app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from IPython.display import Image, display
from typing import TypedDict, List, Dict, Any, Optional
from langgraph.graph import StateGraph, START, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AIMessage
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
#from langchain_community.tools import DuckDuckGoSearchRun
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain.tools import Tool
from serpapi import GoogleSearch

# (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 ------

SERPAPI_API_KEY = os.environ["SERPAPI_TOKEN"]

def serpapi_search(query: str) -> str:
    print(f"Running SerpAPI search for: {query}")
    params = {
        "engine": "google",
        "q": query,
        "api_key": SERPAPI_API_KEY,
        "num": 3,
    }
    search = GoogleSearch(params)
    results = search.get_dict()
    if "organic_results" in results:
        snippets = [item.get("snippet", "") for item in results["organic_results"]]
        return "\n".join(snippets)
    return "No results found."

serpapi_tool = Tool(
    name="serpapi_search",
    func=serpapi_search,
    description="A tool that allows you to search the web using Google via SerpAPI. Input should be a search query."
)

# Initialize LLM
model = ChatOpenAI( model="gpt-4o",temperature=0)
vision_llm = ChatOpenAI(model="gpt-4o")

#search_tool = DuckDuckGoSearchRun()
tools = [serpapi_tool]

llm_with_tools = model.bind_tools(tools, parallel_tool_calls=False)

class AgentState(TypedDict):
    question: Dict[str, Any]
    messages: List[Any]
    answer: Optional[str]
    tool_calls: Optional[list]
    tool_outputs: Optional[list]

def assistant(state: AgentState):
    print("\n--- ASSISTANT NODE ---")
    print(f"State received: {state}")
    question = state["question"]
    print(f"Question dict: {question}")
    #textual_description_of_tool = """
    #search_tool: A tool that allows you to search the web using DuckDuckGo. It returns a list of search results based on the query provided.
    #"""
    textual_description_of_tool = """
    serpapi_search: A tool that allows you to search the web using Google via SerpAPI. It returns a list of search results based on the query provided.
    """
    system_prompt = SystemMessage(
        content=f"""
        You are an expert assistant. Try to answer the question as accurately as possible.
        You can use the following tools to help you:
        {textual_description_of_tool}
        """
    )
    # Always include conversation history
    messages = [system_prompt] + state.get("messages", [])
    # Add the user question only if not already present
    if not any(isinstance(m, HumanMessage) and m.content.startswith("Question:") for m in messages):
        user_prompt = HumanMessage(content=f"Question: {question.get('question', question)}")
        messages.append(user_prompt)
    # If tool_outputs exist, add them as context
    if state.get("tool_outputs"):
        tool_msg = HumanMessage(content=f"Tool results: {state['tool_outputs']}")
        messages.append(tool_msg)
        state.setdefault("messages", []).append(tool_msg)
    print(f"Messages sent to LLM: {messages}")
    response = llm_with_tools.invoke(messages)
    print(f"Raw LLM response: {response}")
    # If the LLM wants to call a tool, store tool_calls in state
    tool_calls = getattr(response, "tool_calls", None)
    if tool_calls:
        print(f"Tool calls requested: {tool_calls}")
        state["tool_calls"] = tool_calls
        state["answer"] = ""  # Not final yet
        state.setdefault("messages", []).append(AIMessage(content="Calling tool: " + str(tool_calls)))
    else:
        state["answer"] = response.content.strip()
        print(f"Model response: {state['answer']}")
        state.setdefault("messages", []).append(AIMessage(content=state["answer"]))
    return state

def tool_node(state: AgentState):
    print("\n--- TOOL NODE ---")
    print(f"State received: {state}")
    outputs = []
    for call in state.get("tool_calls", []):
        print(f"Tool call: {call}")
        args = call.get("args", {})
        # Try to get 'query' or fallback to the first value
        query = args.get("query")
        if query is None and len(args) > 0:
            query = list(args.values())[0]
        print(f"Query to use: {query}")
        if call["name"] == "serpapi_search":
            try:
                result = serpapi_search(query)
            except Exception as e:
                print(f"Error running SerpAPI search: {e}")
                result = f"Error: {e}"
            outputs.append(result)
    state["tool_outputs"] = outputs
    state["tool_calls"] = None  # Clear tool calls
    # Append tool output to conversation history
    state.setdefault("messages", []).append(HumanMessage(content=f"Tool results: {outputs}"))
    return state

#building the graph
answering_graph = StateGraph(AgentState)

# Add nodes
answering_graph.add_node("assistant", assistant)
#answering_graph.add_node("tools", ToolNode(tools))
answering_graph.add_node("tools", tool_node)

# Add edges
answering_graph.add_edge(START, "assistant")
answering_graph.add_conditional_edges(
    "assistant",
    lambda state: "tools" if state.get("tool_calls") else END
)
answering_graph.add_edge("tools", "assistant")

# Compile the graph
compiled_graph = answering_graph.compile()

class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
    def __call__(self, question: str) -> str:
        question_text = question.get("question")
        print(f"Agent received question (first 50 chars): {question_text[:50]}...")

        initial_state = {
            "question": question,
            "messages": [],
            "answer": None
        }

        print(f"Initial state: {initial_state}")
        answer = compiled_graph.invoke(initial_state)
        print(f"Agent returning answer: {answer}")
        return answer

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    if profile:
        username= f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        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()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    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

    # 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)
            submitted_answer = agent(item)
            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)

    # 4. Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.

        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup: # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)