app.py

import os
import re
import gradio as gr
import requests
import inspect
import pandas as pd
import time

# --- LangChain Imports 
import constants
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import SystemMessage

# --- Custom Tools ---
from wikipedia_tool import wikipedia_revision_by_year_keyword
from count_max_bird_species_tool import count_max_bird_species_in_video
from image_to_text_tool import image_to_text
from internet_search_tool import internet_search
from botanical_classification_tool import get_botanical_classification
from excel_parser_tool import parse_excel
from download_task_file import download_file_as_base64
from utils import get_bytes, get_text_file_contents, get_base64

# (Keep Constants as is) ok!
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

class LangChainAgent:
    def __init__(self):
        llm = ChatGoogleGenerativeAI(
            model=constants.MODEL,
            api_key=constants.API_KEY, 
            temperature=0.7)

        tools = [
            wikipedia_revision_by_year_keyword,
            count_max_bird_species_in_video,
            image_to_text,
            internet_search,
            get_botanical_classification,
            parse_excel
        ]

        prompt = ChatPromptTemplate.from_messages([
            SystemMessage(content=(constants.PROMPT_LIMITADOR_LLM)),
            MessagesPlaceholder(variable_name="chat_history"),
            ("human", "{input}"),
            MessagesPlaceholder(variable_name="agent_scratchpad"),
        ])

        agent = create_tool_calling_agent(llm, tools, prompt=prompt)
        self.executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

    def __call__(self, question: str) -> str:
        print(f"LangChain agent received: {question[:50]}...")
        
        download_file_as_base64
        
        #print("Waiting 60s before answering")
        #time.sleep(10)  # Delay for 60 seconds
        
        result = self.executor.invoke({
            "input": question,
            "chat_history": []
        })
        output = result.get("output", "No answer returned.")
        print(f"Agent response: {output}")
        match = re.search(r"FINAL ANSWER:\s*(.*)", output)
        if match:
            return match.group(1).strip()
        else:
            return output

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 = LangChainAgent()
    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:
            file_name = item.get("file_name")
            #provisorio
            #if not file_name and "bird" not in question_text:
            #    continue
            question_text_for_agent = question_text
            if file_name:
                print(f"The following question has attatched file: {question_text[:50]}")
                BASE_DIR = os.path.dirname(os.path.abspath(__file__))
                if file_name.endswith(('.mp3', '.xlsx', '.png')):
                    file_path = os.path.join(BASE_DIR, 'files', f'{file_name}.b64')
                    base64_attatched_file = get_text_file_contents(file_path)
                    question_text_for_agent += f'. The content in base64 of the attatched file mentioned in the question is the following: {base64_attatched_file}'
                else:
                    file_path = os.path.join(BASE_DIR, 'files', file_name)
                    plain_txt_file = get_text_file_contents(file_path)
                    question_text_for_agent += f'. The attatchement has the following content: {plain_txt_file}'				
                question_text_for_agent += f'. The content in base64 of the attatched file mentioned in the question is the following: {base64_attatched_file}'
            submitted_answer = agent(question_text_for_agent)
            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__":
    files_dir = os.path.join(os.path.dirname(__file__), "files")
    if not os.path.exists(files_dir):
        print("Directory 'files/' does not exist.")
    else:
        print("\n".join(os.listdir(files_dir)))
		
    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)