app.py

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


# 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, SystemMessage, AIMessage
# 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, audio_transcriber_tool, wikipedia_search_tool

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

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

# ─── 2) Revised plan_node ───
def plan_node(state: AgentState) -> AgentState:
    prior_msgs = state.get("messages", [])
    user_input = ""
    for msg in reversed(prior_msgs):
        if isinstance(msg, HumanMessage):
            user_input = msg.content
            break

    # (1) Build your system/human messages exactly as before
    system_msg = SystemMessage(
        content=(
            "You are an agent that decides whether to call a tool or answer directly.\n"
            "User's question: \"" + user_input + "\"\n\n"
            "• If you can answer directly, return exactly {\"final_answer\":\"<your answer>\"}.\n"
            "• Otherwise, respond with exactly one of:\n"
            "    {\"web_search_query\":\"<search terms>\"}\n"
            "    {\"ocr_path\":\"<path to image>\"}\n"
            "    {\"excel_path\":\"<path to xlsx>\", \"excel_sheet_name\":\"<sheet>\"}\n"
            "    {\"audio_path\":\"<path to audio file>\"}\n"
            "    {\"wiki_query\":\"<wikipedia search terms>\"}\n"
            "Do not include any extra characters or markdown—only the JSON literal."
        )
    )
    human_msg = HumanMessage(content=user_input)

    # (2) Call the LLM
    llm_response = llm.invoke([system_msg, human_msg])
    llm_out = llm_response.content.strip()

    # ── DEBUG: print raw LLM output ──
    # print("\n>>> plan_node got raw LLM output:")
    # print(llm_out)
    # print("<<< end raw output\n")

    # (3) Append the LLM output to the message history
    ai_msg = AIMessage(content=llm_out)
    new_msgs = prior_msgs.copy() + [ai_msg]

    # (4) Try parsing as JSON
    try:
        parsed = json.loads(llm_out)
        # print(">>> plan_node parsed JSON:", parsed)
        if isinstance(parsed, dict):
            partial: AgentState = {"messages": new_msgs}
            allowed = {
                "web_search_query",
                "ocr_path",
                "excel_path",
                "excel_sheet_name",
                "audio_path",
                "wiki_query",
                "final_answer"
            }
            for k, v in parsed.items():
                if k in allowed:
                    partial[k] = v
                    # print(f">>> plan_node is setting {k!r} → {v!r}")
            return partial
    except json.JSONDecodeError as e:
        # print(">>> plan_node JSON parse error:", e)
        pass
    # (5) Fallback
    # print(">>> plan_node falling back to final_answer alone\n")
    return {"messages": new_msgs, "final_answer": "Sorry, I could not parse your intent."}



# ─── 3) Revised finalize_node ───
def finalize_node(state: AgentState) -> AgentState:
    if state.get("final_answer") is not None:
        return {"final_answer": state["final_answer"]}

    # Re‐extract the last user question
    question = ""
    for msg in reversed(state.get("messages", [])):
        if isinstance(msg, HumanMessage):
            question = msg.content
            break

    # Build one monolithic context
    combined = f"USER_QUESTION: {question}\n"
    if sr := state.get("web_search_result"):
        combined += f"WEB_SEARCH_RESULT: {sr}\n"
    if orc := state.get("ocr_result"):
        combined += f"OCR_RESULT: {orc}\n"
    if exr := state.get("excel_result"):
        combined += f"EXCEL_RESULT: {exr}\n"
    # Note: your code already stores the audio transcription under "transcript"
    if tr := state.get("transcript"):
        combined += f"AUDIO_TRANSCRIPT: {tr}\n"
    if wr := state.get("wiki_result"):
        combined += f"WIKIPEDIA_RESULT: {wr}\n"

    # Here we demand a JSON response with a single key "final_answer"
    combined += (
        "Based on the above, respond with exactly one JSON object, and nothing else. "
        "The JSON object must have exactly one key: \"final_answer\". "
        "For example:\n"
        "{\"final_answer\":\"42\"}\n"
        "Do NOT include any explanation, markdown, or any extra whitespace outside the JSON object. "
        "If the answer is multiple words, put them in a comma-separated string, e.g. \"red,green,blue\". "
        "If the answer is a number, it must be digits only—e.g. \"725.00\".\n"
        "If the answer is a list of items, put them in a comma-separated string, e.g. \"item1,item2,item3\". "
        "If the user prompt asks you to do something, then do it "
    )

    # Debug print
    # print("\n>>> finalize_node JSON‐strict prompt:\n" + combined + "\n<<< end prompt >>>\n")

    llm_response = llm.invoke([SystemMessage(content=combined)])
    raw = llm_response.content.strip()
    # print(">>> finalize_node got raw response:", raw)

    try:
        parsed = json.loads(raw)
        return {"final_answer": parsed["final_answer"]}
    except Exception as e:
        # If the LLM did not return valid JSON, store the error so you can see it
        # print(">>> finalize_node JSON parse error:", e, "raw was:", raw)
        return {"final_answer": f"ERROR: invalid JSON from finalize_node: {raw}"}

