Update app.py

app.py

@@ -6,23 +6,82 @@ import pandas as pd
 #from tavily import TavilyClient
 from langchain_tavily import TavilySearch
 from langgraph.prebuilt import create_react_agent
-from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
-from langchain_core.messages import HumanMessage, SystemMessage
+from langgraph.graph.message import add_messages
+from langgraph_supervisor import create_supervisor
+#from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langchain_core.tools import tool
+from langchain_core.messages import HumanMessage, SystemMessage, BaseMessage
+from langchain.chat_models import init_chat_model
+from typing import Annotated,Sequence, TypedDict
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+#ENV
 def _set_if_undefined(var: str):
     if not os.environ.get(var):
         os.environ[var] = userdata.get(var)
 
 _set_if_undefined("TAVILY_API_KEY")
-_set_if_undefined("HUGGINGFACEHUB_API_TOKEN")
+_set_if_undefined("GEMINI_API_KEY")
+##_set_if_undefined("HUGGINGFACEHUB_API_TOKEN")
 
 TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
-HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-       
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+##HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+#PROMPTS
+prompt_recomendado = """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template:
+                        FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
+                        If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
+                        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise.
+                        If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""
+
+prompt_search = """You are a web research agent.
+                   INSTRUCTIONS:
+                   Assist ONLY with research-related tasks, DO NOT do any math
+                   After you're done with your tasks, respond to the supervisor directly
+                   Respond ONLY with the results of your work, do NOT include ANY other text."""
+
+#TOOLS
+web_search = TavilySearch(
+        max_results=5,
+        topic="general",
+    )
+
+tools = [web_search]
+
+#LLMS
+# Create LLM class 
+#gemini_llm = ChatGoogleGenerativeAI(
+#    model= "gemini-2.0-flash", # replace with "gemini-2.0-flash"
+#    temperature=1.0,
+#    max_tokens=None,
+#    timeout=None,
+#    max_retries=2,
+#    google_api_key=GEMINI_API_KEY,
+#)
+# Bind tools to the model
+#general_model = gemini_llm.bind_tools([get_weather_forecast])
+
+#AGENTS
+web_research_agent = create_react_agent(
+    model="gemini-2.0-flash",  # gpt-4o-mini-2024-07-18
+    tools=[web_search],
+    prompt=prompt_search,
+    name="web_research_agent",
+)
+
+supervisor = create_supervisor(
+    model=init_chat_model("gemini-2.0-flash"),
+    agents=[web_research_agent],
+    prompt=prompt_recomendado,
+    add_handoff_back_messages=True,
+    output_mode="full_history",
+).compile()
+
+
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
@@ -37,23 +96,6 @@ class BasicAgent:
 #        chat_with_tools = chat.bind_tools(tools)
         
     def __call__(self, question: str) -> str:
-        # Generate the chat interface, including the tools
-        print("Generate the chat interface, including the tools")
-        llm = HuggingFaceEndpoint(
-            repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-            huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
-        )
-
-        print("ChatHuggingFace")
-        chat = ChatHuggingFace(llm=llm, verbose=True)
-
-        # Initialize Tavily Search Tool
-        print("Initialize Tavily Search Tool")
-        tavily_search_tool = TavilySearch(
-            max_results=5,
-            topic="general",
-        )
-        
         print(f"Agent received question (first 50 chars): {question[:50]}...")
 
         messages = [