app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from smolagents import tool, Tool, CodeAgent, DuckDuckGoSearchTool, HfApiModel, VisitWebpageTool, SpeechToTextTool, FinalAnswerTool
from dotenv import load_dotenv
import heapq
from collections import Counter
import re
from io import BytesIO
from youtube_transcript_api import YouTubeTranscriptApi
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.document_loaders import ArxivLoader

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

#Load environment variables
load_dotenv()

from langgraph.graph import END, StateGraph
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from langchain_core.tools import tool
from typing import Dict, List, TypedDict, Annotated
import operator
from langchain_community.llms import HuggingFaceHub
from langchain_community.chat_models import ChatHuggingFace


# ====== Tool Definitions ======
@tool
def duckduckgo_search(query: str) -> str:
    """Search web using DuckDuckGo. Returns top 3 results."""
    from duckduckgo_search import DDGS
    with DDGS() as ddgs:
        return "\n\n".join(
            f"Title: {res['title']}\nURL: {res['href']}\nSnippet: {res['body']}"
            for res in ddgs.text(query, max_results=3)
        )

@tool
def wikipedia_search(query: str) -> str:
    """Get Wikipedia summaries. Returns first 3 sentences."""
    import wikipedia
    try:
        return wikipedia.summary(query, sentences=3)
    except wikipedia.DisambiguationError as e:
        return f"Disambiguation options: {', '.join(e.options[:3])}"
    except wikipedia.PageError:
        return "Page not found"

@tool
def arxiv_search(query: str) -> str:
    """Search academic papers on arXiv. Returns top 3 results."""
    import arxiv
    results = arxiv.Search(
        query=query, 
        max_results=3,
        sort_by=arxiv.SortCriterion.Relevance
    ).results()
    
    return "\n\n".join(
        f"Title: {r.title}\nAuthors: {', '.join(a.name for a in r.authors)}\n"
        f"Published: {r.published.strftime('%Y-%m-%d')}\nSummary: {r.summary[:250]}..."
        for r in results
    )

@tool
def document_qa(input_str: str) -> str:
    """Answer questions from documents. Input format: 'document_text||question'"""
    from transformers import pipeline
    if '||' not in input_str:
        return "Invalid format. Use: 'document_text||question'"
    
    context, question = input_str.split('||', 1)
    qa_model = pipeline('question-answering', model='deepset/roberta-base-squad2')
    return qa_model(question=question, context=context)['answer']

@tool
def python_execution(code: str) -> str:
    """Execute Python code and return output."""
    try:
        # Create isolated environment
        env = {}
        exec(f"def __exec_fn__():\n    {indent_code(code)}\nresult = __exec_fn__()", env)
        return str(env.get('result', 'No output'))
    except Exception as e:
        return f"Error: {str(e)}"

def indent_code(code: str) -> str:
    return '\n    '.join(code.splitlines())

# ====== Agent State ======
class AgentState(TypedDict):
    question: str
    history: Annotated[List[Dict], operator.add]
    context: str
    reasoning: str
    iterations: int

# ====== Graph Components ======
def init_state(question: str) -> AgentState:
    return {
        "question": question,
        "history": [],
        "context": f"User question: {question}",
        "reasoning": "",
        "iterations": 0
    }

def should_continue(state: AgentState) -> str:
    """Determine if agent should continue or finish"""
    last_msg = state['history'][-1]
    
    # Stop conditions
    if state['iterations'] >= 5:
        return "end"
    if "FINAL ANSWER:" in last_msg.get('content', ''):
        return "end"
    if last_msg['role'] == 'tool':
        return "reason"
    return "continue"

def reasoning_node(state: AgentState) -> AgentState:
    """Agent reasoning and tool selection"""
    # Get Hugging Face API token from environment
    token = os.environ.get("HF_TOKEN")
    if not token:
        raise ValueError("Hugging Face API token not found in environment variables")
    
    # Create the underlying LLM model
    llm = HuggingFaceHub(
        repo_id="HuggingFaceH4/zephyr-7b-beta",
        huggingfacehub_api_token=token,
        model_kwargs={
            "temperature": 0.1,
            "max_new_tokens": 500
        }
    )
    
