Upload app.py

app.py

@@ -1,13 +1,13 @@
 """
-LangGraph + Groq + Gradio General Assistant Chatbot
-Ready for Hugging Face Spaces (no audioop dependency)
+LangGraph + Groq + Gradio 5 General Assistant Chatbot
+Ready for Hugging Face Spaces (Python 3.13 compatible)
 """
 
 import os
-import gradio as gr
 from typing import Annotated
 from typing_extensions import TypedDict
 
+import gradio as gr
 from langchain_groq import ChatGroq
 from langchain_core.messages import HumanMessage, AIMessage, SystemMessage, BaseMessage
 from langgraph.graph import StateGraph, START, END
@@ -30,21 +30,18 @@ def build_graph(api_key: str, model: str, system_prompt: str):
     )
 
     def chatbot_node(state: State) -> dict:
-        messages = state["messages"]
-        # Prepend system message each call (stateless node)
-        full_messages = [SystemMessage(content=system_prompt)] + messages
+        full_messages = [SystemMessage(content=system_prompt)] + state["messages"]
         response = llm.invoke(full_messages)
         return {"messages": [response]}
 
-    graph_builder = StateGraph(State)
-    graph_builder.add_node("chatbot", chatbot_node)
-    graph_builder.add_edge(START, "chatbot")
-    graph_builder.add_edge("chatbot", END)
-
-    return graph_builder.compile()
+    builder = StateGraph(State)
+    builder.add_node("chatbot", chatbot_node)
+    builder.add_edge(START, "chatbot")
+    builder.add_edge("chatbot", END)
+    return builder.compile()
 
 
-# ── Gradio chat function ─────────────────────────────────────────────────────
+# ── Chat function ─────────────────────────────────────────────────────────────
 
 def respond(
     message: str,
@@ -55,14 +52,14 @@ def respond(
     max_history: int,
 ):
     if not api_key.strip():
-        yield "⚠️ Please enter your Groq API key in the sidebar."
+        yield "⚠️ Please enter your Groq API key in the settings panel below."
         return
 
     if not message.strip():
         yield ""
         return
 
-    # Convert Gradio history → LangChain messages
+    # Convert Gradio openai-style history → LangChain messages
     lc_messages: list[BaseMessage] = []
     for entry in history[-(max_history * 2):]:
         if entry["role"] == "user":
@@ -76,13 +73,12 @@ def respond(
 
     try:
         result = graph.invoke({"messages": lc_messages})
-        ai_msg = result["messages"][-1]
-        yield ai_msg.content
+        yield result["messages"][-1].content
     except Exception as e:
         yield f"❌ Error: {str(e)}"
 
 
-# ── UI ───────────────────────────────────────────────────────────────────────
+# ── Constants ─────────────────────────────────────────────────────────────────
 
 MODELS = [
     "llama-3.3-70b-versatile",
@@ -96,83 +92,56 @@ DEFAULT_SYSTEM = (
     "Answer clearly and concisely. If you don't know something, say so honestly."
 )
 
+# ── UI ────────────────────────────────────────────────────────────────────────
+
 with gr.Blocks(
     title="LangGraph × Groq Assistant",
     theme=gr.themes.Soft(primary_hue="violet", neutral_hue="slate"),
-    css="""
-    #col-left { border-right: 1px solid #e2e8f0; }
-    .gr-button-primary { background: linear-gradient(135deg, #7c3aed, #4f46e5) !important; }
-    footer { display: none !important; }
-    """,
 ) as demo:
 
+    gr.Markdown("# 🤖 LangGraph × Groq Assistant")
     gr.Markdown(
-        """
-        # 🤖 LangGraph × Groq Assistant
-        A stateful conversational AI powered by **LangGraph** graphs and **Groq** inference.
-        """
+        "A stateful conversational AI powered by **LangGraph** graphs and **Groq** inference."
     )
 
-    with gr.Row():
-        # ── Sidebar ──────────────────────────────────────────────────────────
-        with gr.Column(scale=1, elem_id="col-left"):
-            gr.Markdown("### ⚙️ Configuration")
-
-            api_key = gr.Textbox(
+    # Gradio 5 ChatInterface — retry/undo/clear are built into the Chatbot widget,
+    # submit_btn takes a str (not gr.Button), no retry_btn/undo_btn/clear_btn params.
+    chat = gr.ChatInterface(
+        fn=respond,
+        type="messages",
+        submit_btn="Send ➤",
+        examples=[
+            "What is LangGraph and how does it differ from LangChain?",
+            "Explain quantum entanglement in simple terms.",
+            "Write a short Python function to reverse a linked list.",
+            "What are the pros and cons of microservices architecture?",
+        ],
+        additional_inputs=[
+            gr.Textbox(
                 label="Groq API Key",
                 placeholder="gsk_...",
                 type="password",
                 info="Get yours free at console.groq.com",
-            )
-
-            model = gr.Dropdown(
+            ),
+            gr.Dropdown(
                 choices=MODELS,
                 value=MODELS[0],
                 label="Model",
-            )
-
-            system_prompt = gr.Textbox(
+            ),
+            gr.Textbox(
                 label="System Prompt",
                 value=DEFAULT_SYSTEM,
-                lines=4,
-                info="Persona / instructions for the assistant",
-            )
-
-            max_history = gr.Slider(
+                lines=3,
+            ),
+            gr.Slider(
                 minimum=1,
                 maximum=20,
                 value=10,
                 step=1,
                 label="Max history turns",
-                info="How many past turns to include in context",
-            )
-
-            gr.Markdown(
-                """
-                ---
-                **Stack**
-                - 🔗 [LangGraph](https://github.com/langchain-ai/langgraph)
-                - ⚡ [Groq](https://groq.com)
-                - 🖥️ [Gradio](https://gradio.app)
-                """
-            )
-
-        # ── Chat ─────────────────────────────────────────────────────────────
-        with gr.Column(scale=3):
-            chatbot = gr.ChatInterface(
-                fn=respond,
-                type="messages",
-                additional_inputs=[api_key, model, system_prompt, max_history],
-                examples=[
-                    ["What is LangGraph and how does it differ from LangChain?"],
-                    ["Explain quantum entanglement in simple terms."],
-                    ["Write a short Python function to reverse a linked list."],
-                    ["What are the pros and cons of microservices architecture?"],
-                ],
-                submit_btn="Send ➤",
-                retry_btn="🔁 Retry",
-                undo_btn="↩ Undo",
-                clear_btn="🗑️ Clear",
-            )
+            ),
+        ],
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Settings", open=False),
+    )
 
 demo.launch()