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

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

#Load environment variables
load_dotenv()

import io
import contextlib
import traceback
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
# from smolagents import Tool, CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool, HfApiModel # These are already imported above

class CodeLlamaTool(Tool):
    name = "code_llama_tool"
    description = "Solves reasoning/code questions using Meta Code Llama 7B Instruct"

    inputs = {
        "question": {
            "type": "string",
            "description": "The question requiring code-based or reasoning-based solution"
        }
    }
    output_type = "string"

    def __init__(self):
        self.model_id = "codellama/CodeLlama-7b-Instruct-hf"
        token = os.getenv("HF_TOKEN")

        self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, token=token)
        self.model = AutoModelForCausalLM.from_pretrained(
            self.model_id,
            device_map="auto",
            torch_dtype="auto",
            token=token
        )
        self.pipeline = pipeline(
            "text-generation",
            model=self.model,
            tokenizer=self.tokenizer,
            max_new_tokens=256, # 512
            temperature=0.0,
            truncation=True
        )

    def forward(self, question: str) -> str:
        # Corrected: Use self.prompt and then pass it to the pipeline
        self.prompt = f"""You are an AI that uses Python code to answer questions.
Question: {question}
Instructions:
- If solving requires code, use a block like <tool>code</tool>.
- Always end with <final>FINAL ANSWER</final> containing the final number or string.
Example:
Question: What is 5 * sqrt(36)?
Answer:
<tool>
import math
print(5 * math.sqrt(36))
</tool>
<final>30.0</final>
Answer:"""

        response = self.pipeline(self.prompt)[0]["generated_text"] # Pass self.prompt
        return self.parse_and_execute(response)

    def parse_and_execute(self, response: str) -> str:
        try:
            # Extract and run code if exists
            if "<tool>" in response and "</tool>" in response:
                code = response.split("<tool>")[1].split("</tool>")[0].strip()
                result = self._run_code(code)
                return f"FINAL ANSWER (code output): {result}"

            # Extract final result directly
            elif "<final>" in response and "</final>" in response:
                final = response.split("<final>")[1].split("</final>")[0].strip()
                return f"FINAL ANSWER: {final}"

            return f"Could not extract final answer.\n\n{response}"

        except Exception as e:
            return f"Error in parse_and_execute: {str(e)}\n\nFull response:\n{response}"

    def _run_code(self, code: str) -> str:
        buffer = io.StringIO()
        try:
            with contextlib.redirect_stdout(buffer):
                exec(code, {})
            return buffer.getvalue().strip()
        except Exception:
            return f"Error executing code:\n{traceback.format_exc()}"

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.append(w) # Corrected: Use append instead of push
            word_counts = Counter(filtered_words)
            k = 5
            return str(heapq.nlargest(k, word_counts.items(), key=lambda x: x[1])) # Corrected: Convert list of tuples to string
        except Exception as e:
            return f"Error during extracting most common words: {e}"

@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:

        # Corrected: Handle various YouTube URL formats
        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):  # Direct ID
            video_id = url.strip()
        
        if not video_id:
            return f"YouTube URL or ID: {url} is invalid or not supported!"

        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_TOKEN") # Corrected: Use HF_TOKEN

        # Initialize tokenizer
        self.model_id = "codellama/CodeLlama-7b-Instruct-hf"
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, token=token)
                
        # Model (e.g., HfApiModel or other)
        self.model = HfApiModel(
            model=self.model_id,
            temperature=0.0,
            token=token
        )

        # Existing tools
        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()

        # New Llama Tool
        code_llama_tool = CodeLlamaTool()

        self.system_prompt = f"""
You are my general AI assistant. Your task is to answer the question I asked.
First, provide reasoning. Then return: FINAL ANSWER: [your answer].
Answer should be a short string, number, or comma-separated list. Keep it brief.
"""

        self.agent = CodeAgent(
            model=self.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,
                code_llama_tool
            ],
            add_base_tools=True
        )
        self.agent.prompt_templates["system_prompt"] = self.agent.prompt_templates["system_prompt"] + self.system_prompt

    def _build_safe_prompt(self, history: str, question: str, max_total_tokens=32768, reserve_for_output=2048):
        max_input_tokens = max_total_tokens - reserve_for_output
        full_prompt = f"{self.system_prompt}\n{history}\nQuestion: {question}"
        tokenized = self.tokenizer(full_prompt, truncation=True, max_length=max_input_tokens, return_tensors="pt")
        return self.tokenizer.decode(tokenized["input_ids"][0])

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        history = ""  # could be conversation history, if available
        safe_prompt = self._build_safe_prompt(history, question)
        answer = self.agent.run(safe_prompt)
        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)