import os
import gradio as gr
import requests
import inspect
import pandas as pd
import re
import time
from datetime import datetime, timedelta
from collections import deque
import random
from smolagents import CodeAgent, load_tool, tool
from smolagents.models import Model, ChatMessage, MessageRole, Tool
from tools import FinalAnswerTool, WikipediaSearchTool, VisitWebpageTool, DuckDuckGoSearchTool
import google.generativeai as genai

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MAX_RETRIES = 3
INITIAL_RETRY_DELAY = 1  # seconds
MAX_RETRY_DELAY = 32  # seconds

class RateLimiter:
    def __init__(self, requests_per_minute):
        self.requests_per_minute = requests_per_minute
        self.window_size = 60  # 60 seconds = 1 minute
        self.requests = deque()

    def wait_if_needed(self):
        now = datetime.now()
        
        # Remove requests older than our window
        while self.requests and (now - self.requests[0]).total_seconds() > self.window_size:
            self.requests.popleft()
        
        # If we've hit our limit, wait until the oldest request expires
        if len(self.requests) >= self.requests_per_minute:
            wait_time = self.window_size - (now - self.requests[0]).total_seconds()
            if wait_time > 0:
                time.sleep(wait_time + 0.1)  # Add a small buffer
        
        # Add the current request
        self.requests.append(now)

# --- Basic Agent Definition ---

final_answer = FinalAnswerTool()

class GeminiModel(Model):
    def __init__(self, api_key, **kwargs):
        super().__init__(**kwargs)
        self.api_key = api_key
        genai.configure(api_key=api_key)
        self.model = genai.GenerativeModel('models/gemini-2.0-flash-lite')
        self.rate_limiter = RateLimiter(requests_per_minute=25)

        self.system_prompt = """You are a high-performance reasoning agent focused on ACCURACY. Your goal is to answer questions by breaking them down into logical steps and VERIFYING your information from multiple sources.

**CRITICAL ACCURACY RULES:**
- ALWAYS verify information from multiple sources when possible
- Distinguish between different types of releases (studio albums vs live albums vs compilations)
- Be precise about dates, numbers, and classifications
- If information seems unclear or contradictory, search for additional sources
- When counting items, be explicit about what you're counting and why
- **UNDERSTAND CONTEXT AND SYNONYMS**: When searching for specific terms, also look for related terms, synonyms, and contextual descriptions
- **READ FOR CONTEXT**: Don't just search for exact phrases - read the content to understand who or what is being described
- **CONSIDER ALTERNATIVE TERMINOLOGY**: "Horse doctor" = "equine veterinarian", "physician" = "doctor", etc.
- **BREAK DOWN COMPLEX QUESTIONS**: For questions with multiple steps, identify each step clearly:
  1. What is the first thing I need to find?
  2. What is the second thing I need to find using the first result?
  3. What is the final answer I need to provide?
- **FOLLOW THE LOGICAL CHAIN**: Don't jump to conclusions - follow each step in order
- Don't take the final answera from web results preview, always read the full content of the page

**YOUR TOOLS**
- `web_search(query: str)`: Finds URLs and information.
- `visit_webpage(url: str)`: Reads the content of a URL.
- `wikipedia_search(query: str)`: Searches Wikipedia.
- `final_answer(answer: str)`: Submits your final answer.

**YOUR WORKFLOW**
1.  **Think (`Thought:`):** Analyze the question and create a plan.
2.  **Act (`Code:`):** Execute ONE step of your plan.
3.  **Observe (`Observation:`):** Use the result to inform your next step.
4.  **Verify:** Cross-check information when possible.
5.  **Submit** your answer using `final_answer()`.

---

**EXAMPLE 2: Finding a specific count with verification**
*Question:* How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)?

**Your Turn 1:**
Thought: I need to find a reliable discography for Mercedes Sosa. I'll search for multiple sources to verify the information.
Code:
```py
web_search(query="Mercedes Sosa studio albums discography 2000-2009")
```<end_code>
*Observation:* Search results show various sources including Wikipedia, AllMusic, and other music databases.

**Your Turn 2:**
Thought: I need to check multiple sources to ensure accuracy. Let me visit Wikipedia.
Code:
```py
visit_webpage(url="https://en.wikipedia.org/wiki/Mercedes_Sosa")
```<end_code>
*Observation:* The Wikipedia page shows studio albums with release dates. I can see: Corazón Libre (2005), Cantora 1 (2009), Cantora 2 (2009).

**Your Turn 3:**
Thought: I found 3 studio albums in the specified period. Let me verify this with another source to be sure.
Code:
```py
visit_webpage(url="https://www.allmusic.com/artist/mercedes-sosa-mn0000869068#discography")
```<end_code>
*Observation:* AllMusic shows more albums in the discography section between 2000 and 2009: Acustico (2002), Maestros del Folklore (2003), Argentina Quiere Cantar (2003), Para Cantar He Nacido (2005), Corazón Libre (2005), Acustico Na Suica (2007), Acustico en Suiza Inedito (2009), Cantora, Vol. 2 (2009). But it doesn't show the album type - studio, live or something else. I can assume that the 3 studio albums are the albums mentionCorazón Libre (2005), Cantora 1 (2009), and Cantora 2 (2009).

