Add application file

app.py

@@ -0,0 +1,233 @@
+
+import os
+import gradio as gr
+import datasets
+from typing import List, Tuple
+
+# LangChain / LangGraph imports
+from langchain_core.documents import Document
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.tools import Tool
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage, ToolMessage
+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+
+def set_token_hfhub(value):
+    os.environ["HF_TOKEN"] = value
+
+# ==============================
+# 1) Build the same agent (Alfred)
+# ==============================
+
+# Load the dataset and make Documents
+guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+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
+]
+
+# Embeddings & Vectorstore retriever
+embeddings = HuggingFaceEmbeddings(
+    model_name="sentence-transformers/all-MiniLM-L6-v2",
+    encode_kwargs={"normalize_embeddings": True},
+)
+vectorstore = FAISS.from_documents(docs, embeddings)
+retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+# Guest info tool
+def extract_text(query: str) -> str:
+    """Retrieves detailed information about gala guests based on their name or relation."""
+    results = retriever.invoke(query)
+    if results:
+        return "\n\n".join([doc.page_content for doc in results])
+    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.",
+)
+
+# Web search tool
+search_tool = DuckDuckGoSearchRun()
+
+# LLM endpoint (reads token from env var or Python var fallback)
+# hf_token = os.getenv("HF_TOKEN")
+hf_token = os.environ["HF_TOKEN"]
+
+if not hf_token:
+    raise RuntimeError(
+        "HUGGINGFACEHUB_API_TOKEN is not set. Please export it before running the app."
+    )
+
+llm = HuggingFaceEndpoint(
+    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+    huggingfacehub_api_token=hf_token,
+)
+chat = ChatHuggingFace(llm=llm, verbose=True)
+tools = [guest_info_tool, search_tool]
+chat_with_tools = chat.bind_tools(tools)
+
+# Agent state & node
+class AgentState(TypedDict):
+    messages: Annotated[List[AnyMessage], add_messages]
+
+def assistant(state: AgentState):
+    # Produce one assistant message (may include a tool call)
+    return {"messages": [chat_with_tools.invoke(state["messages"])]}
+
+# Graph wiring
+builder = StateGraph(AgentState)
+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")
+alfred = builder.compile()
+
+# ======================================
+# 2) Helper functions for the Gradio UI
+# ======================================
+
+def _msg_content_to_str(msg: AnyMessage) -> str:
+    """
+    Coerce LangChain message content (which might contain tool call structures)
+    into displayable text for the Chatbot.
+    """
+    # Most often, content is a string already
+    content = getattr(msg, "content", "")
+    if isinstance(content, str):
+        return content
+
+    # If it's a list of parts (e.g., tool call traces), join any text parts
+    if isinstance(content, list):
+        texts = []
+        for part in content:
+            if isinstance(part, dict) and "text" in part:
+                texts.append(part["text"])
+            elif isinstance(part, str):
+                texts.append(part)
+        return "\n".join(texts) if texts else str(content)
+
+    # Fallback
+    return str(content)
+
+def startup_state() -> List[AnyMessage]:
+    """Start with an empty conversation."""
+    return []
+
+# Gradio expects chatbot history as List[Tuple[str, str]]
+def submit_user_message(
+    user_text: str,
+    chat_history: List[Tuple[str, str]],
+    agent_messages: List[AnyMessage],
+):
+    """
+    1) Append HumanMessage to agent state
+    2) Run Alfred
+    3) Extract last AIMessage and append to chat_history
+    """
+    if not user_text or user_text.strip() == "":
+        return gr.update(), chat_history, agent_messages
+
+    # Step 1: add HumanMessage to state
+    agent_messages = list(agent_messages or [])
+    agent_messages.append(HumanMessage(content=user_text))
+
+    # Step 2: run the graph
+    out = alfred.invoke({"messages": agent_messages})
+
+    # The graph returns a new messages list *including* the latest assistant/tool steps.
+    # We use the last AIMessage as the displayed reply.
+    new_msgs: List[AnyMessage] = out["messages"]
+    agent_messages = new_msgs  # keep full state for the next turn
+
+    # Find the last assistant message to show in the UI
+    ai_text = ""
+    for m in reversed(new_msgs):
+        if isinstance(m, AIMessage):
+            ai_text = _msg_content_to_str(m)
+            break
+    if not ai_text:
+        # fallback: in rare cases of only tool messages, show a generic note
+        ai_text = "I processed your request using my tools."
+
+    chat_history = list(chat_history or [])
+    chat_history.append({"role": "user", "content": user_text})
+    chat_history.append({"role": "assistant", "content": ai_text})
+    return "", chat_history, agent_messages
+
+def clear_chat():
+    """Reset the Gradio UI and agent state."""
+    return [], startup_state()
+
+# ========================
+# 3) Gradio App UI layout
+# ========================
+
+with gr.Blocks(title="Alfred — LangGraph Agent") as demo:
+    gr.Markdown(
+        """
+        # 🎩 Alfred — Your LangGraph Agent
+        Ask questions and Alfred will respond, using:
+        - a vector search tool over the guest list
+        - DuckDuckGo web search
+        """
+    )
+
+    with gr.Row(): 
+        token1 = gr.Textbox(
+            label="Your hf token",
+            autofocus=True,
+            scale=2,
+        )
+
+    with gr.Row():
+        chatbot = gr.Chatbot(
+            label="Conversation",
+            type="messages",
+            height=500,
+            show_copy_button=True,
+            avatar_images=(None, None),  # customize if you like
+        )
+
+    with gr.Row():
+        txt = gr.Textbox(
+            label="Your message",
+            placeholder="Ask anything…",
+            autofocus=True,
+            scale=4,
+        )
+        send_btn = gr.Button("Send", variant="primary", scale=1)
+        clear_btn = gr.Button("Clear")
+
+    # Hidden state: the agent’s full message list (LangChain messages)
+    agent_state = gr.State(startup_state())
+
+    # Wire up events
+    token1.submit(set_token_hfhub, [token1])
+    txt.submit(submit_user_message, [txt, chatbot, agent_state], [txt, chatbot, agent_state])
+    send_btn.click(submit_user_message, [txt, chatbot, agent_state], [txt, chatbot, agent_state])
+    clear_btn.click(clear_chat, outputs=[chatbot, agent_state])
+
+# Entry point
+if __name__ == "__main__":
+    # You can tweak server_name/port as needed
+    demo.launch()

requirement.txt

@@ -0,0 +1,8 @@
+gradio
+langchain
+langgraph
+langchain-community
+langchain_huggingface
+datasets
+faiss-cpu
+ddgs