app.py

# app.py (New LangChain version)
import os
import gradio as gr
import pandas as pd
from bs4 import BeautifulSoup
import datetime
import pytz
import math
import re
import requests
import traceback
import sys

# --- LangChain and new Transformers imports ---
from langchain.agents import AgentExecutor, create_react_agent
from langchain_huggingface import HuggingFacePipeline
from langchain_core.prompts import PromptTemplate
from langchain.tools import Tool
from langchain_community.tools import DuckDuckGoSearchRun

# --- Other imports ---
import transformers # Still useful for version checking
print(f"--- Using transformers version: {transformers.__version__} ---")

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


# --- Tool Definitions (LangChain Style) ---
# For LangChain, we define the functions and then wrap them in LangChain's Tool class.

def get_current_time_in_timezone_func(timezone: str) -> str:
    """A tool that fetches the current local time in a specified IANA timezone. Always use this tool for questions about the current time. Input should be a valid timezone string (e.g., 'America/New_York', 'Europe/London')."""
    print(f"--- Tool: Executing get_current_time_in_timezone for: {timezone} ---")
    try:
        tz = pytz.timezone(timezone)
        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S %Z%z")
        return f"The current local time in {timezone} is: {local_time}"
    except pytz.exceptions.UnknownTimeZoneError:
        return f"Error: Unknown timezone '{timezone}'. Please use a valid IANA timezone name."
    except Exception as e:
        return f"Error fetching time for timezone '{timezone}': {str(e)}"

# Using the DuckDuckGoSearchRun tool from LangChain for stability
# The description is very important for the agent to know when to use it.
search_tool = DuckDuckGoSearchRun(
    name="web_search",
    description="A tool that performs a web search using DuckDuckGo. Use this to find up-to-date information about events, facts, or topics when the answer isn't already known."
)

def safe_calculator_func(expression: str) -> str:
    """A tool for evaluating simple mathematical expressions. Use this tool *only* for calculations involving numbers, +, -, *, /, %, parentheses, and the math functions: sqrt, pow. Do not use it to run other code."""
    print(f"--- Tool: Executing safe_calculator with expression: {expression} ---")
    try:
        # Using a more restricted eval context for safety
        allowed_names = {"sqrt": math.sqrt, "pow": math.pow, "pi": math.pi}
        result = eval(expression, {"__builtins__": {}}, allowed_names)
        return str(result)
    except Exception as e:
        print(f"Error during calculation for '{expression}': {e}")
        return f"Error calculating '{expression}': Invalid expression or calculation error ({e})."


# --- LangChain Agent Definition ---
class LangChainAgentWrapper:
    def __init__(self):
        print("Initializing LangChainAgentWrapper...")
        
        # Using a newer, more capable instruction-tuned model.
        # This model is generally better at following the ReAct prompt format used by LangChain agents.
        model_id = "mistralai/Mistral-7B-Instruct-v0.1" 
        # model_id = "bigcode/starcoderbase-1b" # You can still use starcoder if you prefer
        
        try:
            hf_auth_token = os.getenv("HF_TOKEN")
            if not hf_auth_token:
                raise ValueError("HF_TOKEN secret is missing. It is required for downloading models.")
            else:
                print("HF_TOKEN secret found.")

            # Create the Hugging Face pipeline
            print(f"Loading model pipeline for: {model_id}")
            llm_pipeline = transformers.pipeline(
                "text-generation",
                model=model_id,
                model_kwargs={"torch_dtype": "auto"}, # Use "auto" for dtype
                device_map="auto", # Requires accelerate
                token=hf_auth_token,
            )
            print("Model pipeline loaded successfully.")
            
            # Wrap the pipeline in a LangChain LLM object
            self.llm = HuggingFacePipeline(pipeline=llm_pipeline)

            # Define the list of LangChain tools
            self.tools = [
                Tool(
                    name="get_current_time_in_timezone",
                    func=get_current_time_in_timezone_func,
                    description=get_current_time_in_timezone_func.__doc__
                ),
                search_tool, # This is already a LangChain Tool instance
                Tool(
                    name="safe_calculator",
                    func=safe_calculator_func,
                    description=safe_calculator_func.__doc__
                ),
            ]
            print(f"Tools prepared for agent: {[tool.name for tool in self.tools]}")
            
            # Create the ReAct agent prompt from a template
            # The prompt is crucial for teaching the agent how to think and use tools.
            react_prompt = PromptTemplate.from_template(
                """
                You are a helpful assistant. Answer the following questions as best you can.
                You have access to the following tools:

                {tools}

                Use the following format:

                Question: the input question you must answer
                Thought: you should always think about what to do
                Action: the action to take, should be one of [{tool_names}]
                Action Input: the input to the action
                Observation: the result of the action
                ... (this Thought/Action/Action Input/Observation can repeat N times)
                Thought: I now know the final answer
                Final Answer: the final answer to the original input question

                Begin!

                Question: {input}
                Thought:{agent_scratchpad}
                """
            )
            
            # Create the agent
            agent = create_react_agent(self.llm, self.tools, react_prompt)
            
            # Create the agent executor, which runs the agent loop
            self.agent_executor = AgentExecutor(agent=agent, tools=self.tools, verbose=True, handle_parsing_errors=True)
            print("LangChain agent created successfully.")

        except Exception as e:
            print(f"CRITICAL ERROR: Failed to initialize LangChain agent: {e}")
            traceback.print_exc() 
            raise RuntimeError(f"LangChain agent initialization failed: {e}") from e

    def __call__(self, question: str) -> str:
        print(f"\n--- LangChainAgentWrapper received question: {question[:100]}... ---")
        try:
            # Invoke the agent executor
            response = self.agent_executor.invoke({"input": question})
            # The answer is in the 'output' key of the response dictionary
            return response.get("output", "No output found.")
        except Exception as e:
            print(f"ERROR: LangChain agent execution failed: {e}")
            traceback.print_exc()
            return f"Agent Error: Failed to process the question. Details: {e}"

# --- Main Evaluation Logic ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the agent 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"

    try:
        # Now instantiate our new LangChain agent
        agent = LangChainAgentWrapper()
    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 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")
        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)

    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.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        traceback.print_exc()
        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.
        """
    )

    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)