app.py

import requests
import os
import gradio as gr
import pandas as pd
import time
import re
import json
import wikipedia
import speech_recognition as sr
from pydub import AudioSegment
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain.memory import ConversationSummaryMemory
from langchain.tools import Tool
from langchain.tools.python.tool import PythonREPLTool
from langchain_community.document_loaders import WikipediaLoader
from langchain.prompts import PromptTemplate

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

# === TOOL: python_repl ===
repl_tool = PythonREPLTool(
    name="python_repl",
    description="A Python REPL for calculations and parsing. Input must be valid Python code, use print() to output results."
)

# === TOOL: file_saver ===
def download_and_save_file(args: dict) -> str:
    try:
        if isinstance(args, str):
            args = json.loads(args)
        url = args.get("url")
        local_filename = args.get("local_filename")
        if not url or not local_filename:
            return "Error: Both 'url' and 'local_filename' must be provided."
        response = requests.get(url, stream=True, timeout=30)
        response.raise_for_status()
        os.makedirs(os.path.dirname(local_filename) or '.', exist_ok=True)
        with open(local_filename, 'wb') as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
        return f"File downloaded successfully to {local_filename}"
    except Exception as e:
        return f"Error downloading file: {e}"

file_saver_tool = Tool(
    name="file_saver",
    description="Downloads a file from a URL and saves it as the given local filename. Input: JSON with 'url' and 'local_filename'.",
    func=download_and_save_file,
)

# === TOOL: audio_transcriber_tool ===
def transcribe_audio_from_path(local_audio_path: str, language: str = "en-US") -> str:
    r = sr.Recognizer()
    temp_wav_path = "temp_audio_to_transcribe.wav"
    transcribed_text = ""
    try:
        if local_audio_path.startswith("http://") or local_audio_path.startswith("https://"):
            return "Error: Only local file paths allowed. Use 'file_saver' first."
        if not os.path.exists(local_audio_path):
            return f"Error: File not found: '{local_audio_path}'."
        audio = AudioSegment.from_file(local_audio_path)
        audio.export(temp_wav_path, format="wav")
        with sr.AudioFile(temp_wav_path) as source:
            audio_listened = r.record(source)
            try:
                transcribed_text = r.recognize_google(audio_listened, language=language)
            except sr.UnknownValueError:
                return "Could not understand audio."
            except sr.RequestError as e:
                return f"Could not request results from Google Speech Recognition; {e}"
    except Exception as e:
        return f"Error: {e}"
    finally:
        if os.path.exists(temp_wav_path):
            os.remove(temp_wav_path)
    return transcribed_text.strip()

audio_transcriber_tool = Tool(
    name="audio_transcriber_tool",
    description="Transcribes audio from a local file path to text. Input: path to audio file (e.g., 'myfile.mp3'). Use 'file_saver' to download first. Optionally set language.",
    func=transcribe_audio_from_path,
)

# === TOOL: wikipedia_search_tool2 ===
def wiki_search(query: str) -> str:
    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata.get("source", "")}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            for doc in search_docs
        ])
    return formatted_search_docs

wikipedia_search_tool2 = Tool(
    name="wikipedia_search_tool2",
    description="Search Wikipedia for a query and return up to 2 results. Input: query string.",
    func=wiki_search,
)

# === PROMPT ===
prompt = PromptTemplate(
    input_variables=["input", "agent_scratchpad", "chat_history", "tool_names"],
    template="""
You are a smart and helpful AI Agent/Assistant that excels at fact-based reasoning. You are allowed and encouraged to use one or more tools as needed to answer complex questions and perform tasks.
STRICT FINAL ANSWER RULES:
- Final Answer must be a number, a few words, or a comma-separated list, as requested.
- No units or extra punctuation unless asked.
Your response must start with 'Thought:' and finish with 'Final Answer:'.
You have access to the following tools:
{tools}
Use this format:
Thought: [thinking]
Action: [tool_name]
Action Input: [input]
Observation: [result]
...
Thought: [done]
Final Answer: [concise answer]

{chat_history}
New input: {input}
---
{agent_scratchpad}
"""
)

# === AGENT ===
class BasicAgent:
    def __init__(
        self,
        agent,
        tools,
        verbose=False,
        handle_parsing_errors=True,
        max_iterations=9,
        memory=None
    ):
        self.agent_obj = AgentExecutor(
            agent=agent,
            tools=tools,
            verbose=verbose,
            handle_parsing_errors=handle_parsing_errors,
            max_iterations=max_iterations,
            memory=memory
        )

    def __call__(self, question: str) -> str:
        result = self.agent_obj.invoke(
            {"input": question},
            config={"configurable": {"session_id": "test-session"}},
        )
        return result['output']

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

    openai_api_key = os.getenv("OPENAI_API_KEY")
    if not openai_api_key:
        print("OpenAI API key not found in environment variables.")
        return "OpenAI API key not found. Please set OPENAI_API_KEY environment variable.", None
    print(f"Using OpenAI API key: {openai_api_key[:4]}... (truncated for security)")

    llm_client = ChatOpenAI(model='gpt-4o', temperature=0, api_key=openai_api_key)

    summary_memory = ConversationSummaryMemory(llm=llm_client, memory_key="chat_history")

    summary_react_agent = create_react_agent(
        llm=llm_client,
        tools=[repl_tool, file_saver_tool, audio_transcriber_tool, wikipedia_search_tool2],
        prompt=prompt
    )

    try:
        agent = BasicAgent(summary_react_agent, [repl_tool, file_saver_tool, audio_transcriber_tool, wikipedia_search_tool2], True, True, 30, summary_memory)
    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)

    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

    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")
        file_name = item.get("file_name")
        full_question_for_agent = question_text
        if file_name:
            attachment_url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
            full_question_for_agent += f"\n\nAttachment '{file_name}' available at EXACT URL: {attachment_url}"
            print(f"Running agent on task {task_id}: {full_question_for_agent}",flush=True)
        try:
            submitted_answer = agent(full_question_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})
            time.sleep(1)
        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)

    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)

    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.")
        cleaned_final_status = re.sub(r'[^\x20-\x7E\n\r\t]+', '', final_status)
        cleaned_final_status = cleaned_final_status.strip()
        results_df = pd.DataFrame(results_log)
        return cleaned_final_status, results_df
    except Exception as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Clone this space and modify the code as needed.
        2. Log in to your Hugging Face account below.
        3. Click 'Run Evaluation & Submit All Answers' to see your score!
        """
    )
    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)
    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

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 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(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)