    # Wrap the LLM in ChatHuggingFace
    chat_model = ChatHuggingFace(llm=llm)
    
    # Build prompt
    prompt = ChatPromptTemplate.from_messages([
        ("system", (
            "You're an expert problem solver. Analyze the question, select the best tool, "
            "and provide reasoning. Available tools: duckduckgo_search, wikipedia_search, "
            "arxiv_search, document_qa, python_execution.\n\n"
            "Current Context:\n{context}\n\n"
            "Reasoning Steps:\n{reasoning}\n\n"
            "Response Format:\nReasoning: [Your analysis]\nAction: [Tool name OR 'Final Answer']\n"
            "Action Input: [Input for tool OR final response]"
        )),
        *state['history']
    ])
    
    chain = prompt | chat_model
    response = chain.invoke({
        "context": state['context'],
        "reasoning": state['reasoning'],
        "question": state['question']
    })
    
    # Parse response
    content = response.content
    reasoning, action, action_input = parse_agent_response(content)
    
    # Update state
    state['history'].append(AIMessage(content=content))
    state['reasoning'] += f"\nStep {state['iterations']+1}: {reasoning}"
    
    if "final answer" in action.lower():
        state['history'].append(AIMessage(content=f"FINAL ANSWER: {action_input}"))
    else:
        state['history'].append({
            "tool": action,
            "input": action_input,
            "role": "action_request"
        })
    
    return state
    

def tool_node(state: AgentState) -> AgentState:
    """Execute selected tool and update state"""
    last_action = state['history'][-1]
    tool_name = last_action['tool']
    tool_input = last_action['input']
    
    # Tool mapping
    tools = {
        "duckduckgo_search": duckduckgo_search,
        "wikipedia_search": wikipedia_search,
        "arxiv_search": arxiv_search,
        "document_qa": document_qa,
        "python_execution": python_execution
    }
    
    # Execute tool
    tool_result = tools[tool_name].invoke(tool_input)
    
    # Update state
    state['history'].append(ToolMessage(
        content=tool_result,
        tool_call_id=tool_name
    ))
    state['context'] = f"Tool Result ({tool_name}): {tool_result}"
    state['iterations'] += 1
    
    return state

def parse_agent_response(response: str) -> tuple:
    """Extract reasoning, action, and input from response"""
    reasoning = response.split("Reasoning:")[1].split("Action:")[0].strip()
    action_part = response.split("Action:")[1].strip()
    
    if "Action Input:" in action_part:
        action, action_input = action_part.split("Action Input:", 1)
        action = action.strip()
        action_input = action_input.strip()
    else:
        action = action_part
        action_input = ""
    
    return reasoning, action, action_input

# ====== Agent Graph ======
def create_agent_workflow():
    workflow = StateGraph(AgentState)
    
    # Define nodes
    workflow.add_node("reason", reasoning_node)
    workflow.add_node("action", tool_node)
    
    # Set entry point
    workflow.set_entry_point("reason")
    
    # Define edges
    workflow.add_conditional_edges(
        "reason",
        should_continue,
        {
            "continue": "action",
            "reason": "reason",
            "end": END
        }
    )
    
    workflow.add_edge("action", "reason")
    return workflow.compile()

# ====== Agent Interface ======
class BasicAgent:
    def __init__(self):
        self.workflow = create_agent_workflow()
        self.tools = [
            duckduckgo_search,
            wikipedia_search,
            arxiv_search,
            document_qa,
            python_execution
        ]
    


    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:50]}{'...' if len(question) > 50 else ''}")
        
        # Initialize state with the question
        state = init_state(question)
        
        # Execute the workflow
        final_state = self.workflow.invoke(state)
        
        # Debug: Print the final state structure
        print(f"Final state keys: {list(final_state.keys())}")
        if 'history' in final_state:
            print(f"History length: {len(final_state['history'])}")
            for i, msg in enumerate(final_state['history']):
                print(f"Message {i}: {type(msg).__name__} - {msg.content[:100]}...")
        
        # Extract final answer from history
        for msg in reversed(final_state['history']):
            if isinstance(msg, AIMessage) and "FINAL ANSWER:" in msg.content:
                # Extract and clean the final answer
                answer = msg.content.split("FINAL ANSWER:")[1].strip()
                print(f"Agent returning answer: {answer}")
                return answer
    


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

    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()
    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

    # 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)