app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.prompts import ChatMessagePromptTemplate, ChatPromptTemplate
from langchain_core.tools import render_text_description
from langchain_core.messages import HumanMessage, SystemMessage
from Agents.agent import build_graph, tools,systemPrompt
from langfuse.langchain import CallbackHandler
from langgraph.prebuilt import create_react_agent
import traceback

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
langfuse_handler = CallbackHandler()
testMode = bool(os.getenv("TestMode"))
langFuseOn = bool(os.getenv("LangFuseOn"))
agentType = os.getenv("AgentType")
gaiaValidationURL = os.getenv("GaiaValidationURL")

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------

class ReactAgent:
    def __init__(self):
        print(f"tools: {tools}")
        print(f"prompt : {systemPrompt}")
        graph = create_react_agent("qwen:qwen3-32b",
            #"anthropic:claude-3-7-sonnet-latest",
            tools=tools,
            prompt=systemPrompt,
        )

    def __call__(self,question:str) -> str:
        inputs = {"messages": [{"role": "user", "content": "{question}"}]}
        for chunk in self.graph.stream(inputs, stream_mode="updates"):
            print(chunk)

class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        self.graph = build_graph()
    def __call__(self, question: str, taskID: str="") -> str:
        print(f"Agent received question (first 200 chars): {question[:200]}...")
        messages = [HumanMessage(content=question, additional_kwargs={"taskID":taskID })]

        '''prompt = ChatPromptTemplate.from_messages([
                        SystemMessage(content="systemPrompt"),
                        ("human", "{question}"),
                ])
        '''
        
        
        if(not langFuseOn):
            print("no langfuse")
            formattedMessage = [{"role":"user","content":msg.content} for msg in messages]
            formattedMessage.append(SystemMessage(content="systemPrompt"))
            print(f"formattedMessage : {formattedMessage}")
            messages = self.graph.invoke({"messages": formattedMessage})
        else:
            print("active langfuse") 
            additionalDetails = f" The taskID is : {taskID}"
            print(f"additionalDetials: {additionalDetails}")
            formattedMessage = [{"role": "system", "content": systemPrompt + "\n" + additionalDetails,"additional_kwargs": {"taskID": taskID}}] + \
                [{"role":"user","content":msg.content,"additional_kwargs": {"taskID": taskID}} for msg in messages]
            #formattedMessage.append({"role": "system", "content": systemPrompt})
            #formattedMessage.append(SystemMessage(content="systemPrompt"))
            print(f"formattedMessage : {formattedMessage}")
            messages = self.graph.invoke({"messages": formattedMessage},config={"callbacks": [langfuse_handler]})

        #self.graph.get_graph().draw_mermaid_png()
        answer = messages['messages'][-1].content
        print(f"ANWSER: {answer}")
        #tools_calls = messages[-1].tool_calls
        #print(f"tool calls : {str(tools_calls)}")
        #print(messages[-1].__dict__)
        #print(f"full answer : {answer}")
        #print("Assistant Output:", messages[-1])
        final_answer = answer.split("FINAL ANSWER: ")[-1]
        print(final_answer)
        return final_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:
        if(agentType == "React"):
            agent = ReactAgent()
        else:
            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)
    
    print(f"testmode: {testMode}")
    if(testMode):
        # 5a test
        #question = "When was a picture of St. Thomas Aquinas first added to the wikipedia page on the principle of double effect?"
        question = "Who are the pitchers with the number before and after Taishō Tamai's number as of July 2023? Give them to me in the form Pitcher Before, Pitcher After, use their last names only, in Roman characters."
        #graph = build_graph(provider="groq")
        try:
            submitted_answer = agent(question)
            print(f"submitted_answer: {submitted_answer}")
        except Exception as e:
            print(f"Error running agent on task {e}")
            traceback.print_exc()
            print(f"AGENT ERROR: {str(e)}")
            status_message = f"AGENT ERROR: {e}"

        status_message = "Submission Successful"
        print(status_message)
        results_df = pd.DataFrame([])
        return status_message, results_df
    else:
        # 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")
            file_name = item.get("file_name")
            question_text = item.get("question")
            print(f"running on Question data {question_text}")
            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,str(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("\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)