Update app.py

app.py

@@ -2,88 +2,84 @@ import gradio as gr
 import os
 import matplotlib.pyplot as plt
 import pandas as pd
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt.tool_executor import ToolExecutor
-from langgraph.prebuilt.react import create_react_plan_and_execute
-from langgraph.checkpoint.sqlite import SqliteSaver
-from langgraph.graph.message import add_messages
-
-from langchain_core.messages import HumanMessage
+from langgraph.graph import StateGraph
+from langgraph.types import Command
+from langchain_core.messages import HumanMessage, AIMessage
 from langchain_anthropic import ChatAnthropic
 
-# Load API Key securely
+# Set API key from environment variable (Hugging Face secret)
 os.environ["ANTHROPIC_API_KEY"] = os.getenv("ANTHROPIC_API_KEY")
-
-# Define the LLM (Claude 3.5 Sonnet)
 llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
 
-# System prompt modifier
-def make_system_prompt(suffix: str) -> str:
+# System message function
+def make_system_prompt(task: str) -> str:
     return (
-        "You are a helpful AI assistant, collaborating with other assistants."
-        " Use the provided tools to progress toward answering the question."
-        " If you cannot fully answer, another assistant will continue where you left off."
-        " If you or the team has a complete answer, prefix it with FINAL ANSWER.\n"
-        f"{suffix}"
+        f"You are an AI assistant. Your task is: {task}. "
+        "If you have the final answer, prefix it with 'FINAL ANSWER'."
     )
 
-# Node 1: Research assistant logic
-def research_node(state: dict) -> dict:
-    messages = state.get("messages", [])
-    prompt = make_system_prompt("You can only do research.")
-    executor = create_react_plan_and_execute(llm=llm, tools=[], system_prompt=prompt)
-    response = executor.invoke(messages)
-    messages.append(HumanMessage(content=response.content, name="researcher"))
-    next_node = "chart_generator" if "FINAL ANSWER" not in response.content else END
-    return {"messages": messages, "next": next_node}
+# Node: research
+def research_node(state: dict) -> Command:
+    user_msg = state["messages"][-1]
+    task = user_msg.content
+    system_prompt = make_system_prompt("Research only")
+
+    response = llm.invoke([HumanMessage(content=system_prompt), user_msg])
+    content = response.content
+
+    state["messages"].append(AIMessage(content=content, name="researcher"))
+    goto = "chart_generator" if "FINAL ANSWER" not in content else "__end__"
+    return Command(update={"messages": state["messages"]}, goto=goto)
 
-# Node 2: Chart assistant logic
-def chart_node(state: dict) -> dict:
-    messages = state.get("messages", [])
-    prompt = make_system_prompt("You can only generate charts.")
-    executor = create_react_plan_and_execute(llm=llm, tools=[], system_prompt=prompt)
-    response = executor.invoke(messages)
-    messages.append(HumanMessage(content=response.content, name="chart_generator"))
-    return {"messages": messages, "next": END}
+# Node: chart generation
+def chart_node(state: dict) -> Command:
+    user_msg = state["messages"][-1]
+    task = user_msg.content
+    system_prompt = make_system_prompt("Generate chart only")
 
-# Define LangGraph flow
+    response = llm.invoke([HumanMessage(content=system_prompt), user_msg])
+    content = response.content
+
+    state["messages"].append(AIMessage(content=content, name="chart_generator"))
+    return Command(update={"messages": state["messages"]}, goto="__end__")
+
+# Build LangGraph
 workflow = StateGraph(dict)
 workflow.add_node("researcher", research_node)
 workflow.add_node("chart_generator", chart_node)
 workflow.set_entry_point("researcher")
-workflow.set_finish_point(END)
-workflow.add_conditional_edges("researcher", lambda x: x["next"])
-workflow.add_edge("chart_generator", END)
-
+workflow.set_finish_point("__end__")
+workflow.add_edge("researcher", "chart_generator")
 graph = workflow.compile()
 
-# Function to run the graph and optionally return chart
+# Stream function
 def run_langgraph(input_text):
     try:
         events = graph.stream({"messages": [HumanMessage(content=input_text)]})
-        output = list(events)[-1]
-        final_content = output["messages"][-1].content
+        final_state = list(events)[-1]
+        messages = final_state["messages"]
+        final_output = messages[-1].content
 
-        if "FINAL ANSWER" in final_content:
-            # Example static chart
+        if "FINAL ANSWER" in final_output:
+            # Dummy chart
             years = [2020, 2021, 2022, 2023, 2024]
             gdp = [21.4, 22.0, 23.1, 24.8, 26.2]
             plt.figure()
             plt.plot(years, gdp, marker="o")
             plt.title("USA GDP Over Last 5 Years")
             plt.xlabel("Year")
-            plt.ylabel("GDP in Trillions")
+            plt.ylabel("GDP in Trillions USD")
             plt.grid(True)
             plt.tight_layout()
-            plt.savefig("gdp_chart.png")
-            return "Chart generated based on FINAL ANSWER", "gdp_chart.png"
+            chart_path = "gdp_chart.png"
+            plt.savefig(chart_path)
+            return final_output, chart_path
         else:
-            return final_content, None
-
+            return final_output, None
     except Exception as e:
         return f"Error: {str(e)}", None
 
-# Gradio Interface
+# Gradio UI
 def process_input(user_input):
     return run_langgraph(user_input)