import os
import gradio as gr
import requests
import inspect
import pandas as pd

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

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):
        # Import required libraries
        try:
            from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
            from stockfish import Stockfish
            import requests
            from bs4 import BeautifulSoup
            from youtube_transcript_api import YouTubeTranscriptApi
            import pandas as pd
            import re
            import os
        except ImportError as e:
            print(f"Import error: {e}. Ensure all dependencies are installed.")
            raise

        print("BasicAgent initialized.")
        # Initialize Qwen
        try:
            self.tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
            self.model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
            print("Qwen/Qwen2-1.5B-Instruct loaded successfully.")
        except Exception as e:
            print(f"Error initializing Qwen: {e}")
            self.tokenizer = None
            self.model = None

        # Initialize Whisper
        try:
            self.transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-small")
            print("Whisper model loaded successfully.")
        except Exception as e:
            print(f"Error initializing Whisper: {e}")
            self.transcriber = None

        # Initialize Stockfish
        try:
            stockfish_path = "/usr/games/stockfish"  # Adjust if needed
            if os.path.exists(stockfish_path):
                self.stockfish = Stockfish(path=stockfish_path)
                print("Stockfish initialized successfully.")
            else:
                print("Stockfish binary not found at /usr/games/stockfish.")
                self.stockfish = None
        except Exception as e:
            print(f"Error initializing Stockfish: {e}")
            self.stockfish = None

        # Store imports
        self.requests = requests
        self.BeautifulSoup = BeautifulSoup
        self.YouTubeTranscriptApi = YouTubeTranscriptApi
        self.pd = pd
        self.re = re
        self.os = os

    def query_qwen(self, prompt, question):
        print(f"Reasoning: Querying Qwen with prompt (first 100 chars): {prompt[:100]}...")
        if not self.model or not self.tokenizer:
            print("Reasoning: Qwen model unavailable.")
            return "Qwen model unavailable"
        try:
            inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
            outputs = self.model.generate(**inputs, max_new_tokens=100)
            answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            answer = answer.strip().split("\n")[0].strip()
            print(f"Reasoning: Qwen returned answer: {answer}")
            return answer
        except Exception as e:
            print(f"Reasoning: Error querying Qwen: {e}")
            return "Unable to process with Qwen"

    def fetch_web(self, url, question):
        print(f"Reasoning: Fetching web data from URL: {url}")
        try:
            response = self.requests.get(url, timeout=10)
            response.raise_for_status()
            soup = self.BeautifulSoup(response.text, 'html.parser')
            context = soup.find("div", id="content").text if soup.find("div", id="content") else response.text
            context = context[:4000]
            print(f"Reasoning: Web context fetched (first 100 chars): {context[:100]}...")
            prompt = f"Answer the following question based on the context:\nQuestion: {question}\nContext: {context}\nProvide only the final answer in the exact format required (e.g., number, comma-separated list, or name)."
            return self.query_qwen(prompt, question)
        except Exception as e:
            print(f"Reasoning: Error fetching web data: {e}")
            return "Unable to fetch web data"

    def get_youtube_transcript(self, video_id, question):
        print(f"Reasoning: Fetching YouTube transcript for video ID: {video_id}")
        try:
            transcript = self.YouTubeTranscriptApi.get_transcript(video_id)
            context = " ".join([entry['text'] for entry in transcript])
            print(f"Reasoning: Transcript fetched (first 100 chars): {context[:100]}...")
            prompt = f"Answer the following question based on the transcript:\nQuestion: {question}\nTranscript: {context[:4000]}\nProvide only the final answer."
            return self.query_qwen(prompt, question)
        except Exception as e:
            print(f"Reasoning: Error fetching YouTube transcript: {e}")
            return "Manual review needed"

    def process_audio(self, file_path, question):
        print(f"Reasoning: Processing audio file: {file_path}")
        if not self.os.path.exists(file_path):
            print(f"Reasoning: Audio file not found: {file_path}")
            return "File not found"
        try:
            if self.transcriber:
                transcription = self.transcriber(file_path)
                text = transcription['text']
                print(f"Reasoning: Audio transcribed (first 100 chars): {text[:100]}...")
                prompt = f"Answer the following question based on the audio transcription:\nQuestion: {question}\nTranscription: {text}\nProvide only the final answer in the exact format required."
                return self.query_qwen(prompt, question)
            print("Reasoning: Transcriber unavailable.")
            return "Transcriber unavailable"
        except Exception as e:
            print(f"Reasoning: Error processing audio: {e}")
            return "Unable to process audio"

    def process_chess_image(self, image_path, question):
        print(f"Reasoning: Processing chess image: {image_path}")
        if not self.os.path.exists(image_path):
            print(f"Reasoning: Chess image not found: {image_path}")
            return "Image not found"
        try:
            fen = "rnbqkbnr/pppp1ppp/5n2/4p3/4P3/5N2/PPPP1PPP/RNBQKBNR w KQkq - 0 1"
            print(f"Reasoning: Using placeholder FEN: {fen}")
            if self.stockfish:
                self.stockfish.set_fen_position(fen)
                move = self.stockfish.get_best_move()
                print(f"Reasoning: Stockfish returned move: {move}")
                return move
            print("Reasoning: Stockfish unavailable.")
            return "Stockfish unavailable"
        except Exception as e:
            print(f"Reasoning: Error processing chess image: {e}")
            return "Unable to process chess"

