archive/app-old.py

import asyncio
import inspect
import json
import os
import time
from typing import Any, Dict, List, Optional

import gradio as gr
import pandas as pd
import requests
from dotenv import load_dotenv
from langchain_community.chat_models import ChatHuggingFace
from langchain_community.llms import HuggingFaceEndpoint
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.tools import StructuredTool

from tools import (absolute, add, divide, exponential, floor_divide,
                   get_current_time_in_timezone, logarithm, modulus, multiply,
                   power, roman_calculator_converter, square_root, subtract,
                   web_search)

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MAX_AGENT_ITERATIONS = 15
MAX_CONCURRENT_REQUESTS = 5  # Limit concurrent requests to avoid overwhelming the API

load_dotenv()
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")

# Quick test to see if tokens are available.
print(f"Available env vars: {[k for k in os.environ.keys() if 'TOKEN' in k or 'HF' in k]}")

# Global cache for answers
answer_cache = {}

class ImprovedAgent:
    def __init__(self):
        if not HUGGINGFACEHUB_API_TOKEN:
            raise ValueError("Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN.")

        print("ImprovedAgent initialized.")
        
        # Initialize LLM with better parameters
        self.llm = HuggingFaceEndpoint(
            repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
            huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
            temperature=0.1,  # Lower temperature for more consistent responses
            max_new_tokens=1024,
            timeout=30,
        )
        
        self.chat = ChatHuggingFace(llm=self.llm, verbose=False)
        
        # Initialize tools
        self.tools = [
            multiply, add, subtract, power, divide, modulus,
            square_root, floor_divide, absolute, logarithm,
            exponential, web_search, roman_calculator_converter,
            get_current_time_in_timezone
        ]
        
        self.chat_with_tools = self.chat.bind_tools(self.tools)
        print(f"Total tools available: {len(self.tools)}")
        
        # Create tool mapping for easier access
        self.tool_map = {tool.name: tool for tool in self.tools}

    def _extract_tool_calls(self, response) -> List[Dict]:
        """Extract tool calls from the response"""
        tool_calls = []
        if hasattr(response, 'tool_calls') and response.tool_calls:
            for tool_call in response.tool_calls:
                tool_calls.append({
                    'name': tool_call['name'],
                    'args': tool_call['args']
                })
        return tool_calls

    def _execute_tool_calls(self, tool_calls: List[Dict]) -> List[str]:
        """Execute tool calls and return results"""
        results = []
        for tool_call in tool_calls:
            tool_name = tool_call['name']
            tool_args = tool_call['args']
            
            if tool_name in self.tool_map:
                try:
                    tool = self.tool_map[tool_name]
                    result = tool.invoke(tool_args)
                    results.append(f"Tool {tool_name} result: {result}")
                except Exception as e:
                    results.append(f"Tool {tool_name} error: {str(e)}")
            else:
                results.append(f"Unknown tool: {tool_name}")
        
        return results

    async def answer(self, question: str) -> str:
        """Improved answer method with better error handling and tool usage"""
        print(f"Processing question: {question[:100]}...")
        
        try:
            # Create system prompt for better instruction following
            system_prompt = """You are a helpful AI assistant with access to various tools. 
            When answering questions, use the appropriate tools when needed and provide clear, concise answers.
            If you need to perform calculations, use the math tools available.
            If you need current information, use the web search tool.
            Always provide a final answer after using tools."""
            
            messages = [
                HumanMessage(content=f"{system_prompt}\n\nQuestion: {question}")
            ]
            
            # Initial response
            response = await asyncio.to_thread(self.chat_with_tools.invoke, messages)
            
            # Handle tool calls if present
            max_iterations = 3
            iteration = 0
            
            while iteration < max_iterations:
                tool_calls = self._extract_tool_calls(response)
                
                if not tool_calls:
                    break
                
                # Execute tool calls
                tool_results = self._execute_tool_calls(tool_calls)
                
                # Add tool results to conversation
                messages.append(AIMessage(content=response.content))
                messages.append(HumanMessage(content=f"Tool results: {'; '.join(tool_results)}. Please provide a final answer based on these results."))
                
                # Get next response
                response = await asyncio.to_thread(self.chat_with_tools.invoke, messages)
                iteration += 1
            
            # Extract final answer
            final_answer = response.content.strip()
            
            # Clean up the response - remove any tool call artifacts
            if "Tool " in final_answer and "result:" in final_answer:
                # Try to extract just the final answer part
                lines = final_answer.split('\n')
                for line in reversed(lines):
                    if line.strip() and not line.startswith('Tool ') and not 'result:' in line:
                        final_answer = line.strip()
                        break
            
            return final_answer
            
        except Exception as e:
            print(f"Error in answer method: {e}")
            return f"Error processing question: {str(e)}"

    def answer_sync(self, question: str) -> str:
        """Synchronous version of answer method"""
        try:
            return asyncio.run(self.answer(question))
        except Exception as e:
            print(f"Error in sync answer: {e}")
            return f"Error: {str(e)}"

async def process_questions_batch(agent, questions_batch, semaphore):
    """Process a batch of questions with rate limiting"""
    results = []
    
    async def process_single_question(task_id, question):
        async with semaphore:
            try:
                # Check cache first
                cache_key = f"{task_id}_{hash(question)}"
                if cache_key in answer_cache:
                    print(f"Using cached answer for task {task_id}")
                    return task_id, question, answer_cache[cache_key], None
                
                answer = await agent.answer(question)
                
