fixed agent

agent.py

@@ -1,6 +1,8 @@
 from textwrap import dedent
 from typing import TypedDict, List, Dict, Any, Optional, Annotated
+from functools import partial
 import os
+import re
 
 # from langchain_openai import ChatOpenAI
 # from langchain_huggingface.llms import HuggingFaceEndpoint
@@ -13,8 +15,6 @@ from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
 from langfuse.langchain import CallbackHandler
 from langfuse import get_client
 
-from tools import fetch_website, get_wiki_full, youtube_transcript, python_repl_tool, duckduckgo_search_results
-
 os.environ["LANGFUSE_PUBLIC_KEY"] = os.getenv("LANGFUSE_PUBLIC_KEY", "pk-lf-***")  # Public key is safe to expose in client-side code
 os.environ["LANGFUSE_SECRET_KEY"] = os.getenv("LANGFUSE_SECRET_KEY", "sk-lf-***") 
 os.environ["LANGFUSE_BASE_URL"] = os.getenv("LANGFUSE_BASE_URL", "https://us.cloud.langfuse.com") # 🇺🇸 US region
@@ -27,32 +27,32 @@ else:
     print("Authentication failed. Please check your credentials and host.")
 langfuse_handler = CallbackHandler()
 
-# Initialize the Hugging Face model
-hf_model_name = "openai/gpt-oss-120b" # "Qwen/Qwen2.5-72B-Instruct"
-hf_model_provider = "nscale" # "hf-inference"
-
-llm = HuggingFaceEndpoint(
-    repo_id=hf_model_name, 
-    provider=hf_model_provider,
-    max_new_tokens=8192,
-    do_sample=False,
-    # temperature=0.,
-)
-
-chat_model = ChatHuggingFace(llm=llm)
-
-# Equip llm with tools
-tools_list = [
-    fetch_website,
-    get_wiki_full,
-    youtube_transcript,
-    python_repl_tool,
-    duckduckgo_search_results
-]
-
-llm_with_tools = chat_model.bind_tools(
-    tools_list
-)
+# # Initialize the Hugging Face model
+# hf_model_name = "openai/gpt-oss-120b" # "Qwen/Qwen2.5-72B-Instruct"
+# hf_model_provider = "nscale" # "hf-inference"
+
+# llm = HuggingFaceEndpoint(
+#     repo_id=hf_model_name, 
+#     provider=hf_model_provider,
+#     max_new_tokens=8192,
+#     do_sample=False,
+#     # temperature=0.,
+# )
+
+# chat_model = ChatHuggingFace(llm=llm)
+
+# # Equip llm with tools
+# tools_list = [
+#     fetch_website,
+#     get_wiki_full,
+#     youtube_transcript,
+#     python_repl_tool,
+#     duckduckgo_search_results
+# ]
+
+# llm_with_tools = chat_model.bind_tools(
+#     tools_list
+# )
 
 # Define Agent Workflow
 
@@ -60,7 +60,7 @@ class AgentState(TypedDict):
     messages: Annotated[list[AnyMessage], add_messages]
 
 
-def assistant(state: AgentState):
+def assistant(state: AgentState, llm) -> Dict[str, Any]:
     # System message
     textual_description_of_tool = dedent(
         """
@@ -111,27 +111,27 @@ def assistant(state: AgentState):
     )
 
     return {
-        "messages": [llm_with_tools.invoke([sys_msg] + state["messages"])],
+        "messages": [llm.invoke([sys_msg] + state["messages"])],
     }
 
