Create app.py

app.py

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+import gradio as gr
+import os
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.types import Command
+from langchain_core.messages import HumanMessage
+from langgraph.prebuilt import create_react_agent
+from langchain_anthropic import ChatAnthropic
+
+# Set Anthropic API key from Hugging Face secret environment
+os.environ["ANTHROPIC_API_KEY"] = os.getenv("ANTHROPIC_API_KEY")
+
+# Initialize Claude 3.5 Sonnet LLM
+llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
+
+# System prompt template function
+def make_system_prompt(suffix: str) -> str:
+    return (
+        "You are a helpful AI assistant, collaborating with other assistants."
+        " Use the provided tools to progress towards answering the question."
+        " If you are unable to fully answer, that's OK, another assistant with different tools "
+        " will help where you left off. Execute what you can to make progress."
+        " If you or any of the other assistants have the final answer or deliverable,"
+        " prefix your response with FINAL ANSWER so the team knows to stop."
+        f"\n{suffix}"
+    )
+
+# Research agent logic
+def research_node(state: MessagesState) -> Command[str]:
+    agent = create_react_agent(
+        llm,
+        tools=[],
+        state_modifier=make_system_prompt("You can only do research.")
+    )
+    result = agent.invoke(state)
+    goto = END if "FINAL ANSWER" in result["messages"][-1].content else "chart_generator"
+    result["messages"][-1] = HumanMessage(
+        content=result["messages"][-1].content, name="researcher"
+    )
+    return Command(update={"messages": result["messages"]}, goto=goto)
+
+# Chart generator logic
+def chart_node(state: MessagesState) -> Command[str]:
+    agent = create_react_agent(
+        llm,
+        tools=[],
+        state_modifier=make_system_prompt("You can only generate charts.")
+    )
+    result = agent.invoke(state)
+    goto = END if "FINAL ANSWER" in result["messages"][-1].content else "researcher"
+    result["messages"][-1] = HumanMessage(
+        content=result["messages"][-1].content, name="chart_generator"
+    )
+    return Command(update={"messages": result["messages"]}, goto=goto)
+
+# Build the LangGraph workflow
+workflow = StateGraph(MessagesState)
+workflow.add_node("researcher", research_node)
+workflow.add_node("chart_generator", chart_node)
+workflow.add_edge(START, "researcher")
+workflow.add_edge("researcher", "chart_generator")
+workflow.add_edge("chart_generator", END)
+graph = workflow.compile()
+
+# Function to execute LangGraph flow
+def run_langgraph(user_input):
+    events = graph.stream(
+        {"messages": [("user", user_input)]},
+        {"recursion_limit": 150}
+    )
+    output = []
+    for event in events:
+        output.append(event)
+    return output[-1]["messages"][-1].content if output else "No output generated"
+
+# Gradio interface logic
+def process_input(user_input):
+    return run_langgraph(user_input)
+
+# Launch Gradio app
+interface = gr.Interface(
+    fn=process_input,
+    inputs="text",
+    outputs="text",
+    title="LangGraph Research Automation",
+    description="Enter your research task (e.g., 'Get GDP data for the USA over the past 5 years and create a chart.')"
+)
+
+if __name__ == "__main__":
+    interface.launch(server_name="0.0.0.0", server_port=7860)