app.py

import os
import gradio as gr
import requests
# import inspect
import pandas as pd
from typing import Optional


from smolagents import CodeAgent, LiteLLMModel, VisitWebpageTool, DuckDuckGoSearchTool

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


# Define the Agent Class
class BasicAgent:
    def __init__(self):
        print("Initializing Mistral-Powered Agent...")
        
        # --- 1. API KEY CHECK ---
        mistral_key = os.getenv("MISTRAL_API_KEY")
        if not mistral_key:
            # Fallback to Qwen if Mistral is unavailable.
            print("Mistral Key not found. Please set MISTRAL_API_KEY for best results.")
            # Fallback logic if needed, but for now we raise error to alert user
            raise ValueError("MISTRAL_API_KEY missing!")

        # --- 2. MODEL SETUP ---
        model = LiteLLMModel(
            model_id="mistral/mistral-large-latest",
            api_key=mistral_key
        )

        # --- 3. TOOLS ---
        search_tool = DuckDuckGoSearchTool()
        visit_tool = VisitWebpageTool()

        # --- 4. CREATE AGENT ---
        self.agent = CodeAgent(
            tools=[search_tool, visit_tool],
            model=model,
            additional_authorized_imports=[
                "numpy", "pandas", "math", "datetime", "re", "csv", "json", "random", "itertools"
            ],
            max_steps=25,
            verbosity_level=2,
            name="Mistral_Gaia_Solver" 
        )

    def __call__(self, question: str, file_path: str = None) -> str:
        # Prompt Logic
        prompt = f"""
        Task: {question}
        
        INSTRUCTIONS:
        1. Use Python code to solve this step-by-step.
        2. If a file is attached, YOU MUST READ IT using Python immediately.
        3. Output ONLY the final answer value.
        """
        
        if file_path:
            prompt += f"\n\n ATTACHED FILE: '{file_path}'"

        try:
            print(f" Agent working on: {question[:30]}...")
            response = self.agent.run(prompt)
            
            # Output Cleaning
            final_answer = str(response).replace("Final Answer:", "").strip()
            
            if final_answer.endswith(".") and len(final_answer) < 20:
                final_answer = final_answer[:-1]
                
            return final_answer
            
        except Exception as e:
            print(f" Error in Agent: {e}")
            return f"Error: {e}"

# Evaluation
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    1. Fetch questions.
    2. Download the file (previously missing).
    3. Run the agent.
    4. Submit the results.
    """
    
    # --- A. LOGIN CHECK ---
    if profile is None:
        return " Please Login to Hugging Face with the button above.", None
    
    username = profile.username
    space_id = os.getenv("SPACE_ID")
    
    # URLs
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # --- B. INIT AGENT ---
    try:
        agent = BasicAgent()
    except Exception as e:
        return f" Agent Init Error: {e}", None

    agent_code_link = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Code Link: {agent_code_link}")

    # --- C. FETCH QUESTIONS ---
    try:
        print(" Fetching questions...")
        questions_data = requests.get(questions_url).json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    results_log = []
    answers_payload = []

    print(f" Starting processing of {len(questions_data)} questions...")

    # --- D. PROCESSING LOOP ---
    for item in questions_data:
        task_id = item["task_id"]
        question_text = item["question"]
        file_name = item.get("file_name") # GAIA tasks often have files

        print(f"\n--- Processing Task {task_id} ---")
        
        local_file_path = None

        # 1. DOWNLOAD FILE (CRITICAL STEP)
        if file_name:
            print(f" Downloading file: {file_name}")
            try:
                file_url = f"{api_url}/files/{task_id}"
                file_resp = requests.get(file_url, timeout=10)
                
                if file_resp.status_code == 200:
                    with open(file_name, "wb") as f:
                        f.write(file_resp.content)
                    local_file_path = file_name
                    print(" File downloaded successfully.")
                else:
                    print(f" File download failed (Status {file_resp.status_code})")
            except Exception as e:
                print(f" File download error: {e}")

        # 2. RUN AGENT
        try:
            # The agent receives the file path as input.
            submitted_answer = agent(question_text, file_path=local_file_path)
            
            print(f" Final Answer: {submitted_answer}")
            
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text, 
                "File": file_name if file_name else "None",
                "Answer": submitted_answer
            })
            
        except Exception as e:
            results_log.append({"Task ID": task_id, "Error": str(e)})

        # 3. CLEANUP (File delete karo)
        if local_file_path and os.path.exists(local_file_path):
            os.remove(local_file_path)

    # --- E. SUBMIT ---
    print("Submitting answers to leaderboard...")
    submission_data = {
        "username": username,
        "agent_code": agent_code_link,
        "answers": answers_payload
    }

    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        res_json = response.json()
        
        score = res_json.get('score', 0)
        correct = res_json.get('correct_count', 0)
        
        status_msg = (
            f"Submission Done!\n"
            f"User: {username}\n"
            f"Score: {score}%\n"
            f"Correct: {correct}"
        )
        return status_msg, pd.DataFrame(results_log)

    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)


# --- GRADIO UI ---
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent Solver (Mistral + Files Fix)")
    gr.Markdown("""
    **Instruction:**
    1. Login via Hugging Face button.
    2. Click 'Run Evaluation'.
    3. Wait (it takes time to process all questions).
    """)
    
    gr.LoginButton()
    
    run_btn = gr.Button("Run Evaluation & Submit", variant="primary")
    
    status_out = gr.Textbox(label="Status")
    results_df = gr.DataFrame(label="Detailed Logs")
    
    run_btn.click(
        fn=run_and_submit_all,
        outputs=[status_out, results_df]
    )

if __name__ == "__main__":
    # Enabling the queue eliminates timeout issues.
    demo.queue(default_concurrency_limit=1).launch()