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 smolagents import Tool
from langchain_community.document_loaders import WikipediaLoader

class WikiSearchTool(Tool):
    name = "wiki_search"
    description = "Search Wikipedia for a query and return up to 2 results."
    inputs = {
        "query": {"type": "string", "description": "The search term for Wikipedia."}
    }
    output_type = "string"

    def forward(self, 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", "Wikipedia")}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
                for doc in search_docs
            ]
        )
        return formatted_search_docs

    


class StringReverseTool(Tool):
    name = "reverse_message"
    description = "When you received a strange text, try to reverse it and perform action described in reversed message."
    inputs = {
        "message": {
            "type": "string",
            "description": "A message, which looks like strange and can be reversed to get actions to execute."
        }
    }
    output_type = "string"

    def __init__(self):
        return

    def forward(self, message: str):
        return message[::-1]

class KeywordsExtractorTool(Tool):
    """Extracts top 5 keywords from a given text based on frequency."""

    name = "keywords_extractor"
    description = "This tool returns the 5 most frequent keywords occur in provided block of text."

    inputs = {
        "text": {
            "type": "string",
            "description": "Text to analyze for keywords.",
        }
    }
    output_type = "string"

    def forward(self, text: str) -> str:
        try:
            all_words = re.findall(r'\b\w+\b', text.lower())
            conjunctions = {'a', 'and', 'of', 'is', 'in', 'to', 'the'}
            filtered_words = []
            for w in all_words:
                if w not in conjunctions:
                    filtered_words.push(w)
            word_counts = Counter(filtered_words)
            k = 5
            return heapq.nlargest(k, word_counts.items(), key=lambda x: x[1])
        except Exception as e:
            return f"Error during extracting most common words: {e}"


@tool
def calculator(inputs: Union[str, dict]):
    """
    Perform mathematical operations based on the operation provided.
    Supports both binary (a, b) operations and list operations.
    """

    # If input is a JSON string, parse it
    if isinstance(inputs, str):
        try:
            import json
            inputs = json.loads(inputs)
        except Exception as e:
            return f"Invalid input format: {e}"

    # Handle list-based operations like SUM
    if "list" in inputs:
        nums = inputs.get("list", [])
        op = inputs.get("operation", "").lower()

        if not isinstance(nums, list) or not all(isinstance(n, (int, float)) for n in nums):
            return "Invalid list input. Must be a list of numbers."

        if op == "sum":
            return sum(nums)
        elif op == "multiply":
            return reduce(operator.mul, nums, 1)
        else:
            return f"Unsupported list operation: {op}"

    # Handle basic two-number operations
    a = inputs.get("a")
    b = inputs.get("b")
    operation = inputs.get("operation", "").lower()

    if a is None or b is None or not isinstance(a, (int, float)) or not isinstance(b, (int, float)):
        return "Both 'a' and 'b' must be numbers."

    if operation == "add":
        return a + b
    elif operation == "subtract":
        return a - b
    elif operation == "multiply":
        return a * b
    elif operation == "divide":
        if b == 0:
            return "Error: Division by zero"
        return a / b
    elif operation == "modulus":
        return a % b
    else:
        return f"Unknown operation: {operation}"


@tool
def parse_excel_to_json(task_id: str) -> dict:
    """
    For a given task_id fetch and parse an Excel file and save parsed data in structured JSON file.
    Args:
        task_id: An task ID to fetch.
        
    Returns:
        {
            "task_id": str,
            "sheets": {
                "SheetName1": [ {col1: val1, col2: val2, ...}, ... ],
                ...
            },
            "status": "Success" | "Error"
        }
    """
    url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"

    try:
        response = requests.get(url, timeout=100)
        if response.status_code != 200:
            return {"task_id": task_id, "sheets": {}, "status": f"{response.status_code} - Failed"}

        xls_content = pd.ExcelFile(BytesIO(response.content))
        json_sheets = {}

        for sheet in xls_content.sheet_names:
            df = xls_content.parse(sheet)
            df = df.dropna(how="all")  
            rows = df.head(20).to_dict(orient="records")
            json_sheets[sheet] = rows

        return {
            "task_id": task_id,
            "sheets": json_sheets,
            "status": "Success"
        }

