app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import asyncio
from smolagents import ToolCallingAgent, InferenceClientModel
from smolagents import DuckDuckGoSearchTool, Tool, CodeAgent
from huggingface_hub import login
#hi
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

login(token=os.environ["HUGGINGFACEHUB_API_TOKEN"])

## TOOL 1
search_tool = DuckDuckGoSearchTool()

class WikipediaTool(Tool):
    name = "wikipedia_search"
    description = "Search Wikipedia for a given topic and return a concise summary."
    inputs = {
        "query": {
            "type": "string",
            "description": "The topic to search on Wikipedia"
        }
    }
    output_type = "string"

    def forward(self, query: str) -> str:
        try:
            page = wikipedia.page(query)
            return page.summary[:1000]  # truncate to first 1000 characters
        except wikipedia.exceptions.DisambiguationError as e:
            return f"Disambiguation: {', '.join(e.options[:5])}"
        except wikipedia.exceptions.PageError:
            return "No Wikipedia page found."
        except Exception as e:
            return f"Error: {str(e)}"


# --- Tool 2: Audio Transcription Tool (uses whisper base) ---
class AudioTranscriptionTool(Tool):
    name = "audio_transcriber"
    description = "Transcribes an uploaded audio file to text using Whisper."
    inputs = {
        "audio": {
            "type": "audio",
            "description": "The audio file to transcribe"
        }
    }
    output_type = "string"

    def forward(self, audio: str) -> str:
        try:
            model = whisper.load_model("base")
            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
                tmp_file.write(audio.read())
                tmp_file_path = tmp_file.name

            result = model.transcribe(tmp_file_path)
            os.remove(tmp_file_path)
            return result["text"]
        except Exception as e:
            return f"Transcription failed: {str(e)}"


# --- Tool 3: Excel Reader Tool ---
class ExcelReaderTool(Tool):
    name = "excel_reader"
    description = "Reads uploaded Excel file and summarizes sheet data."
    inputs = {
        "excel": {
            "type": "file",
            "description": "The Excel (.xlsx) file to summarize"
        }
    }
    output_type = "string"

    def forward(self, excel) -> str:
        try:
            with tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx") as tmp_file:
                tmp_file.write(excel.read())
                tmp_file_path = tmp_file.name

            df = pd.read_excel(tmp_file_path)
            summary = df.describe(include="all").to_string()
            os.remove(tmp_file_path)
            return f"Summary statistics:\n{summary}"
        except Exception as e:
            return f"Failed to read Excel file: {str(e)}"


async def run_and_submit_all(profile: gr.OAuthProfile | None):
    log_output = ""

    try:
        agent = ToolCallingAgent(
            tools=[search_tool, WikipediaTool(), AudioTranscriptionTool(), ExcelReaderTool()],
            model=InferenceClientModel(model="deepseek-ai/DeepSeek-V3", provider="together"),
            max_steps=15,
            verbosity_level=0,
        )
    except Exception as e:
        yield f"Error initializing agent: {e}", None, log_output
        return

    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    questions_url = f"{DEFAULT_API_URL}/questions"
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()

        #selected_indices = [0,2,3,4,5,8,9,10,11,14,15,16,18]  # 2nd, 5th, 6th, and 8th questions
        #questions_data = [questions_data[i] for i in selected_indices if i < len(questions_data)]

        
        if not questions_data:
            yield "Fetched questions list is empty or invalid format.", None, log_output
            return
    except Exception as e:
        yield f"Error fetching questions: {e}", None, log_output
        return

    results_log = []
    answers_payload = []
    loop = asyncio.get_event_loop()

    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:
            continue

        log_output += f"🔍 Solving Task ID: {task_id}...\n"
        yield None, None, log_output

        try:
            system_prompt = (
                "You are a general AI assistant. I will ask you a question. "
                "Report your thoughts, and finish your answer with the following template: "
                "FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. "
                "If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. "
                "If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. "
                "If the answer is that no such event or person exists, respond with the single word 'none' or 'no one' as appropriate. Avoid long explanations."
                "ALWAYS answer with the least amount of words (single word wherever possible). If the answer is 'right', say 'right, and not 'the answer is right'"
                "Only respond with your final answer using the format: FINAL ANSWER: [your answer]. Do not include reasoning, context, explanations, markdown, or additional sections."   
                "Do not provide short answers and detailed answers seperately. You ONLY have to answer with the shortest answer possible. We DO NOT need explanations. Also dont use 'task outcome:'"
                "You are a precise and concise AI assistant competing in a strict evaluation benchmark."
                "You are a precise and concise AI assistant competing in a strict evaluation benchmark.  You will be asked a question. You must only output a single final answer, using the following format exactly: FINAL ANSWER: [your answer]"
                "Guidelines: Do NOT explain your reasoning. Do NOT output markdown, sections, titles, bullet points, or formatting like (Task outcome), (Detailed version), or (Additional context). Do NOT include references, sources, or citations. Do NOT include any leading text like (Here is the final answer), (The correct answer is), or (Answer):.  Do NOT repeat the question or summarize it."
                "Your response must consist of a single line starting with: FINAL ANSWER: and nothing else."
                "Normalization rules: If the answer is a list, apply the above rules to each item, and separate them with commas. If the answer is a number, write it as digits (no commas, no units unless asked). If the answer is a string, use lowercase, no articles (a, an, the), and no abbreviations."
                "If there is no correct answer (e.g., no such person, place, or event), respond with: FINAL ANSWER: none"
                "If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.\n\n"
            )
            full_prompt = system_prompt + f"Question: {question_text.strip()}"

            agent_result = await loop.run_in_executor(None, agent, full_prompt)

            # Extract final answer cleanly
            if isinstance(agent_result, dict) and "final_answer" in agent_result:
                final_answer = str(agent_result["final_answer"]).strip()
            elif isinstance(agent_result, str):
                response_text = agent_result.strip()

                # Remove known boilerplate
                if "Here is the final answer from your managed agent" in response_text:
                    response_text = response_text.split(":", 1)[-1].strip()

                if "FINAL ANSWER:" in response_text:
                    _, final_answer = response_text.rsplit("FINAL ANSWER:", 1)
                    final_answer = final_answer.strip()
                else:
                    final_answer = response_text
            else:
                final_answer = str(agent_result).strip()

            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": final_answer
            })

            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": final_answer
            })

            log_output += f"✅ Done: {task_id} — Answer: {final_answer[:60]}\n"
            yield None, None, log_output

        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}"
            })
            log_output += f"⛔️ Error: {task_id} — {e}\n"
            yield None, None, log_output

    if not answers_payload:
        yield "Agent did not produce any answers to submit.", pd.DataFrame(results_log), log_output
        return

    username = profile.username if profile else "unknown"
    submit_url = f"{DEFAULT_API_URL}/submit"
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    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.')}"
        )
        results_df = pd.DataFrame(results_log)
        yield final_status, results_df, log_output
    except Exception as e:
        status_message = f"Submission Failed: {e}"
        results_df = pd.DataFrame(results_log)
        yield status_message, results_df, log_output

with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
    **Instructions:**
    1. Clone this space and define your agent logic.
    2. Log in to your Hugging Face account.
    3. Click 'Run Evaluation & Submit All Answers'.
    ---
    **Note:**
    The run may take time. Async is now used to improve responsiveness.
    """)

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
    progress_log = gr.Textbox(label="Progress Log", lines=10, interactive=False)

    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table, progress_log])

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST: https://{space_host_startup}.hf.space")
    if space_id_startup:
        print(f"✅ SPACE_ID: https://huggingface.co/spaces/{space_id_startup}")

    print("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)