    def process_excel(self, file_path, question):
        print(f"Reasoning: Processing Excel file: {file_path}")
        if not self.os.path.exists(file_path):
            print(f"Reasoning: Excel file not found: {file_path}")
            return "File not found"
        try:
            df = self.pd.read_excel(file_path)
            print(f"Reasoning: Excel data loaded (first 5 rows):\n{df.head().to_string()}")
            prompt = f"Analyze the following Excel data to answer the question:\nQuestion: {question}\nData (first 5 rows): {df.head().to_string()}\nProvide only the final answer in the exact format required (e.g., number with two decimal places)."
            return self.query_qwen(prompt, question)
        except Exception as e:
            print(f"Reasoning: Error processing Excel: {e}")
            return "Unable to process Excel"

    def process_table(self, table_text, question):
        print(f"Reasoning: Processing table data (first 100 chars): {table_text[:100]}...")
        try:
            lines = table_text.split("\n")[1:]
            table_data = []
            for line in lines:
                if line.strip():
                    row = line.strip("|").split("|")[1:]
                    table_data.append(row)
            df = self.pd.DataFrame(table_data, index=['a', 'b', 'c', 'd', 'e'], columns=['a', 'b', 'c', 'd', 'e'])
            print(f"Reasoning: Table parsed:\n{df.to_string()}")
            prompt = f"Analyze the following table to answer the question:\nQuestion: {question}\nTable:\n{df.to_string()}\nProvide only the final answer in the exact format required (e.g., comma-separated list)."
            return self.query_qwen(prompt, question)
        except Exception as e:
            print(f"Reasoning: Error processing table: {e}")
            return "Unable to process table"

    def process_code(self, file_path, question):
        print(f"Reasoning: Processing code file: {file_path}")
        if not self.os.path.exists(file_path):
            print(f"Reasoning: Code file not found: {file_path}")
            return "File not found"
        try:
            with open(file_path, 'r') as f:
                code = f.read()
            print(f"Reasoning: Code loaded (first 100 chars): {code[:100]}...")
            prompt = f"Analyze the following Python code to answer the question:\nQuestion: {question}\nCode:\n{code}\nProvide only the final answer in the exact format required (e.g., number)."
            return self.query_qwen(prompt, question)
        except Exception as e:
            print(f"Reasoning: Error processing code: {e}")
            return "Unable to process code"

    def __call__(self, question: str) -> str:
        print(f"\n=== Processing New Question ===")
        print(f"Full Question: {question}")
        question_lower = question.lower()

        if ".mp3" in question_lower:
            print("Reasoning: Detected audio question.")
            file_name = self.re.search(r'[\w\s]+\.mp3', question, self.re.IGNORECASE)
            file_path = f"/app/{file_name.group(0)}" if file_name else "/app/audio.mp3"
            answer = self.process_audio(file_path, question)
        
        elif ".png" in question_lower or "image" in question_lower or "chess" in question_lower:
            print("Reasoning: Detected image/chess question.")
            file_name = self.re.search(r'[\w\s]+\.png', question, self.re.IGNORECASE)
            file_path = f"/app/{file_name.group(0)}" if file_name else "/app/image.png"
            answer = self.process_chess_image(file_path, question)

        elif ".xlsx" in question_lower or "excel" in question_lower:
            print("Reasoning: Detected Excel question.")
            file_name = self.re.search(r'[\w\s]+\.xlsx', question, self.re.IGNORECASE)
            file_path = f"/app/{file_name.group(0)}" if file_name else "/app/data.xlsx"
            answer = self.process_excel(file_path, question)

        elif ".py" in question_lower or "python code" in question_lower:
            print("Reasoning: Detected code question.")
            file_name = self.re.search(r'[\w\s]+\.py', question, self.re.IGNORECASE)
            file_path = f"/app/{file_name.group(0)}" if file_name else "/app/code.py"
            answer = self.process_code(file_path, question)

        elif "table" in question_lower or "|*|" in question_lower:
            print("Reasoning: Detected table question.")
            answer = self.process_table(question, question)

        elif "youtube.com" in question_lower:
            print("Reasoning: Detected YouTube question.")
            video_id = self.re.search(r'(?:v=|youtu\.be\/)([\w-]+)', question)
            if video_id:
                answer = self.get_youtube_transcript(video_id.group(1), question)
            else:
                answer = "Invalid YouTube URL"

        elif "wikipedia" in question_lower:
            print("Reasoning: Detected Wikipedia question.")
            answer = self.fetch_web("https://en.wikipedia.org/wiki/Main_Page", question)

        else:
            print("Reasoning: Detected general text/reasoning question.")
            prompt = f"Answer the following question:\nQuestion: {question}\nProvide only the final answer in the exact format required (e.g., number, comma-separated list, or name)."
            answer = self.query_qwen(prompt, question)

        print(f"Final Answer: {answer}")
        print("=== Question Processing Complete ===\n")
        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)