**Your Turn 4:**
Thought: I have verified from multiple sources that there are exactly 3 studio albums: Corazón Libre (2005), Cantora 1 (2009), and Cantora 2 (2009).
Code:
```py
final_answer("3")
```<end_code>

---
**EXAMPLE 3: Understanding Context and Alternative Terms**
*Question:* What is the surname of the equine veterinarian mentioned in the LibreTexts chemistry exercises?

**Your Turn 1:**
Thought: I need to find the LibreTexts chemistry exercises and look for mentions of an equine veterinarian. I should also search for related terms like "horse doctor" or "veterinarian" since the exact phrase might not be used.
Code:
```py
web_search(query="LibreTexts chemistry exercises equine veterinarian horse doctor")
```<end_code>
*Observation:* Search results show the LibreTexts exercises page.

**Your Turn 2:**
Thought: I found the exercises page. Now I need to read the content carefully, looking not just for "equine veterinarian" but also for related terms and contextual descriptions of someone who treats horses.
Code:
```py
visit_webpage(url="https://chem.libretexts.org/Bookshelves/Introductory_Chemistry/Introductory_Chemistry_(LibreTexts)/01%3A_The_Chemical_World/1.E%3A_Exercises")
```<end_code>
*Observation:* The page contains a story about Pasteur and a "horse doctor in eastern France named Louvrier" who claimed to have a cure for anthrax.

**Your Turn 3:**
Thought: I found the answer! The "horse doctor" mentioned is Louvrier. Even though the text says "horse doctor" instead of "equine veterinarian", this is clearly the same profession and the person being asked about.
Code:
```py
final_answer("Louvrier")
```<end_code>

"""

    def generate(
        self,
        messages: list[ChatMessage],
        stop_sequences: list[str] | None = None,
        response_format: dict[str, str] | None = None,
        tools_to_call_from: list[Tool] | None = None,
        **kwargs,
    ) -> ChatMessage:
        retry_count = 0
        delay = INITIAL_RETRY_DELAY

        # The smol-agent framework prepares the full conversation history.
        # We concatenate the content of all messages to provide full context.
        conversation_history = []
        for message in messages:
            content = ""
            if isinstance(message, ChatMessage) and message.content:
                content = message.content
            elif isinstance(message, dict) and 'content' in message:
                content = str(message['content'])
            else:
                content = str(message)
            conversation_history.append(content)
        
        prompt = "\n".join(conversation_history)

        # The system prompt comes first, followed by the full conversation.
        full_prompt = f"{self.system_prompt}\n\n{prompt}"
        
        while True:
            try:
                self.rate_limiter.wait_if_needed()

                response = self.model.generate_content(full_prompt)
                
                response_text = ""
                if hasattr(response, 'text'):
                    response_text = response.text
                elif hasattr(response, 'parts') and response.parts:
                    response_text = "".join(part.text for part in response.parts if hasattr(part, 'text'))
                elif isinstance(response, str):
                    response_text = response
                else:
                    response_text = str(response)
                    
                return ChatMessage(
                    role=MessageRole.ASSISTANT,
                    content=response_text,
                    raw=response
                )
                    
            except Exception as e:
                error_str = str(e)
                
                # Check if it's a rate limit error
                if "429" in error_str and retry_count < MAX_RETRIES:
                    retry_count += 1
                    
                    # Add some random jitter to prevent all retries happening at exactly the same time
                    jitter = random.uniform(0, 0.1) * delay
                    sleep_time = delay + jitter
                    
                    print(f"Rate limit hit. Retrying in {sleep_time:.2f} seconds (attempt {retry_count}/{MAX_RETRIES})")
                    time.sleep(sleep_time)
                    
                    # Exponential backoff
                    delay = min(delay * 2, MAX_RETRY_DELAY)
                    continue
                    
                print(f"Error in generate: {e}")
                raise e

class MyAgent:
    def __init__(self):
        gemini_api_key = os.getenv("GEMINI_API_KEY")
        if not gemini_api_key:
            raise ValueError("GEMINI_API_KEY not set in environment variables.")
        
        self.model = GeminiModel(gemini_api_key)
        
        self.agent = CodeAgent(
            tools=[
                FinalAnswerTool(), 
                DuckDuckGoSearchTool(),
                WikipediaSearchTool(),
                VisitWebpageTool(),
            ],
            model=self.model,
            max_steps=10 # Increased for better verification and accuracy
        )

    def __call__(self, question: str) -> str:
        print(f"\n=== Processing Question: {question} ===")
        try:
            # agent.run() executes the plan and returns the final answer.
            answer = self.agent.run(question)
            print(f"\n=== Final Answer from Agent ===\n{answer}\n===")

            # If the agent returns a string, use it. Otherwise, indicate no answer was found.
            if isinstance(answer, str) and answer:
                return answer
            else:
                # This case might be hit if the agent finishes without a clear answer string.
                return "I was unable to find a definitive answer."

        except Exception as e:
            error_message = str(e)
            print(f"An error occurred while processing the question: {error_message}")
            # Check for a timeout or max steps error from the agent.
            if "Agent stopped after" in error_message and "final_answer" in error_message:
                return "I was unable to find a definitive answer within the allowed steps."
            return f"An error occurred: {error_message}"

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 = MyAgent()
    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.
        """
    )

    with gr.Tab("Main Evaluation"):
        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)
    # 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)