app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from langgraph.prebuilt import ToolNode, create_react_agent


# from typing import Any, Dict
# from typing import TypedDict, Annotated

from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain.schema import HumanMessage, AIMessage, SystemMessage
# Create a ToolNode that knows about your web_search function
import json
from state import AgentState

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

from tools import ocr_image_tool, parse_excel_tool, web_search_tool, run_tools
tool_node = ToolNode([ocr_image_tool, parse_excel_tool, web_search_tool])

llm = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0.0)

agent = create_react_agent(model=llm, tools=tool_node)

# ─── Revised plan_node with NO extra arguments ───
def plan_node(state: AgentState) -> AgentState:
    """
    Assumes that `state["messages"]` already ends with a HumanMessage of the user’s question.
    We look at that last HumanMessage, append it to our new history, and ask the LLM
    to set exactly one key in a Python dict: web_search_query, ocr_path,
    excel_path (+ excel_sheet_name), or final_answer.
    """
    # 1) Grab all prior BaseMessage objects (SystemMessage/HumanMessage/AIMessage) from state
    prior_msgs = state.get("messages", [])

    # 2) Find the very last HumanMessage (the user_input). We assume the last message is one.
    #    If there is no HumanMessage, we treat user_input as empty.
    user_input = ""
    for msg in reversed(prior_msgs):
        if isinstance(msg, HumanMessage):
            user_input = msg.content
            break

    # 3) Build our new chat history by re‐using prior_msgs. It already includes that HumanMessage.
    new_history = prior_msgs.copy()

    # 4) Add a SystemMessage that instructs the LLM how to choose exactly one key
    explanation = SystemMessage(
        content=(
            "You can set exactly one of the following keys in a Python dict, and nothing else:\n"
            "  • web_search_query: <search terms>  \n"
            "  • ocr_path: <path to an image file>  \n"
            "  • excel_path: <path to a .xlsx file>  \n"
            "  • excel_sheet_name: <sheet name>  \n"
            "Or, if no tool is needed, set final_answer: <your answer>.\n"
            "Example: {'web_search_query':'Mercedes Sosa discography'}\n"
            "Respond with only that Python dict literal—no extra text or explanation."
        )
    )

    # 5) Compose the prompt as a list of BaseMessage, then call the LLM
    prompt_messages = new_history + [explanation]
    llm_response = llm(prompt_messages)
    llm_out = llm_response.content.strip()

    # 6) Parse the LLM’s output as a dict
    try:
        parsed = eval(llm_out, {}, {})
        if isinstance(parsed, dict):
            partial: AgentState = {"messages": new_history}
            allowed = {
                "web_search_query",
                "ocr_path",
                "excel_path",
                "excel_sheet_name",
                "final_answer"
            }
            for k, v in parsed.items():
                if k in allowed:
                    partial[k] = v
            return partial
    except Exception:
        pass

    # 7) Fallback if parsing failed
    return {
        "messages": new_history,
        "final_answer": "Sorry, I could not parse your intent."
    }


# ─── Revised finalize_node with NO extra arguments ───
def finalize_node(state: AgentState) -> AgentState:
    """
    Assumes that `state['messages']` is a list of BaseMessage, possibly ending in an AIMessage
    (or plan_node may have set final_answer directly). We append any tool results
    as SystemMessages, then prompt the LLM for one final answer.
    """
    # 1) Copy the existing BaseMessage list
    history = state.get("messages", []).copy()

    # 2) If any tool-result fields exist, append them as SystemMessages
    if "web_search_result" in state and state["web_search_result"] is not None:
        history.append(SystemMessage(content=f"WEB_SEARCH_RESULT: {state['web_search_result']}"))
    if "ocr_result" in state and state["ocr_result"] is not None:
        history.append(SystemMessage(content=f"OCR_RESULT: {state['ocr_result']}"))
    if "excel_result" in state and state["excel_result"] is not None:
        history.append(SystemMessage(content=f"EXCEL_RESULT: {state['excel_result']}"))

    # 3) If plan_node already set final_answer, just return it:
    if state.get("final_answer") is not None:
        return {"final_answer": state["final_answer"]}

    # 4) Otherwise, ask the LLM to give the final answer now
    history.append(SystemMessage(content="Please provide the final answer now."))
    llm_response = llm(history)
    return {"final_answer": llm_response.content.strip()}

tool_node = ToolNode([web_search_tool, ocr_image_tool, parse_excel_tool])

# ─── 5) Build the StateGraph ───
graph = StateGraph(AgentState)

# 5.a) Register nodes
graph.add_node("plan", plan_node)
graph.add_node("tools", tool_node)
graph.add_node("run_tools", run_tools)
graph.add_node("finalize", finalize_node)

# 5.b) START → plan
graph.add_edge(START, "plan")






def route_plan(plan_out: AgentState) -> str:
    """
    plan_out is exactly what plan_node returned (a partial AgentState).
    If it set any of the tool-request keys, route to 'tools'; otherwise 'finalize'.
    """
    if plan_out.get("web_search_query") or plan_out.get("ocr_path") or plan_out.get("excel_path"):
        return "tools"
    return "finalize"

graph.add_conditional_edges(
    "plan",
    route_plan,
    {"tools": "tools", "finalize": "finalize"}
)



graph.add_edge("tools", "run_tools")

# 5.e) run_tools → finalize
graph.add_edge("run_tools", "finalize")

# 5.f) finalize → END
graph.add_edge("finalize", END)

compiled_graph = graph.compile()

def respond_to_input(user_input: str) -> str:
    """
    Initialize with a SystemMessage (tools description) and the user’s question as a HumanMessage.
    Then run through plan → tools → run_tools → finalize. Return the "final_answer" from final_state.
    """
    # 1) Create a SystemMessage that tells the agent about its tools
    system_msg = SystemMessage(
        content=(
            "You have access to exactly these tools:\n"
            "  1) web_search(query:str) → Returns the top search results for the query.\n"
            "  2) parse_excel(path:str, sheet_name:str) → Reads an Excel file and returns its contents.\n"
            "  3) ocr_image(path:str) → Runs OCR on an image and returns any detected text.\n\n"
            "If you need a tool, set exactly one of these keys in a Python‐dict:\n"
            "  • web_search_query: <search terms>\n"
            "  • ocr_path: <path to image>\n"
            "  • excel_path: <path to xlsx>\n"
            "  • excel_sheet_name: <sheet name>\n"
            "Otherwise, set final_answer: <your answer>.\n"
            "Respond with that Python dict literal—no extra text or explanation."
        )
    )

    # 2) Wrap the user_input in a HumanMessage
    human_msg = HumanMessage(content=user_input)

    # 3) Build the initial state so that "messages" contains both messages
    initial_state: AgentState = {
        "messages": [system_msg, human_msg],
        "user_input": user_input
    }

    # 4) Invoke the compiled graph (no second argument needed)
    final_state = compiled_graph.invoke(initial_state)

    # 5) Return the final answer (or a fallback if missing)
    return final_state.get("final_answer", "Error: No final answer generated.")



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 respond_to_input(question)
        # return fixed_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)
            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("LangGraph version:", langgraph.__version__) 
    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
    # import langgraph
    # print("▶︎ LangGraph version:", langgraph.__version__)
    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)