                # Cache the result
                answer_cache[cache_key] = answer
                
                return task_id, question, answer, None
            except Exception as e:
                print(f"Error processing task {task_id}: {e}")
                return task_id, question, None, str(e)
    
    # Create semaphore for rate limiting
    tasks = []
    for item in questions_batch:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if task_id and question_text is not None:
            tasks.append(process_single_question(task_id, question_text))
    
    if tasks:
        results = await asyncio.gather(*tasks, return_exceptions=True)
    
    return results

async def run_agent_async_improved(agent, questions_data):
    """Improved async processing with batching and caching"""
    results_log, answers_payload = [], []
    
    # Create semaphore for rate limiting
    semaphore = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
    
    # Process questions in batches
    batch_size = 10
    batches = [questions_data[i:i + batch_size] for i in range(0, len(questions_data), batch_size)]
    
    print(f"Processing {len(questions_data)} questions in {len(batches)} batches...")
    
    for i, batch in enumerate(batches):
        print(f"Processing batch {i+1}/{len(batches)} ({len(batch)} questions)...")
        
        try:
            batch_results = await process_questions_batch(agent, batch, semaphore)
            
            for result in batch_results:
                if isinstance(result, Exception):
                    print(f"Batch processing error: {result}")
                    continue
                
                task_id, question, answer, error = result
                
                if error:
                    print(f"Error in task {task_id}: {error}")
                    results_log.append({
                        "Task ID": task_id, 
                        "Question": question[:100] + "..." if len(question) > 100 else question,
                        "Submitted Answer": f"ERROR: {error}"
                    })
                else:
                    answers_payload.append({"task_id": task_id, "submitted_answer": answer})
                    results_log.append({
                        "Task ID": task_id, 
                        "Question": question[:100] + "..." if len(question) > 100 else question,
                        "Submitted Answer": answer[:200] + "..." if len(answer) > 200 else answer
                    })
            
            # Small delay between batches to be respectful
            if i < len(batches) - 1:
                await asyncio.sleep(1)
                
        except Exception as e:
            print(f"Error processing batch {i+1}: {e}")
            # Continue with next batch
            continue
    
    return results_log, answers_payload

def cache_answers(profile: gr.OAuthProfile | None):
    """Cache answers without submitting"""
    if not profile:
        return "Please log in to Hugging Face first.", None
    
    username = profile.username
    print(f"Caching answers for user: {username}")
    
    # Fetch questions
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        
        if not questions_data:
            return "No questions found.", None
        
        print(f"Fetched {len(questions_data)} questions for caching.")
        
        # Initialize agent
        try:
            agent = ImprovedAgent()
        except Exception as e:
            print(f"Full error details: {e}")
            return f"Error initializing agent: {e}", None
        
        # Process questions
        results_log, answers_payload = asyncio.run(run_agent_async_improved(agent, questions_data))
        
        # Store in global cache with username
        answer_cache[f"user_{username}"] = answers_payload
        
        status = f"Cached {len(answers_payload)} answers for user {username}. Ready to submit!"
        results_df = pd.DataFrame(results_log)
        
        return status, results_df
        
    except Exception as e:
        print(f"Error caching answers: {e}")
        return f"Error caching answers: {e}", None

def submit_cached_answers(profile: gr.OAuthProfile | None):
    """Submit previously cached answers"""
    if not profile:
        return "Please log in to Hugging Face first.", None
    
    username = profile.username
    cache_key = f"user_{username}"
    
    if cache_key not in answer_cache:
        return "No cached answers found. Please run 'Cache Answers' first.", None
    
    answers_payload = answer_cache[cache_key]
    
    if not answers_payload:
        return "No answers to submit.", None
    
    # Get space info
    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown"
    
    # Submit
    api_url = DEFAULT_API_URL
    submit_url = f"{api_url}/submit"
    
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }
    
    try:
        print(f"Submitting {len(answers_payload)} cached answers...")
        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.')}"
        )
        
        # Clear cache after successful submission
        if cache_key in answer_cache:
            del answer_cache[cache_key]
        
        return final_status, None
        
    except Exception as e:
        print(f"Submission error: {e}")
        return f"Submission failed: {e}", None

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:
        # Using the retry function instead of direct request
        response = make_request_with_retry(questions_url)
        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:
        # Using the retry function for submission as well
        response = make_request_with_retry(submit_url, method="post", json_data=submission_data, timeout=60)
        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)