app.py

import base64
import datetime
import os
import tempfile
from pathlib import Path
from typing import List, Optional, Tuple

import gradio as gr
import pandas as pd
import requests
from langchain_core.messages import HumanMessage

from agent import BasicAgent

# ── Bootstrap ChromaDB if not present ─────────────────────────────────────────
_CHROMA_DIR = Path("chroma_db")
_QUESTIONS_FILE = Path("gaia_questions.jsonl")

if not _CHROMA_DIR.exists() and _QUESTIONS_FILE.exists():
    print("ChromaDB not found — running ingest from gaia_questions.jsonl ...")
    try:
        from ingest import main as _ingest_main
        _ingest_main(_QUESTIONS_FILE)
        print("Ingest complete.")
    except Exception as _e:
        print(f"Warning: ingest failed ({_e}). question_search tool will be unavailable.")

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


# ── Evaluation runner ──────────────────────────────────────────────────────────

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    space_id = os.getenv("SPACE_ID")

    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
    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    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 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

        file_name = item.get("file_name", "")
        file_url = f"{api_url}/files/{task_id}" if file_name else None

        try:
            submitted_answer = agent(
                question_text,
                file_url=file_url,
                file_name=file_name or None,
            )
            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.")
        return final_status, pd.DataFrame(results_log)
    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)
        return status_message, pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        return status_message, pd.DataFrame(results_log)
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)


# ── Q&A Chatbot ────────────────────────────────────────────────────────────────

class QnAChatbot:
    """Interactive chatbot wrapping BasicAgent for the Q&A tab."""

    def __init__(self):
        print("Initialising QnAChatbot...")
        self.agent = BasicAgent()
        print("QnAChatbot ready.")

    def process_question(
        self,
        question: str,
        history: List[dict],
        uploaded_files: Optional[List] = None,
    ) -> Tuple[str, List[dict], None]:
        if not question.strip() and not uploaded_files:
            return "", history, None

        try:
            file_context = self._process_uploaded_files(uploaded_files or [])
            if file_context:
                question = f"{question}\n\n{file_context}" if question.strip() else file_context

            result = self.agent.graph.invoke({"messages": [HumanMessage(content=question)]})
            answer = result["messages"][-1].content
            if answer.startswith("Assistant: "):
                answer = answer[len("Assistant: "):]

            history.append({"role": "user", "content": question})
            history.append({"role": "assistant", "content": answer})
            return "", history, None

        except Exception as e:
            import traceback
            print(f"Error: {e}\n{traceback.format_exc()}")
            history.append({"role": "user", "content": question})
            history.append({"role": "assistant", "content": f"Agent error: {e}"})
            return "", history, None

    def _process_uploaded_files(self, uploaded_files: List) -> str:
        contexts = []
        for file_path in uploaded_files:
            if not file_path or not os.path.exists(file_path):
                continue
            try:
                name = os.path.basename(file_path)
                ext = os.path.splitext(name)[1].lower()

                if ext in {".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp"}:
                    with open(file_path, "rb") as f:
                        b64 = base64.b64encode(f.read()).decode()
                    contexts.append(f"[IMAGE: {name}] Base64: {b64}")
                elif ext in {".txt", ".md", ".py", ".js", ".html", ".css", ".json", ".xml"}:
                    with open(file_path, encoding="utf-8") as f:
                        contexts.append(f"[TEXT FILE: {name}]\n{f.read()}")
                elif ext == ".csv":
                    contexts.append(f"[CSV FILE: {name}] Path: {file_path}")
                elif ext in {".xlsx", ".xls"}:
                    contexts.append(f"[EXCEL FILE: {name}] Path: {file_path}")
                elif ext == ".pdf":
                    contexts.append(f"[PDF FILE: {name}] Path: {file_path}")
                else:
                    contexts.append(f"[FILE: {name}] Path: {file_path}")
            except Exception as e:
                contexts.append(f"[ERROR reading {os.path.basename(file_path)}]: {e}")
        return "\n\n".join(contexts)

    def clear(self):
        return []


def export_chat(history: List[dict]):
    if not history:
        return None
    lines = [f"# GAIA Agent Chat Export\nDate: {datetime.datetime.now()}\n\n"]
    for i, msg in enumerate(history, 1):
        role = msg.get("role", "unknown").capitalize()
        content = msg.get("content", "")
        lines.append(f"## [{i}] {role}\n{content}\n\n---\n\n")
    tmp = tempfile.NamedTemporaryFile(
        mode="w", suffix=".md", delete=False, encoding="utf-8"
    )
    tmp.write("".join(lines))
    tmp.close()
    return tmp.name


