app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import time
import json
from typing import Dict, List, Union, Optional

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

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        # Initialize the Hugging Face API client
        self.hf_api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
        self.hf_api_token = os.getenv("HF_API_TOKEN")
        if not self.hf_api_token:
            print("WARNING: HF_API_TOKEN not found. Using default fallback methods.")
        self.headers = {"Authorization": f"Bearer {self.hf_api_token}"} if self.hf_api_token else {}
        self.max_retries = 3
        self.retry_delay = 2  # seconds
        
    def query_llm(self, prompt):
        """Send a prompt to the LLM API and return the response."""
        if not self.hf_api_token:
            # Fallback to a rule-based approach if no API token
            return self.rule_based_answer(prompt)
            
        payload = {
            "inputs": prompt,
            "parameters": {
                "max_new_tokens": 512,
                "temperature": 0.7,
                "top_p": 0.9,
                "do_sample": True
            }
        }
        
        for attempt in range(self.max_retries):
            try:
                response = requests.post(self.hf_api_url, headers=self.headers, json=payload, timeout=30)
                response.raise_for_status()
                result = response.json()
                
                # Extract the generated text from the response
                if isinstance(result, list) and len(result) > 0:
                    generated_text = result[0].get("generated_text", "")
                    # Clean up the response to get just the answer
                    return self.clean_response(generated_text, prompt)
                return "I couldn't generate a proper response."
                
            except Exception as e:
                print(f"Attempt {attempt+1}/{self.max_retries} failed: {str(e)}")
                if attempt < self.max_retries - 1:
                    time.sleep(self.retry_delay)
                else:
                    # Fall back to rule-based method on failure
                    return self.rule_based_answer(prompt)
    
    def clean_response(self, response, prompt):
        """Clean up the LLM response to extract the answer."""
        # Remove the prompt from the beginning if it's included
        if response.startswith(prompt):
            response = response[len(prompt):]
        
        # Try to find where the model's actual answer begins
        # This is model-specific and may need adjustments
        markers = ["<answer>", "<response>", "Answer:", "Response:"]
        for marker in markers:
            if marker.lower() in response.lower():
                parts = response.lower().split(marker.lower(), 1)
                if len(parts) > 1:
                    response = parts[1].strip()
        
        # Remove any closing tags if they exist
        end_markers = ["</answer>", "</response>"]
        for marker in end_markers:
            if marker.lower() in response.lower():
                response = response.lower().split(marker.lower())[0].strip()
        
        return response.strip()
    
    def rule_based_answer(self, question):
        """Fallback method using rule-based answers for common question types."""
        question_lower = question.lower()
        
        # Simple pattern matching for common question types
        if "what is" in question_lower or "define" in question_lower:
            if "agent" in question_lower:
                return "An agent is an autonomous entity that observes and acts upon an environment using sensors and actuators, usually to achieve specific goals."
            if "gaia" in question_lower:
                return "GAIA (General AI Assistant) is a framework for creating and evaluating AI assistants that can perform a wide range of tasks."
        
        if "how to" in question_lower:
            return "To accomplish this task, you should first understand the requirements, then implement a solution step by step, and finally test your implementation."
        
        if "example" in question_lower:
            return "Here's an example implementation that demonstrates the concept in a practical manner."
        
        # Default response for unmatched questions
        return "Based on my understanding, the answer involves analyzing the context carefully and applying the relevant principles to arrive at a solution."
    
    def format_prompt(self, question):
        """Format the question into a proper prompt for the LLM."""
        return f"""You are an intelligent AI assistant. Please answer the following question accurately and concisely:

Question: {question}

Answer:"""
    
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        try:
            # Format the question as a prompt
            prompt = self.format_prompt(question)
            
            # Query the LLM
            answer = self.query_llm(prompt)
            
            print(f"Agent returning answer (first 50 chars): {answer[:50]}...")
            return answer
            
        except Exception as e:
            print(f"Error in agent: {e}")
            # Fallback to the rule-based method if anything goes wrong
            fallback_answer = self.rule_based_answer(question)
            print(f"Agent returning fallback answer: {fallback_answer[:50]}...")
            return fallback_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)