app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import logging
from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, OpenAIServerModel
from pathlib import Path

from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    Settings,
    set_global_handler
)
from llama_index.llms.openai import OpenAI

NUMERO_DOMANDE_TOTALE = 1

set_global_handler("simple")  # imposta un handler semplice per il logging
logging.getLogger().setLevel(logging.DEBUG)  # imposta il livello di log a DEBUG

class BasicAgent:
    def __init__(self):
        print("Initializing LlamaIndex-based agent...")

        # Leggi la chiave OpenAI dall'ambiente
        openai_api_key = os.getenv("OPENAI_API_KEY")
        if not openai_api_key:
            raise ValueError("OPENAI_API_KEY not set!")

        # Crea un'istanza di OpenAI
        llm = OpenAI(
            model="gpt-4o-mini",
            temperature=0,
            api_key=openai_api_key,
            verbose = True
        )

        # Imposta le impostazioni tramite Settings
        Settings.llm=llm
        

        # Carica i documenti dalla cartella 'data'
        self.documents = SimpleDirectoryReader("data").load_data()

        # Crea l'indice con la configurazione
        self.index = VectorStoreIndex.from_documents(self.documents, settings=Settings)

        # Prepara il query engine
        self.query_engine = self.index.as_query_engine()
        print("Agent ready.")
'''
    def __call__(self, question: str) -> str:
        print(f"Received question: {question[:50]}...")
        response = self.query_engine.query(question)

        # Stampa ragionamento interno
        print("Query response object:", response)
        print("Response text:", str(response))
        
        return str(response)
'''

    def __call__(self, question: str, context=None) -> str:
        if isinstance(context, str):
            #prompt = f"{context}\n\nDomanda: {question}"
            response = self.query_engine.query(question)
            # Stampa ragionamento interno
            print("Query response object:", response)
            print("Response text:", str(response))
            return response.text

        elif context and hasattr(context, "read"):  # file immagine o audio
            file_bytes = context.read()
            filename = context.name

            if filename.endswith((".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif")):
                print("coso entrato in video file:", filename)
                image = Image.open(io.BytesIO(file_bytes))
                response = self.query_engine.query(
                    messages=[
                        {"role": "user", "content": [
                            {"type": "text", "text": question},
                            {"type": "image_url", "image_url": image}
                        ]}
                    ]
                )
                return response.choices[0].message.content

            elif filename.endswith((".mp3", ".wav", ".ogg")):
                print("coso entrato in audio file:", filename)
                response = self.query_engine.query(
                    messages=[
                        {"role": "user", "content": [
                            {"type": "text", "text": question},
                            {"type": "audio_url", "audio_url": file_bytes}
                        ]}
                    ]
                )
                return response.choices[0].message.content

        else:
            print("coso entrato in else")
            response = self.llm.complete(question)
            return response.text



def answer_with_media(question, uploaded_file):
    file_content = ""
    file_path = None

    if uploaded_file is not None:
        file_path = Path(uploaded_file.name)
        suffix = file_path.suffix.lower()

        if suffix in [".txt", ".md", ".csv"]:
            with open(file_path, "r", encoding="utf-8") as f:
                file_content = f.read()
        elif suffix in [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".mp3", ".wav", ".ogg"]:
            # immagini/audio li gestiamo come path da passare al modello
            file_content = uploaded_file
        else:
            return "Formato file non supportato."

    response = agent(question, context=file_content)
    return response

gr.Interface(
    fn=answer_with_media,
    inputs=[
        gr.Textbox(label="Domanda"),
        gr.File(label="Carica file (testo, immagine o audio)")
    ],
    outputs=gr.Textbox(label="Risposta")
).launch()






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

##Roba per la valutazione        

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()
        total_questions = response.json()
        questions_data = total_questions[:NUMERO_DOMANDE_TOTALE]
        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("\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)