr.Blocks() as demo:
         1. Log in to your Hugging Face account using the button below.
         2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run the agent, and submit.
 
-        **Agent:** LangChain tool-calling agent with Groq (llama-3.3-70b-versatile)
+        **Agent:** Custom ReAct loop with Groq (llama-3.3-70b-versatile) + bind_tools
         **Tools:** DuckDuckGo search, Wikipedia, Python REPL, File fetcher
 
         ---