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


from langchain.schema import HumanMessage  # Or your framework's equivalent

def init_state(question: str):
    return {
        "question": question,
        "history": [HumanMessage(content=question)],
        "context": {}  # <- Add this line
    }



# ====== 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)}"

from typing import Optional
from langchain_core.tools import BaseTool
from youtube_transcript_api import YouTubeTranscriptApi

class VideoTranscriptionTool(BaseTool):
    name: str = "transcript_video"
    description: str = "Fetch text transcript from YouTube videos using URL or ID. Optionally include timestamps."

    def _run(self, url: str, include_timestamps: Optional[bool] = False) -> str:
        # Extract video ID
        video_id = None
        if "youtube.com/watch?v=" in url:
            video_id = url.split("v=")[1].split("&")[0]
        elif "youtu.be/" in url:
            video_id = url.split("youtu.be/")[1].split("?")[0]
        elif len(url.strip()) == 11 and not ("http://" in url or "https://" in url):
            video_id = url.strip()

        if not video_id:
            return f"Invalid or unsupported YouTube URL/ID: {url}"

        try:
            transcription = YouTubeTranscriptApi.get_transcript(video_id)

            if include_timestamps:
                formatted = []
                for part in transcription:
                    timestamp = f"{int(part['start']//60)}:{int(part['start']%60):02d}"
                    formatted.append(f"[{timestamp}] {part['text']}")
                return "\n".join(formatted)
            else:
                return " ".join([part['text'] for part in transcription])

        except Exception as e:
            return f"Error fetching transcript: {str(e)}"

    def _arun(self, *args, **kwargs):
        raise NotImplementedError("Async not supported for this tool.")

        

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:
    history = state.get("history", [])

    if not history:
        return "reason"  # No history yet, reason first

    last_message = history[-1]

    # End if agent has produced a final answer
    if isinstance(last_message, AIMessage) and "FINAL ANSWER:" in last_message.content:
        return "end"

    # If an action_request exists, trigger tool use
    for msg in reversed(history):
        if isinstance(msg, dict) and msg.get("role") == "action_request":
            return "continue"

    # Otherwise, go back to reasoning
    return "reason"



def reasoning_node(state: AgentState) -> AgentState:
    import os
    import time
    from langchain_google_genai import ChatGoogleGenerativeAI
    from langchain.schema import HumanMessage, AIMessage
    from langchain.prompts import ChatPromptTemplate
    from google.api_core.exceptions import ResourceExhausted

    # Load API key
    GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
    if not GOOGLE_API_KEY:
        raise ValueError("GOOGLE_API_KEY not set in environment variables.")

    # Ensure history is well-formed
    if "history" not in state or not isinstance(state["history"], list):
        state["history"] = []
    if not state["history"] or not isinstance(state["history"][-1], HumanMessage):
        state["history"].append(HumanMessage(content="Continue."))

    # Ensure context and reasoning fields
    state.setdefault("context", {})
    state.setdefault("reasoning", "")
    state.setdefault("iterations", 0)

    # Create Gemini model wrapper
    llm = ChatGoogleGenerativeAI(
        model="gemini-1.5-flash",
        temperature=0.1,
        google_api_key=GOOGLE_API_KEY
    )

    # Create prompt chain
    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"
            "Important: You must select a tool for questions involving video, audio, or code.\n\n"
            "Current Context:\n{context}\n\n"
            "Reasoning Steps:\n{reasoning}\n\n"
            "Response Format:\n"
            "Reasoning: [Your analysis]\n"
            "Action: [Tool name OR 'Final Answer']\n"
            "Action Input: [Input for tool OR final response]"
        )),
        *state["history"]
    ])

    chain = prompt | llm

    # === Add Retry Logic ===
    def call_with_retry(inputs, retries=3, delay=60):
        for attempt in range(retries):
            try:
                return chain.invoke(inputs)
            except ResourceExhausted as e:
                print(f"[Retry {attempt+1}] Gemini rate limit hit. Waiting {delay}s...")
                time.sleep(delay)
        raise RuntimeError("Failed after multiple retries due to Gemini quota limit.")

    # Call model with retry protection
    response = call_with_retry({
        "context": state["context"],
        "reasoning": state["reasoning"],
        "question": state["question"]
    })

    # Parse output
    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}"
    state["iterations"] += 1

    if "final answer" in action.lower():
        state["history"].append(AIMessage(content=f"FINAL ANSWER: {action_input}"))
    else:
        state["context"]["current_tool"] = {
            "tool": action,
            "input": action_input
        }

    return state




    

def tool_node(state: AgentState) -> AgentState:
    from langchain.schema import AIMessage

    # Ensure history exists
    if "history" not in state or not isinstance(state["history"], list):
        raise ValueError("Invalid or missing history in state")

    # Find the most recent action request in history
    tool_call = None
    for msg in reversed(state["history"]):
        if isinstance(msg, dict) and msg.get("role") == "action_request":
            tool_call = msg
            break

    if not tool_call:
        raise ValueError("No tool call found in history")

    tool_name = tool_call.get("tool")
    tool_input = tool_call.get("input")

    # Defensive check for missing tool or input
    if not tool_name or tool_input is None:
        raise ValueError("Tool name or input missing from action request")

    # Look up and invoke the tool
    agent = BasicAgent()  # Create agent to access tools
    tool_fn = next((t for t in agent.tools if t.__name__ == tool_name), None)

    if tool_fn is None:
        raise ValueError(f"Tool '{tool_name}' not found")

    try:
        tool_output = tool_fn(tool_input)
    except Exception as e:
        tool_output = f"[Tool Error] {str(e)}"

    # Add output to history as an AIMessage
    state["history"].append(AIMessage(content=f"[{tool_name} output]\n{tool_output}"))

    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,
            VideoTranscriptionTool() 
        ]

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:50]}{'...' if len(question) > 50 else ''}")

        # Initialize state with proper structure
        state = {
            "question": question,
            "context": {},  # Ensure it's a dict
            "reasoning": "",
            "iterations": 0,
            "history": [HumanMessage(content=question)]
        }

        final_state = self.workflow.invoke(state)

        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']):
                if isinstance(msg, dict):
                    print(f"Message {i}: dict - {msg}")
                else:
                    print(f"Message {i}: {type(msg).__name__} - {msg.content[:100]}...")

        # Extract the FINAL ANSWER from history
        for msg in reversed(final_state["history"]):
            if isinstance(msg, AIMessage) and "FINAL ANSWER:" in msg.content:
                answer = msg.content.split("FINAL ANSWER:")[1].strip()
                print(f"Agent returning answer: {answer}")
                return answer

        raise ValueError("No FINAL ANSWER found in agent history.")

    


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)