building own agent code

.gitignore

@@ -0,0 +1,2 @@
+.env
+try.py

app.py

@@ -3,6 +3,14 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from langchain_openai import ChatOpenAI
+from dotenv import load_dotenv
+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -10,14 +18,55 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- 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
+
+# load system prompt
+system_prompt = SystemMessage(content="""
+You are a general AI assistant. I will ask you a question. 
+Report your thoughts, and finish your answer by reporting just your answer, nothing else. 
+Your final answer should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
+If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
+If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
+If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+""")
+
+load_dotenv()  # Load variables from .env into environment
+api_key = os.getenv("OPENAI_API_KEY")
+chat = ChatOpenAI(
+    api_key=api_key,
+    model="gpt-4o"
+)
+
+tools = []
+chat_with_tools = chat.bind_tools(tools)
+
+# Generate the AgentState and Agent graph
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+def assistant(state: AgentState):
+    return {
+        "messages": [chat_with_tools.invoke(state["messages"])],
+    }
+
+## The graph
+builder = StateGraph(AgentState)
+
+# Define nodes
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+
+# Define edges
+builder.add_edge(START, "assistant")
+builder.add_conditional_edges(
+    "assistant",
+    # If the latest message requires a tool, route to tools
+    # Otherwise, provide a direct response
+    tools_condition,
+)
+builder.add_edge("tools", "assistant")
+
+
+#------------------------------------SUBMISSION------------------------------------------------
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -40,7 +89,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = builder.compile()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -80,7 +129,13 @@ 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)
+            response = agent.invoke({
+                "messages": [
+                    system_prompt,
+                    HumanMessage(content=question_text)
+                ]
+            })
+            submitted_answer = response["messages"][-1].content
             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:

prompt.py

@@ -0,0 +1,8 @@
+prompt = """
+You are a general AI assistant. I will ask you a question. 
+Report your thoughts, and finish your answer by reporting just your answer, nothing else. 
+Your final answer should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
+If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 
+If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
+If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+"""

tools.py

@@ -0,0 +1,78 @@
+from typing import Optional
+from langchain_core.tools import tool
+import math
+import pandas as pd
+import requests
+import io
+
+
+@tool
+def compute_expression(expr: str) -> float:
+    """
+    Safely evaluate basic math expressions.
+    Supports +, -, *, /, **, %, and parentheses.
+    Example: "(2 + 3) * 4"
+    """
+    try:
+        result = eval(expr, {"__builtins__": None}, math.__dict__)
+        return float(result)
+    except Exception as e:
+        return f"Math error: {str(e)}"
+
+
+@tool
+def describe_table_from_url(file_url: str) -> str:
+    """
+    Download a CSV or Excel file from a URL and describe the columns and types.
+    Useful for understanding tabular data.
+    """
+    try:
+        response = requests.get(file_url, timeout=10)
+        response.raise_for_status()
+        content = io.BytesIO(response.content)
+
+        if file_url.endswith(".csv"):
+            df = pd.read_csv(content)
+        elif file_url.endswith(".xls") or file_url.endswith(".xlsx"):
+            df = pd.read_excel(content)
+        else:
+            return "Unsupported file format. Please use CSV or Excel."
+
+        desc = f"Columns: {list(df.columns)}\nTypes:\n{df.dtypes}"
+        return desc
+    except Exception as e:
+        return f"Error loading table: {e}"
+
+
+@tool
+def search_web(query: str) -> str:
+    """
+    Perform a simple web search using DuckDuckGo Instant Answer API.
+    Returns a short summary or answer.
+    """
+    try:
+        response = requests.get("https://api.duckduckgo.com/", params={
+            "q": query,
+            "format": "json",
+            "no_redirect": 1,
+            "no_html": 1
+        }, timeout=10)
+        data = response.json()
+        if data.get("AbstractText"):
+            return data["AbstractText"]
+        elif data.get("Answer"):
+            return data["Answer"]
+        elif data.get("RelatedTopics"):
+            return data["RelatedTopics"][0].get("Text", "No summary found.")
+        else:
+            return "No results found."
+    except Exception as e:
+        return f"Web search error: {e}"
+
+
+# Export the tools in a list
+TOOLS = [
+    compute_expression,
+    describe_table_from_url,
+    search_web,
+]