Create app.py

app.py

@@ -0,0 +1,86 @@
+# app.py — Entry point for HF Spaces
+import os
+import gradio as gr
+from typing import TypedDict, Annotated
+from langgraph.graph import StateGraph, START, END
+from langgraph.prebuilt import ToolNode, tools_condition
+from langgraph.graph.message import add_messages
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
+from langchain_groq import ChatGroq
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_core.tools import tool
+import requests
+
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+
+# ─── LLM ─────────────────────────────────
+llm = ChatGroq(
+    model="llama-3.1-8b-instant")
+
+# ─── Tools ───────────────────────────────
+search_tool = DuckDuckGoSearchRun()
+
+@tool
+def calculator(first_num: float, second_num: float, operation: str) -> dict:
+    """Perform basic arithmetic. Operations: add, sub, mul, div"""
+    ops = {"add": first_num + second_num, "sub": first_num - second_num,
+           "mul": first_num * second_num}
+    if operation == "div":
+        return {"result": "Division by zero" if second_num == 0 else first_num / second_num}
+    return {"result": ops.get(operation, f"Unknown operation: {operation}")}
+
+@tool
+def get_stock_price(symbol: str) -> dict:
+    """Fetch latest stock price for a symbol like AAPL or TSLA."""
+    url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}&apikey={os.getenv('STOCKS_API_KEY')}"
+    return requests.get(url).json()
+
+tools = [search_tool, calculator, get_stock_price]
+llm_with_tools = llm.bind_tools(tools)
+
+# ─── State ───────────────────────────────
+class ChatState(TypedDict):
+    messages: Annotated[list[BaseMessage], add_messages]
+
+# ─── Graph ───────────────────────────────
+def chat_node(state: ChatState):
+    """LLM node that may answer or request a tool call."""
+    messages = state['messages']
+    response = llm_with_tools.invoke(messages)
+    return {"messages": [response]}
+
+graph = StateGraph(ChatState)
+graph.add_node("chat_node", chat_node)
+graph.add_node("tools", ToolNode(tools))
+graph.add_edge(START, "chat_node")
+graph.add_conditional_edges("chat_node", tools_condition)
+graph.add_edge("tools", "chat_node")
+agent = graph.compile()
+
+# ─── Gradio UI ───────────────────────────
+def respond(message, history):
+    # Convert Gradio history → LangChain messages
+    messages = []
+    for user_msg, bot_msg in history:
+        messages.append(HumanMessage(content=user_msg))
+        if bot_msg:
+            messages.append(AIMessage(content=bot_msg))
+    messages.append(HumanMessage(content=message))
+
+    result = agent.invoke({"messages": messages})
+    return result["messages"][-1].content
+
+demo = gr.ChatInterface(
+    fn=respond,
+    title="🔍 AI Research Agent",
+    description="Ask me anything — I can search the web and do calculations!",
+    examples=[
+        "What is LangGraph?",
+        "What's happening in AI news today?",
+        "Calculate 128 multiplied by 37",
+    ],
+    theme=gr.themes.Soft()
+)
+
+if __name__ == "__main__":
+    demo.launch()