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alfred.py

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+# alfred_streamlit.py
+import streamlit as st
+from langchain_core.messages import HumanMessage
+from tools import alfred  # Import your LangGraph agent
+
+st.set_page_config(page_title="🕵️ Alfred - AI Assistant", page_icon="🎩")
+
+st.title("🎩 Alfred - Your AI Assistant")
+st.markdown("Ask Alfred anything. He’s connected to weather, search, model stats, and even your guest list!")
+
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# Chat input
+user_input = st.chat_input("Ask Alfred...")
+
+if user_input:
+    st.session_state.chat_history.append(HumanMessage(content=user_input))
+    with st.spinner("Alfred is thinking..."):
+        response = alfred.invoke({"messages": st.session_state.chat_history})
+        ai_response = response['messages'][-1].content
+        st.session_state.chat_history.append(response['messages'][-1])
+
+# Display chat history
+for msg in st.session_state.chat_history:
+    role = "🤵 Alfred" if msg.type == "ai" else "🧑 You"
+    st.chat_message(role).markdown(msg.content)

tools.py

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+from langchain_community.tools import DuckDuckGoSearchRun
+from typing import TypedDict,Annotated
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage ,HumanMessage,AIMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START,StateGraph
+from langgraph.prebuilt import tools_condition
+from langchain_groq import ChatGroq
+from langchain.tools import Tool
+from huggingface_hub import list_models
+import random
+from dotenv import load_dotenv
+import os
+from langchain_community.utilities import  SerpAPIWrapper
+load_dotenv()
+os.environ["GROQ_API_KEY"]=os.getenv("GROQ_API_KEY")
+#os.environ["SERPAPI_API_KEY"]=os.getenv("SERPAPI_API_KEY")
+groq_api_key=os.getenv("GROQ_API_KEY")
+serp_api_key=os.getenv("SERPAPI_API_KEY")
+
+from langchain_community.utilities import SerpAPIWrapper
+
+search = SerpAPIWrapper(serpapi_api_key=serp_api_key)
+search_tool = Tool(
+    name="SerpAPI Search",
+    func=search.run,
+    description="Search the web using SerpAPI"
+)
+
+    
+
+
+
+### weather tool
+def get_weather_info(location: str) -> str:
+    """Fetches dummy weather information for a given location."""
+    # Dummy weather data
+    weather_conditions = [
+        {"condition": "Rainy", "temp_c": 15},
+        {"condition": "Clear", "temp_c": 25},
+        {"condition": "Windy", "temp_c": 20}
+    ]
+    # Randomly select a weather condition
+    data = random.choice(weather_conditions)
+    return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"
+
+# Initialize the tool
+weather_info_tool = Tool(
+    name="get_weather_info",
+    func=get_weather_info,
+    description="Fetches dummy weather information for a given location."
+)
+
+
+
+##most downloaded 
+def get_hub_stats(author: str) -> str:
+    """Fetches the most downloaded model from a specific author on the Hugging Face Hub."""
+    try:
+        # List models from the specified author, sorted by downloads
+        models = list(list_models(author=author, sort="downloads", direction=-1, limit=1))
+
+        if models:
+            model = models[0]
+            return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
+        else:
+            return f"No models found for author {author}."
+    except Exception as e:
+        return f"Error fetching models for {author}: {str(e)}"
+
+# Initialize the tool
+hub_stats_tool = Tool(
+    name="get_hub_stats",
+    func=get_hub_stats,
+    description="Fetches the most downloaded model from a specific author on the Hugging Face Hub."
+)
+
+
+
+
+
+
+
+
+
+
+### langchain
+import datasets
+from langchain.docstore.document import Document
+from langchain_community.retrievers import BM25Retriever
+from langchain.tools import Tool
+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage,HumanMessage,AIMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START,StateGraph
+from langgraph.prebuilt import tools_condition
+from langchain_huggingface import HuggingFaceEndpoint ,ChatHuggingFace
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+import os
+load_dotenv()
+os.environ["GROQ_API_KEY"]=os.getenv("GROQ_API_KEY")
+groq_api_key=os.getenv("GROQ_API_KEY")
+# Load the dataset
+guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+
+# Convert dataset entries into Document objects
+docs = [
+    Document(
+        page_content="\n".join([
+            f"Name: {guest['name']}",
+            f"Relation: {guest['relation']}",
+            f"Description: {guest['description']}",
+            f"Email: {guest['email']}"
+        ]),
+        metadata={"name": guest["name"]}
+    )
+    for guest in guest_dataset
+]
+
+
+
+
+
+
+bm25_retriever = BM25Retriever.from_documents(docs)
+
+def extract_text(query: str) -> str:
+    """Retrieves detailed information about gala guests based on their name or relation."""
+    results = bm25_retriever.invoke(query)
+    if results:
+        return "\n\n".join([doc.page_content for doc in results[:3]])
+    else:
+        return "No matching guest information found."
+
+guest_info_tool = Tool(
+    name="guest_info_retriever",
+    func=extract_text,
+    description="Retrieves detailed information about gala guests based on their name or relation."
+)
+
+# Generate the chat interface , including the tools
+llm = ChatGroq(model="Gemma2-9b-It",groq_api_key=groq_api_key)
+tools = [guest_info_tool,search_tool,weather_info_tool,hub_stats_tool]
+llm_with_tools = llm.bind_tools(tools)
+
+# Generate the AgentState and Agent graph
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+def assistant(state: AgentState):
+    return {
+        "messages": [llm_with_tools.invoke(state["messages"])],
+    }
+
+## The graph
+builder = StateGraph(AgentState)
+
+# Define nodes: these do the work
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+
+# 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")
+alfred = builder.compile()
+
+messages = [HumanMessage(content="Tell me about our guest named 'Lady Ada Lovelace'.")]
+response = alfred.invoke({"messages": messages})
+
+
+