fix import

app.py

@@ -3,7 +3,7 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-import lang_graph_agent
+from lang_grpaph_agent import LangGraphAgent
 
 # (Keep Constants as is)
 # --- Constants ---

lang_graph_agent.py

@@ -0,0 +1,92 @@
+import os
+from typing import TypedDict, List, Dict, Any, Optional, Annotated
+from langgraph.graph import StateGraph, START, END
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.utils.function_calling import convert_to_openai_function
+from duckduckgo_search import DDGS
+
+class AgentState(TypedDict):
+    question: str
+    messages: List[Any]
+    search_results: Optional[List[Dict[str, str]]]
+    final_answer: Optional[str]
+
+def web_search(query: str, num_results: int = 3) -> List[Dict[str, str]]:
+    """Perform a web search using DuckDuckGo"""
+    with DDGS() as ddgs:
+        results = []
+        for r in ddgs.text(query, max_results=num_results):
+            results.append({
+                'title': r['title'],
+                'link': r['link'],
+                'snippet': r['body']
+            })
+        return results
+
+class LangGraphAgent:
+    def __init__(self):
+        print("LangGraphAgent initialized.")
+        self.llm = ChatOpenAI(model="gpt-4", temperature=0)
+        self.graph = self._build_graph()
+
+    def _build_graph(self) -> StateGraph:
+        workflow = StateGraph(AgentState)
+
+        # Define the search node
+        workflow.add_node("search", self.search_step)
+        
+        # Define the answer generation node
+        workflow.add_node("generate_answer", self.generate_answer)
+
+        # Connect the nodes
+        workflow.set_entry_point("search")
+        workflow.add_edge("search", "generate_answer")
+        workflow.set_finish_point("generate_answer")
+
+        return workflow
+
+    def search_step(self, state: AgentState) -> AgentState:
+        """Perform web search based on the question"""
+        search_results = web_search(state['question'])
+        state['search_results'] = search_results
+        return state
+
+    def generate_answer(self, state: AgentState) -> AgentState:
+        """Generate final answer using search results"""
+        prompt = ChatPromptTemplate.from_messages([
+            ("system", "You are a helpful AI assistant that provides accurate answers based on search results."),
+            ("human", "Question: {question}\n\nSearch Results:\n{search_results}\n\nPlease provide a comprehensive answer based on these search results."),
+        ])
+
+        # Format search results for the prompt
+        formatted_results = '\n'.join([f"Title: {r['title']}\nSnippet: {r['snippet']}\nLink: {r['link']}\n" 
+                                     for r in state['search_results']])
+
+        # Generate response
+        response = self.llm.invoke(
+            prompt.format_messages(
+                question=state['question'],
+                search_results=formatted_results
+            )
+        )
+
+        state['final_answer'] = response.content
+        return state
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question: {question}")
+        
+        # Initialize state
+        state = {
+            'question': question,
+            'messages': [],
+            'search_results': None,
+            'final_answer': None
+        }
+
+        # Run the graph
+        final_state = self.graph.invoke(state)
+        
+        return final_state['final_answer']

smol_agent.py

@@ -0,0 +1,8 @@
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a new default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer