'Added agent'

agent.py

@@ -0,0 +1,124 @@
+#Imports
+from langchain_core.tools import tool
+from langchain_community.tools import DuckDuckGoSearchResults
+from langchain_openai import ChatOpenAI
+from langchain_groq import ChatGroq
+from datetime import datetime
+from langgraph.graph import StateGraph, END
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+from typing import TypedDict, Annotated
+from langchain_core.messages import AnyMessage
+from langgraph.graph.message import add_messages
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition, ToolNode
+import gradio as gr
+from dotenv import load_dotenv
+
+load_dotenv()
+
+#Fetch from the space's secrets (previously added)
+import os
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+#LLM Setup
+# llm = ChatOpenAI(model="gpt-4.1")
+llm = ChatGroq(model="llama3-70b-8192", api_key=os.getenv("GROQ_API_KEY"), temperature=0.0, max_tokens=1000, top_p=1.0, frequency_penalty=0.0, presence_penalty=0.0)
+
+#Tools to be used by the LLM
+
+@tool
+def add(a: float, b: float) -> float:
+    """Add two numbers."""
+    return a + b
+
+
+@tool
+def subtract(a: float, b: float) -> float:
+    """Subtract the second number from the first."""
+    return a - b
+
+
+@tool
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers."""
+    return a * b
+
+
+@tool
+def divide(a: float, b: float) -> float:
+    """Divide the first number by the second."""
+    if b == 0:
+        raise ValueError("Division by zero.")
+    return a / b
+
+
+@tool
+def get_current_time() -> str:
+    """Get the current date and time."""
+    return datetime.now().isoformat()
+
+
+search = DuckDuckGoSearchResults()
+
+#Tool List
+tools = [add, subtract, multiply, divide, get_current_time, search]
+
+#Bind LLM with Tools
+llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=True)
+
+#Class to hold the state to be passed through the graph/ flow
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+#Define the Assistant Node
+def assistant(state: AgentState) -> AgentState:
+    messages = state["messages"]
+    response = llm_with_tools.invoke(messages)
+    return {"messages": messages + [response]}
+
+#Graph
+builder = StateGraph(AgentState)
+
+# Define nodes: these do the work
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+
+# Define edges: these determine how the control flow moves
+builder.add_edge(START, "assistant")
+builder.add_conditional_edges(
+    "assistant",
+    # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools
+    # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END
+    tools_condition,
+)
+builder.add_edge("tools", "assistant")
+
+react_graph = builder.compile()
+
+#Helper function to find the last LLM message/ response
+def final_ai_message(input: str) -> str:
+    final_ai_message_temp = None
+    for message in reversed(input):
+        if isinstance(message, AIMessage):
+            final_ai_message_temp = message.content
+            return final_ai_message_temp
+            break
+    return final_ai_message_temp
+
+sys_prompt = "You are a general AI assistant. I will ask you a question. Report your thoughts, and\nfinish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].\nYOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated\nlist of numbers and/or strings.\nIf you are asked for a number, don’t use comma to write your number neither use units such as $ or\npercent sign unless specified otherwise.\nIf you are asked for a string, don’t use articles, neither abbreviations (e.g. for cities), and write the\ndigits in plain text unless specified otherwise.\nIf you are asked for a comma separated list, apply the above rules depending of whether the element\nto be put in the list is a number or a string."
+
+#Create a function to interact with graph
+def chat_with_agent(user_input):
+    inputs = {
+        "messages": [
+            SystemMessage(content=sys_prompt),
+            HumanMessage(content=user_input)
+        ]
+    }
+    
+    # Run the graph
+    state = react_graph.invoke(inputs)
+
+    final_ai_message_text = final_ai_message(state["messages"])
+
+    return final_ai_message_text if final_ai_message_text else "Sorry, I couldn't find a response."

app.py

@@ -3,22 +3,101 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+# import agent
+from typing import Any
+from agent import chat_with_agent  # import the function directly
 
-# (Keep Constants as is)
-# --- Constants ---
+#Constants
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+QUESTIONS_URL = f"{DEFAULT_API_URL}/questions"
+SUBMIT_URL = f"{DEFAULT_API_URL}/submit"
+
+#Fetch Questions
+def fetchQuestions():
+    print(f"Fetching questions from: {QUESTIONS_URL}")
+    print("I am fetching questions from the server...")
+    try:
+        response = requests.get(QUESTIONS_URL, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+            print("Fetched questions list is empty.")
+            return "Fetched questions list is empty or invalid format.", None
+        return questions_data
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+    except requests.exceptions.JSONDecodeError as e:
+        print(f"Error decoding JSON response from questions endpoint: {e}")
+        print(f"Response text: {response.text[:500]}")
+        return f"Error decoding server response for questions: {e}", None
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
 
 # --- 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}")
+        fixed_answer = chat_with_agent(question)
+        print(f"Agent's answer (last 200 chars): {fixed_answer[-200:]}")
+
+        # --- FORCE FINAL ANSWER Format ---
+        if "FINAL ANSWER:" not in fixed_answer:
+            print("WARNING: FINAL ANSWER not found. Formatting...")
+            fixed_answer = f"FINAL ANSWER: {fixed_answer.strip()}"
+        
         return fixed_answer
 
+
+def runAgent(agent_object: BasicAgent, questions_data:list[Any]):
+    # gets submitted at the end
+    results_log = []
+    # For reference only
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        task_id = item.get("task_id")
+        print(f"Processing task_id: {task_id}")
+        question_text = item.get("question")
+        print(f"Question text (first 50 chars): {question_text[:50]}...")
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            submitted_answer = agent_object(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:
+            print(f"Error running agent on task {task_id}: {e}")
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "Submitted Answer": f"AGENT ERROR: {e}",
+                }
+            )
+            print(results_log)
+
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    return results_log, answers_payload
+
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
@@ -34,13 +113,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print("User not logged in.")
         return "Please Login to Hugging Face with the button.", None
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent_temp = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -49,47 +124,11 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     print(agent_code)
 
     # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+    questions_data = fetchQuestions()
+    print(questions_data)
 
     # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        try:
-            submitted_answer = agent(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:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+    results_log, answers_payload = runAgent(agent_temp, questions_data)
 
     # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
@@ -97,9 +136,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     print(status_update)
 
     # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    print(f"Submitting {len(answers_payload)} answers to: {SUBMIT_URL}")
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response = requests.post(SUBMIT_URL, json=submission_data, timeout=60)
         response.raise_for_status()
         result_data = response.json()
         final_status = (

requirements.txt

@@ -1,2 +1,5 @@
 gradio
-requests
+requests
+langchain
+langgraph
+langchain-groq