# ── Gradio UI ──────────────────────────────────────────────────────────────────

chatbot_instance = QnAChatbot()

with gr.Blocks(title="GAIA Agent") as demo:
    gr.Markdown("# GAIA Agent")

    with gr.Tabs():

        # ── Tab 1: Evaluation runner ───────────────────────────────────────────
        with gr.Tab("Evaluation Runner"):
            gr.Markdown(
                """
                **Instructions:**
                1. Log in to your Hugging Face account using the button below.
                2. Click **Run Evaluation & Submit All Answers** to fetch questions, run the agent, and submit.

                > This can take a while — the agent processes every question sequentially.
                """
            )
            if os.getenv("SPACE_ID"):  # only render inside HF Space
                gr.LoginButton()
            run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
            status_output = gr.Textbox(
                label="Run Status / Submission Result", lines=5, interactive=False
            )
            results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
            run_button.click(
                fn=run_and_submit_all,
                outputs=[status_output, results_table],
            )

        # ── Tab 2: Q&A Chatbot ─────────────────────────────────────────────────
        with gr.Tab("Q&A Chatbot"):
            gr.Markdown(
                """
                Ask the agent anything. Upload files (images, CSV, Excel, PDF, text) to include them as context.
                """
            )
            chatbot_ui = gr.Chatbot(label="Conversation", height=500)

            with gr.Row():
                question_input = gr.Textbox(
                    label="Your question",
                    placeholder="Type your question here…",
                    lines=2,
                    scale=8,
                )
                send_btn = gr.Button("Send", variant="primary", scale=1)

            file_upload = gr.File(
                label="Attach files (optional)",
                file_count="multiple",
                file_types=[
                    ".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp",
                    ".txt", ".md", ".py", ".js", ".html", ".css", ".json", ".xml",
                    ".csv", ".xlsx", ".xls", ".pdf", ".doc", ".docx",
                ],
            )

            with gr.Row():
                clear_btn = gr.Button("Clear History", variant="secondary")
                export_btn = gr.Button("Export Chat", variant="secondary")
            download_file = gr.File(visible=False)

            gr.Examples(
                examples=[
                    "What is the capital of France?",
                    "Calculate the 15th Fibonacci number using code.",
                    "Search Wikipedia for the history of the Eiffel Tower.",
                    "What are the latest AI research papers on arXiv?",
                ],
                inputs=question_input,
            )

            # Events
            def _submit(question, history, files):
                return chatbot_instance.process_question(question, history, files)

            send_btn.click(
                fn=_submit,
                inputs=[question_input, chatbot_ui, file_upload],
                outputs=[question_input, chatbot_ui, file_upload],
                show_progress=True,
            )
            question_input.submit(
                fn=_submit,
                inputs=[question_input, chatbot_ui, file_upload],
                outputs=[question_input, chatbot_ui, file_upload],
                show_progress=True,
            )
            clear_btn.click(fn=chatbot_instance.clear, outputs=[chatbot_ui])
            export_btn.click(fn=export_chat, inputs=[chatbot_ui], outputs=[download_file])


if __name__ == "__main__":
    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
    space_host = os.getenv("SPACE_HOST")
    space_id = os.getenv("SPACE_ID")
    if space_host:
        print(f"SPACE_HOST: {space_host}")
    if space_id:
        print(f"SPACE_ID: {space_id}")
    print("-" * (60 + len(" App Starting ")) + "\n")
    demo.launch(debug=True, share=False, theme=gr.themes.Soft())