Update agent.py

agent.py

@@ -4,8 +4,8 @@ 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_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
+# from langchain_google_genai import ChatGoogleGenerativeAI
+# from langchain_groq import ChatGroq
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_community.document_loaders import WikipediaLoader
@@ -152,16 +152,16 @@ tools = [
 ]
 
 # Build graph function
-def build_graph(provider: str = "groq"):
+def build_graph(provider: str = "huggingface"):
     """Build the graph"""
     # Load environment variables from .env file
-    if provider == "google":
+    # if provider == "google":
         # Google Gemini
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-    elif provider == "groq":
+        # llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
+    # elif provider == "groq":
         # Groq https://console.groq.com/docs/models
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
-    elif provider == "huggingface":
+        # llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
+    if provider == "huggingface":
         # TODO: Add huggingface endpoint
         llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
@@ -206,7 +206,7 @@ def build_graph(provider: str = "groq"):
 if __name__ == "__main__":
     question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
     # Build the graph
-    graph = build_graph(provider="groq")
+    graph = build_graph(provider="huggingface")
     # Run the graph
     messages = [HumanMessage(content=question)]
     messages = graph.invoke({"messages": messages})