commit

app.py

@@ -1,86 +1,36 @@
+""" Basic Agent Evaluation Runner"""
 import os
+import inspect
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
-import os
-import yaml
-from tools_agent import ReverseTextTool, TableCommutativityTool, VegetableListTool, ExcelSumFoodTool
-from smolagents import (
-    CodeAgent,
-    OpenAIServerModel,
-    DuckDuckGoSearchTool,
-    FinalAnswerTool,
-    PythonInterpreterTool,
-    HfApiModel
-)
-from huggingface_hub import login, InferenceClient
+from langchain_core.messages import HumanMessage
+from agent import build_graph
+
+
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 
 
-# --- Agent Definition ---
 class BasicAgent:
+    """A langgraph agent."""
     def __init__(self):
-        print("Initializing BasicAgent with tools...")
-
-        # Load OpenAI token from environment
-        openai_token = os.getenv("HF_TOKEN")
-        if not openai_token:
-            raise ValueError("Missing API token!")
-
-        # Initialize model and tools
-
-        # model = HfApiModel(
-        #     model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-        #     token=openai_token,
-        #     max_tokens=5000,
-        #     provider="together",
-        # )
-
-        client = InferenceClient(
-            provider="together",
-            api_key=openai_token,
-        )
-
-        # model = OpenAIServerModel(
-        #     #api_base="openai",
-        #     api_key=openai_token,
-        #     model_id="gpt-4.1"
-        # )
-        search_tool = DuckDuckGoSearchTool()
-        final_answer_tool = FinalAnswerTool()
-        reverse_tool = ReverseTextTool()
-        table_tool = TableCommutativityTool()
-        veg_tool = VegetableListTool()
-        python_tool = PythonInterpreterTool()
-        exfood_tool = ExcelSumFoodTool()
-
-        # Load system prompt templates
-        with open("prompts.yaml", "r") as stream:
-            prompt_templates = yaml.safe_load(stream)
-
-        # Build the agent
-        self.agent = CodeAgent(
-            model=client,
-            prompt_templates=prompt_templates,
-            tools=[search_tool, reverse_tool, table_tool, veg_tool, python_tool, exfood_tool], #final_answer_tool
-            add_base_tools=True,
-            planning_interval=None,
-            name = "GoodAgent",
-            max_steps=10,
-            verbosity_level=1,
-        )
+        print("BasicAgent initialized.")
+        self.graph = build_graph()
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question}...")
-        answer = self.agent(question)
-        print(f"Agent returning answer: {answer}")
-        return answer
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """