init basic tools

app.py

@@ -1,23 +1,83 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
+from langchain_openai import ChatOpenAI
+from langchain.agents import create_agent 
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_experimental.utilities import PythonREPL
+from langchain_core.tools import tool
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+search_tool = DuckDuckGoSearchRun()
+python_repl = PythonREPL()
+
+@tool
+def python_calculator(code: str): 
+    """
+    Useful for solving math problems, analyzing data, or reading files.
+    Input should be valid python code.
+    If you need to read a file, assume it is in the current directory.
+    ALWAYS print the final result using `print(...)`.
+    """
+    return python_repl.run(code)
+
+REACT_PROMPT = """
+You are a helpful and powerful assistant. Your goal is to answer the user's question accurately.
+
+You have access to the following tools:
+{tools}
+
+To answer the question, you MUST follow this strict procedure:
+1.  **Thought**: First, think about the user's question and decide if you need a tool.
+2.  **Action**: If you need to use a tool, you MUST use the following JSON format:
+{{
+    "thought": "Your reasoning for using this tool and what you plan to do with the result.",
+    "tool": "tool_name",
+    "tool_input": "the input for the tool"
+}}
+
+The `tool_name` must be one of the following: {tool_names}.
+
+3.  After getting the result from the tool (the "Observation"), think again. If you have the final answer, proceed to the next step. If not, go back to step 1 and decide if you need to use another tool.
+
+4.  **Final Answer**: When you have the final answer, you MUST use the following JSON format. Do not add any other text outside this JSON block:
+{{
+    "thought": "I have now found the final answer to the user's question.",
+    "final_answer": "The final answer is..."
+}}
+"""
+
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 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 default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        self.llm = ChatOpenAI(
+            model="deepseek-chat",
+            temperature=0,
+            openai_api_key=os.getenv("OPENAI_API_KEY")
+        )
+
+        self.tools = [search_tool, python_calculator]
+
+        # Initiate ReAct agent
+        self.agent_executor = create_agent(
+            model=self.llm,
+            tools=self.tools,
+            system_prompt=REACT_PROMPT
+        )
+    
+    def run(self, question: str) -> str: 
+        response = self.agent_executor.invoke({
+            "input": question
+        })
+
+        return response['output']
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -80,7 +140,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            submitted_answer = agent.run(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:

requirements.txt

@@ -1,2 +1,11 @@
 gradio
-requests
+requests
+langchain
+langchain-community
+langchain-experimental
+langchain-openai
+ddgs
+duckduckgo-search
+pandas
+openpyxl
+tabulate