add agent

agent.py

@@ -0,0 +1,75 @@
+import os
+from dotenv import load_dotenv
+from langgraph.graph import START, StateGraph, MessagesState
+from langgraph.prebuilt import tools_condition
+from langgraph.prebuilt import ToolNode
+from langchain_groq import ChatGroq
+from langchain_community.tools import DuckDuckGoSearchResults
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.tools import tool
+from langchain_core.output_parsers import StrOutputParser
+
+load_dotenv()
+
+@tool
+def wiki_search(query: str) -> str:
+    """Search Wikipedia for a query and return maximum 2 results.
+    
+    Args:
+        query: The search query."""
+    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ])
+    return {"wiki_results": formatted_search_docs}
+
+@tool
+def web_search(query: str) -> str:
+    """Search DuckDuckGo for a query and return maximum 3 results.
+    
+    Args:
+        query: The search query."""
+    search_docs = DuckDuckGoSearchResults(max_results=3).invoke(query)
+    return {"web_results": search_docs}
+
+system_prompt =  "You are a helpful assistant"
+sys_msg = SystemMessage(content=system_prompt)
+
+tools = [
+    wiki_search,
+    web_search,
+]
+
+llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
+llm_with_tools = llm.bind_tools(tools)
+
+def assistant(state: MessagesState):
+    """Assistant node"""
+    return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+builder = StateGraph(MessagesState)
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+builder.add_edge(START, "assistant")
+builder.add_conditional_edges(
+    "assistant",
+    tools_condition,
+)
+builder.add_edge("tools", "assistant")
+graph = builder.compile()
+
+
+def get_answer(query):
+  messages = [HumanMessage(content=query)]
+  messages = graph.invoke({"messages": messages})
+  return result["messages"][-1]["content"]
+
+if __name__ == "__main__":
+    question = "In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?"
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()

app.py

@@ -3,6 +3,7 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from agent import graph, get_answer
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -13,9 +14,10 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 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."
+        fixed_answer = get_answer(question)
         print(f"Agent returning fixed answer: {fixed_answer}")
         return fixed_answer
 

requirements.txt

@@ -1,2 +1,8 @@
 gradio
-requests
+requests
+langgraph
+dotenv
+langchain-groq
+langchain-community
+wikipedia
+duckduckgo-search