-# Build the StateGraph for the agent
-# The graph
-builder = StateGraph(AgentState)
-
-# Define nodes: these do the work
-builder.add_node("assistant", assistant)
-builder.add_node("tools", ToolNode(tools_list))
-
-# Define edges: these determine how the control flow moves
-builder.add_edge(START, "assistant")
-builder.add_conditional_edges(
-    "assistant",
-    # If the latest message requires a tool, route to tools
-    # Otherwise, provide a direct response
-    tools_condition,
-)
-builder.add_edge("tools", "assistant")
-agent_graph = builder.compile()
+# # Build the StateGraph for the agent
+# # The graph
+# builder = StateGraph(AgentState)
+
+# # Define nodes: these do the work
+# builder.add_node("assistant", assistant)
+# builder.add_node("tools", ToolNode(tools_list))
+
+# # Define edges: these determine how the control flow moves
+# builder.add_edge(START, "assistant")
+# builder.add_conditional_edges(
+#     "assistant",
+#     # If the latest message requires a tool, route to tools
+#     # Otherwise, provide a direct response
+#     tools_condition,
+# )
+# builder.add_edge("tools", "assistant")
+# agent_graph = builder.compile()
 
 def extract_answer(text):
     match = re.search(r'<answer>(.*?)</answer>', text, re.DOTALL)
@@ -140,14 +140,60 @@ def extract_answer(text):
     return 'None'
 
 class BasicAgent:
-    def __init__(self):
+
+    def __init__(self, hf_model_name, hf_model_provider, tools_list):
+        self.hf_model_name = hf_model_name
+        self.hf_model_provider = hf_model_provider
+        self.tools_list = tools_list
         print("BasicAgent initialized.")
+
+    def build_llm_with_tools(self):
+        print("Building Hugging Face model and tools...")
+        # Initialize the Hugging Face model
+        llm = HuggingFaceEndpoint(
+            repo_id=self.hf_model_name, 
+            provider=self.hf_model_provider,
+            max_new_tokens=8192,
+            do_sample=False,
+            temperature=0.4,
+        )
+
+        chat_model = ChatHuggingFace(llm=llm)
+
+        # Equip llm with tools
+
+        llm_with_tools = chat_model.bind_tools(
+            self.tools_list
+        )
+        print("Agent built successfully.")
+        return llm_with_tools
+    
+    def build_agent_graph(self):    
+        llm_with_tools = self.build_llm_with_tools()
+        # Build the StateGraph for the agent
+        builder = StateGraph(AgentState)
+
+        # Define nodes: these do the work
+        builder.add_node("assistant", partial(assistant, llm=llm_with_tools))
+        builder.add_node("tools", ToolNode(self.tools_list))
+
+        # Define edges: these determine how the control flow moves
+        builder.add_edge(START, "assistant")
+        builder.add_conditional_edges(
+            "assistant",
+            # If the latest message requires a tool, route to tools
+            # Otherwise, provide a direct response
+            tools_condition,
+        )
+        builder.add_edge("tools", "assistant")
+        return builder.compile()
+
     async def __call__(self, question: str) -> str:
         print(f"Agent received question (first 100 chars): {question[:100]}...")
         # fixed_answer = "This is a default answer."
         # print(f"Agent returning fixed answer: {fixed_answer}")
         # Create agent with all the tools
-        
+        agent_graph = self.build_agent_graph()
         # Example query agent might receive
         # fixed_answer = await agent.run(question)
         messages = [

app.py

@@ -7,6 +7,8 @@ import inspect
 import pandas as pd
 import re
 
+from tools import fetch_website, get_wiki_full, youtube_transcript, python_repl_tool, duckduckgo_search_results
+
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -18,6 +20,18 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # offloaded to agent.py for better modularity and readability
 from agent import BasicAgent
 
+# Initialize the Hugging Face model
+hf_model_name = "openai/gpt-oss-120b" # "Qwen/Qwen2.5-72B-Instruct"
+hf_model_provider = "nscale" # "hf-inference"
+
+# Equip llm with tools
+tools_list = [
+    fetch_website,
+    get_wiki_full,
+    youtube_transcript,
+    python_repl_tool,
+    duckduckgo_search_results
+]
 
 async def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -40,7 +54,7 @@ async def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = BasicAgent(hf_model_name, hf_model_provider, tools_list)
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None