app.py

import os, sys
from enum import Enum
import gradio as gr
import requests
import inspect
import subprocess
import dateparser
from bs4 import BeautifulSoup
import regex
import pandas as pd
import torch
from functools import lru_cache
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from smolagents import PromptTemplates, CodeAgent, WebSearchTool, WikipediaSearchTool, VisitWebpageTool, PythonInterpreterTool
import smolagents.tools as _tools
from smolagents.models import ChatMessage
# from huggingface_hub import InferenceClient, hf_hub_download

subprocess.run(["playwright", "install"], check=True)

print(dir(_tools))

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

# class LocalLLM:
#     def __init__(self, pipe):
#         self.pipe = pipe

#     def generate(self, prompt, **kwargs):
#         unsupported_keys = ["stop_sequences"] # Remove keys not accepted by HF pipelines
#         cleaned_kwargs = {k: v for k, v in kwargs.items() if k not in unsupported_keys}
#         # print(f"🧪 kwargs cleaned: {cleaned_kwargs.keys()}")
#         try:
#             outputs = self.pipe(prompt, **cleaned_kwargs)
#             # print(f"🧪 Raw output from pipe: {outputs}")
#             if isinstance(outputs, list) and isinstance(outputs[0], dict):
#                 out = outputs[0]["generated_text"]
#             elif isinstance(outputs, list):
#                 out = outputs[0]  # fallback if it's just a list of strings
#             else:
#                 out = str(outputs)
#             print("🧪 Final object to return:", type(out), out[:100])
#             return {'role': 'assistant', 'content': [{'type':'text', 'text': out}]}
#         except Exception as e:
#             print(f"❌ Error in LocalLLM.generate(): {e}")
#             raise

def check_token_access():
    token = os.environ.get("HF_TOKEN", "")
    if not token:
        print("❌ No token found")
        return
    headers = {"Authorization": f"Bearer {token}"}
    url = "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/resolve/main/config.json"
    try:
        r = requests.get(url, headers=headers, timeout=10)
        print(f"🔍 Token test response: {r.status_code}")
        if r.status_code == 200:
            print("✅ Token access confirmed for gated model.")
        elif r.status_code == 403:
            print("❌ 403 Forbidden: Token does not have access.")
        else:
            print("⚠️ Unexpected status:", r.status_code)
    except Exception as e:
        print("❌ Token check failed:", e)

class CachedWebSearchTool(WebSearchTool):
    @lru_cache(maxsize=128)
    def run(self, query: str):
        # identical queries return instantly
        return super().run(query)

class CachedWikiTool(WikipediaSearchTool):
    @lru_cache(maxsize=128)
    def run(self, page: str):
        return super().run(page)

class PreloadedPythonTool(PythonInterpreterTool):
    """
    A PythonInterpreterTool that automatically prepends the necessary imports
    (bs4, BeautifulSoup, regex) so you never hit NameError inside your code blocks.
    """
    def run(self, code: str) -> str:
        preamble = (
            "import bs4\n"
            "from bs4 import BeautifulSoup\n"
            "import regex\n"
        )
        return super().run(preamble + code)

# --- Basic Agent Definition ---
# ----- THIS IS WHERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self, model_id="meta-llama/Llama-3.1-8B-Instruct", hf_token=""):
        print("BasicAgent initialized.")
        print("ENV-HF_TOKEN-LEN", len(hf_token), file=sys.stderr)
        check_token_access()

        # Local test
        # client = InferenceClient(
        # model="meta-llama/Llama-3.1-8B-Instruct",
        # token=os.environ["HF_TOKEN"]
        # )
        # print(client.text_generation("Hello, my name is", max_new_tokens=20))
        
        # Initialize the model
        # model = HfApiModel(model_id="meta-llama/Llama-3.1-8B-Instruct",
        #                   # format="text-generation",
        #                   token=os.environ["HF_TOKEN"], 
        #                   max_tokens=2048,
        #                   temperature=0.0
        #                  )
        
        # Initialize the tools other than the base tools
        # See list of base tools in https://github.com/huggingface/smolagents/blob/main/src/smolagents/default_tools.py
        
        # Download the model weights and build the pipeline
        tok = AutoTokenizer.from_pretrained(model_id, token=hf_token)
        mod = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
            device_map="auto",  # auto-distributes to GPU
            token=hf_token
        )
        self.pipe = pipeline(
            "text-generation",
            model=mod,
            tokenizer=tok,
            max_new_tokens=512,
            return_full_text=False,      # <— only get the completion, not the prompt + completion
            # temperature=1.0,
        )
        # Introduce tools
        wiki_tool = CachedWikiTool()
        search_tool = CachedWebSearchTool()
        python_tool = PreloadedPythonTool()
        html_parse_tool = VisitWebpageTool()
        # System prompt
        # 1) Get the default templates
        default_templates = PromptTemplates()
        print(">>> prompt_templates is a", type(default_templates))
        # 2) Construct a new one, swapping in only your system prompt
        my_templates = PromptTemplates(
            system_prompt=(
                "When writing Python code in the PythonInterpreterTool," 
                "you must always import bs4 and regex (already preloaded)," 
                "and *return* your result by calling final_answer(...). Do not assign to a variable named final_answer."
            ),
            planning=default_templates.planning,
            managed_agent=default_templates.managed_agent,
            final_answer=default_templates.final_answer,
        )
        # Initialize the agent
        self.agent = CodeAgent(model=self, 
                               tools=[wiki_tool, search_tool, python_tool, html_parse_tool], 
                               add_base_tools=True,
                               prompt_templates=my_templates, 
                               additional_authorized_imports=["dateparser", "bs4", "regex"]
         )
        
    def _serialize_messages(self, messages):
        prompt = []
        for m in messages:
            r = m["role"]
            role = r.value if isinstance(r, Enum) and hasattr(r, "value") else r # "system" / "user" / "assistant"
            text = "".join([c['text'] for c in m['content']])
            prompt.append(f"{role}: {text}")
        return "\n".join(prompt)

    def generate(self, question: str, stop_sequences=None, **kwargs) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        # 1. Build the HF kwargs
        allowed = {"max_new_tokens", "temperature", "top_k", "top_p"}
        gen_kwargs = {k: v for k, v in kwargs.items() if k in allowed}
        
        # 2. Serialize the message and get the response
        prompt_str = (
            self._serialize_messages(question)
            if isinstance(question, list)
            else question
        )
        outputs = self.pipe(prompt_str, **gen_kwargs)
        response = outputs[0]["generated_text"]
        # response = self.agent.run(question)

        # 3. Optionally map SmolAgents’ stop_sequences → HF pipeline’s 'stop'
        if stop_sequences:
            # find the earliest occurrence of any stop token
            cuts = [response.find(s) for s in stop_sequences if response.find(s) != -1]
            if cuts:
                response = response[: min(cuts)]
        
        print(f"Agent returning its generated answer: {response}")
        
        # wrap back into a chat message dict
        return ChatMessage(role="assistant", content=response)
        # return {
        #     "role": 'assistant',
        #     "content": [{"type": "text", "text": response}],
        # }

    __call__ = generate

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
    hf_token = os.getenv("HF_TOKEN")

    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(hf_token=hf_token).agent
    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()
        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

    questions_data = questions_data[:5]
    
    # 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)