app.py

import os
import gradio as gr
import requests
import pandas as pd
from typing import List, Dict, Any
from dotenv import load_dotenv
import json
import traceback
import subprocess
import sys
import wikipedia

# LlamaIndex Imports
from llama_index.core.llms import LLM
from llama_index.llms.gemini import Gemini
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
from llama_index.core.tools import BaseTool
from llama_index.core.agent import ReActAgent
from llama_index.core.memory import ChatMemoryBuffer

# Load environment variables
load_dotenv()

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
OUTPUT_FORMAT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""

# --- Basic Agent Definition ---
class BasicAgent:
    """A LlamaIndex-based agent."""
    def __init__(self):
        print("BasicAgent initialized.")
        # Initialize the core components
        self.llm = self._initialize_llm()
        
        # Import get_tools from agent.py here to avoid circular imports
        from agent import get_tools
        self.tools = get_tools()
        
        self.memory = ChatMemoryBuffer.from_defaults(token_limit=3900)
        # Build the agent
        self.agent = self._build_agent()
        print("Agent setup complete.")
        
    def _initialize_llm(self) -> LLM:
        """Initialize the LLM based on configuration."""
        provider = os.getenv("DEFAULT_LLM_PROVIDER", "gemini").lower()
        
        if provider == "gemini":
            api_key = os.getenv("GOOGLE_API_KEY")
            if not api_key:
                raise ValueError("GOOGLE_API_KEY not found in environment variables")
            
            return Gemini(
                model_name="models/gemini-1.5-flash",
                api_key=api_key,
                temperature=0.1,
                top_p=0.95,
                max_tokens=1024,
            )
            
        elif provider == "huggingface":
            api_key = os.getenv("HUGGINGFACE_API_KEY")
            if not api_key:
                raise ValueError("HUGGINGFACE_API_KEY not found in environment variables")
            
            return HuggingFaceInferenceAPI(
                model_name="Qwen/Qwen2.5-Coder-32B-Instruct",
                api_key=api_key,
                temperature=0.1,
                max_tokens=1024,
            )
        else:
            raise ValueError(f"Unsupported LLM provider: {provider}")
    
    def _build_agent(self) -> ReActAgent:
        """Build and return the agent."""
        # Load system prompt from file
        try:
            with open("system_prompt.txt", "r", encoding="utf-8") as f:
                system_prompt = f.read()
        except Exception as e:
            print(f"Error loading system prompt: {e}")
            system_prompt = "You are an intelligent agent designed to answer a wide variety of questions."
        
        return ReActAgent.from_tools(
            tools=self.tools,
            llm=self.llm,
            memory=self.memory,
            system_prompt=system_prompt,
            verbose=True,
        )

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            # Process the question
            response = self.agent.query(question)
            answer_text = str(response)
            
            # Extract the FINAL ANSWER part if it exists
            if "FINAL ANSWER:" in answer_text:
                reasoning_trace = answer_text.split("FINAL ANSWER:")[0].strip()
                model_answer = answer_text.split("FINAL ANSWER:")[1].strip()
                
                # For debugging/logging purposes
                print(f"Reasoning trace (first 100 chars): {reasoning_trace[:100]}..." if len(reasoning_trace) > 100 else f"Reasoning trace: {reasoning_trace}")
                print(f"Agent generated answer: {model_answer[:50]}..." if len(model_answer) > 50 else f"Agent generated answer: {model_answer}")
                
                # Return only the model_answer as plain text (no JSON)
                return model_answer
            else:
                # If no FINAL ANSWER pattern, return the whole response
                print(f"No 'FINAL ANSWER' found in response. Returning full response.")
                return answer_text
                
        except Exception as e:
            print(f"Error generating answer: {e}")
            error_msg = f"Error while answering question: {str(e)}"
            return error_msg

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 = []
    # Also create JSONL file for submission
    jsonl_output = []
    
    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:
            # Get agent response - now it's directly a string, not JSON
            model_answer = agent(question_text)
            
            # Add to answers payload
            answers_payload.append({"task_id": task_id, "submitted_answer": model_answer})
            
            # Add to results log for display
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text, 
                "Submitted Answer": model_answer
            })
            
            # Add to JSONL output
            jsonl_output.append({
                "task_id": task_id,
                "model_answer": model_answer,
                "question": question_text
            })
            
        except Exception as e:
             print(f"Error running agent on task {task_id}: {e}")
             error_msg = f"AGENT ERROR: {e}"
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": error_msg})
             answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
             jsonl_output.append({
                 "task_id": task_id,
                 "model_answer": error_msg,
                 "question": question_text
             })

    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)

    # Save JSONL output to file
    try:
        with open("submissions.jsonl", "w") as f:
            for item in jsonl_output:
                f.write(json.dumps(item) + "\n")
        print("Saved submissions to submissions.jsonl")
    except Exception as e:
        print(f"Error saving submissions.jsonl: {e}")

    # 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)