    except Exception as e:
        return {
            "task_id": task_id,
            "sheets": {},
            "status": f"Error in parsing Excel file: {str(e)}"
        }



class VideoTranscriptionTool(Tool):
    """Fetch transcripts from YouTube videos"""
    name = "transcript_video"
    description = "Fetch text transcript from YouTube movies with optional timestamps"
    inputs = {
        "url": {"type": "string", "description": "YouTube video URL or ID"},
        "include_timestamps": {"type": "boolean", "description": "If timestamps should be included in output", "nullable": True}
    }
    output_type = "string"

    def forward(self, url: str, include_timestamps: bool = False) -> str:

        if "youtube.com/watch" 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:  # Direct ID
            video_id = url.strip()
        else:
            return f"YouTube URL or ID: {url} is invalid!"

        try:
            transcription = YouTubeTranscriptApi.get_transcript(video_id)

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

        except Exception as e:
            return f"Error in extracting YouTube transcript: {str(e)}"

class BasicAgent:
    def __init__(self):
        token = os.environ.get("HF_API_TOKEN")
        model = HfApiModel(
            temperature=0.1,
            token=token
        )

        search_tool = DuckDuckGoSearchTool()
        wiki_search_tool = WikiSearchTool()
        str_reverse_tool = StringReverseTool()
        keywords_extract_tool = KeywordsExtractorTool()
        speech_to_text_tool = SpeechToTextTool()
        visit_webpage_tool = VisitWebpageTool()
        final_answer_tool = FinalAnswerTool()
        video_transcription_tool = VideoTranscriptionTool()

        system_prompt = f"""
You are an advanced, helpful, and highly analytical research assistant. Your goal is to provide accurate, comprehensive, and well-structured answers to user queries, leveraging all available tools efficiently.

**Follow this robust process:**

1.  **Understand the User's Need:** Carefully analyze the user's question, including any attached files or specific requests (e.g., "summarize," "analyze data," "find facts").
2.  **Formulate a Detailed Plan:** Before acting, create a clear, step-by-step plan. This plan should outline:
    * What information needs to be gathered.
    * Which tools are most appropriate for each step (e.g., `duckduckgo_search` for general web search, `wiki_search` for encyclopedic facts, `transcript_video` for YouTube, `file_analysis` or `data_analysis` for local files).
    * How you will combine information from different sources.
    * How you will verify or synthesize the findings.
3.  **Execute the Plan Using Tools:** Call the necessary tools, providing clear and correct arguments. If a tool fails, try to understand why and adapt your plan (e.g., try a different search query or tool).
4.  **Synthesize and Verify Information:** Once you have gathered sufficient information, synthesize it into a coherent answer. Do not just list facts; explain their significance and how they relate to the original question. If there are contradictions or uncertainties, mention them.
5.  **Formulate the Final Answer:**
    * Present your answer clearly and concisely.
    * Always begin your ultimate response with "FINAL ANSWER:".
    * If the answer is a single number, provide only the number.
    * If the answer is a list, provide comma-separated values.
    * For complex answers, use structured formats like bullet points or JSON where appropriate to enhance readability.
    * **Crucially, always include sources or references (e.g., URLs, Wikipedia titles, file names) where you obtained the information.** This builds trust and allows for verification.
    * If you used `file_analysis` or `data_analysis` tools on an uploaded file, explicitly state that you analyzed the provided file.

**Important Considerations:**
* **Prioritize:** If the query involves a specific file, start by analyzing that file if appropriate.
* **Ambiguity:** If the question is ambiguous, ask for clarification.
* **Limitations:** If you cannot answer a question with the available tools, state that clearly.
* **Conciseness:** Be as concise as possible while providing a complete and accurate answer.
"""
        self.agent = CodeAgent(
            model=model,
            tools=[search_tool, wiki_search_tool, str_reverse_tool, keywords_extract_tool, speech_to_text_tool, visit_webpage_tool, \
                   final_answer_tool, parse_excel_to_json, video_transcription_tool],
            add_base_tools=True,
            max_steps=15 
        )
        self.agent.prompt_templates["system_prompt"] = self.agent.prompt_templates["system_prompt"] + system_prompt

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        answer = self.agent.run(question)
        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)