# ─── 4) Wrap tools in a ToolNode ───
def tool_node(state: AgentState) -> AgentState:
    """
    Inspect exactly which tool‐key was set in `state` and call that function.
    Returns only the partial state (with the tool's outputs) so that merge_tool_output can combine it.
    """
    # We expect exactly one of these keys to be non‐empty:
    #   "web_search_query", "ocr_path", "excel_path"/"excel_sheet_name", "audio_path"
    # Whichever is present, call the corresponding tool and return its result.
    
    if state.get("wiki_query"):
        out = wikipedia_search_tool(state)
        return out
    
    if state.get("web_search_query"):
        # print(f">>> tools_node dispatching web_search_tool with query: {state['web_search_query']!r}")
        out = web_search_tool(state)
        return out

    if state.get("ocr_path"):
        # print(f">>> tools_node dispatching ocr_image_tool with path: {state['ocr_path']!r}")
        out = ocr_image_tool(state)
        return out

    if state.get("excel_path"):
        # We assume plan_node always sets both excel_path and excel_sheet_name together
        # print(f">>> tools_node dispatching parse_excel_tool with path: {state['excel_path']!r}, sheet: {state.get('excel_sheet_name')!r}")
        out = parse_excel_tool(state)
        return out

    if state.get("audio_path"):
        # print(f">>> tools_node dispatching audio_transcriber_tool with path: {state['audio_path']!r}")
        out = audio_transcriber_tool(state)
        return out



    # If we somehow reach here, no recognized tool key was set:
    # print(">>> tools_node: no valid tool key found in state!")
    return {}


# Add a node to store the previous state

def store_prev_state(state: AgentState) -> AgentState:
    return {**state, "prev_state": state.copy()}

def merge_tool_output(state: AgentState) -> AgentState:
    prev_state = state.get("prev_state", {})
    merged = {**prev_state, **state}
    merged.pop("prev_state", None)
    return merged

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

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

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

# 5.c) plan → conditional: if any tool key was set, go to "tools"; otherwise "finalize"
def route_plan(plan_out: AgentState) -> str:
    # print what keys are present in plan_out
    # print(f">> route_plan sees plan_out keys: {list(plan_out.keys())}")

    if (
        plan_out.get("web_search_query")
        or plan_out.get("ocr_path")
        or plan_out.get("excel_path")
        or plan_out.get("audio_path")
        or plan_out.get("wiki_query")
    ):
        # print(">> route_plan ➡️ tools")
        return "tools"
    # print(">> route_plan ➡️ finalize")
    return "finalize"


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

# 5.d) store_prev_state → tools
graph.add_edge("store_prev_state", "tools")

# 5.e) tools → merge_tool_output
graph.add_edge("tools", "merge_tool_output")

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

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

compiled_graph = graph.compile()


# ─── 6) respond_to_input ───
def respond_to_input(user_input: str, task_id) -> str:
    """
    Seed state['messages'] with a SystemMessage (tools description) + HumanMessage(user_input).
    Then invoke the graph; return the final_answer from the resulting state.
    """
    system_msg = SystemMessage(
    content=(
        "You are an agent that picks exactly one course of action:\n"
        "Use Wikipedia first before even using web search Please. "
        "  1) If the user's question is best answered by looking up a Wikipedia summary, return\n"
        "     {\"wiki_query\":\"<query for Wikipedia>\"} and nothing else.\n"
        "  2) Otherwise, if Wikipedia won't directly help, but you still need web results, return\n"
        "     {\"web_search_query\":\"<search terms>\"} and nothing else.\n"
        "  3) Otherwise, if it's an image, use OCR: return {\"ocr_path\":\"<path>\"}.\n"
        "  4) Otherwise, if it's a spreadsheet question, return {\"excel_path\":\"<path>\", \"excel_sheet_name\":\"<sheet>\"}.\n"
        "  5) Otherwise, if it's an audio transcription, return {\"audio_path\":\"<path to audio file>\"}.\n"
        "  6) If you can answer immediately without any tool, return exactly {\"final_answer\":\"<your answer>\"}.\n"
        "Do NOT include any other keys, text, or explanation—just one JSON object with exactly one of those keys."
    )
)
    human_msg = HumanMessage(content=user_input)

    initial_state: AgentState = {"messages": [system_msg, human_msg], "task_id": task_id}
    final_state = compiled_graph.invoke(initial_state)
    return final_state.get("final_answer", "Error: No final answer generated.")




class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
    def __call__(self, question: str, task_id) -> 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}")
        print()
        print()
        print()
        print()
        
        
        print(f"Agent received question: {question}")
        print()
        return respond_to_input(question, task_id)
        # 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, task_id)
            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)