app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
#import smolagents  #to test
from smolagents import CodeAgent, InferenceClientModel, load_tool, tool #DuckDuckGoSearchTool,
from huggingface_hub import InferenceClient
import json
from final_answer import FinalAnswerTool
from visit_webpage import VisitWebpageTool
from web_search import web_search_DuckDuckGoSearchTool
from wikipediaLookup import WikipediaLookupTool

api_url = "https://agents-course-unit4-scoring.hf.space"
questions_url = f"{api_url}/questions"
submit_url = f"{api_url}/submit"

class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        fixed_answer = "This is a default answer."
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_answer

def load_questions_from_file(filepath="questions.json"):
    try:
        with open(filepath, "r", encoding="utf-8") as f:
            questions_data = json.load(f)
            if not questions_data:
                print("Loaded file is empty.")
                return "Loaded file is empty.", None
            print(f"Loaded {len(questions_data)} questions from file.")
            return "Loaded questions successfully.", questions_data
    except FileNotFoundError:
        print("File not found. Please run the API fetch first.")
        return "File not found.", None
    except json.JSONDecodeError as e:
        print(f"Error decoding JSON: {e}")
        return f"Error decoding JSON: {e}", None
    except Exception as e:
        print(f"Unexpected error: {e}")
        return f"Unexpected error: {e}", None

#set up
#token

#Model
model = InferenceClientModel(
max_tokens=2096,
temperature=0.5,
model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
custom_role_conversions=None,
)

#Tools
final_answer = FinalAnswerTool()
#duckDuckGoSearch = DuckDuckGoSearchTool() #smolagent version
visitWebpage = VisitWebpageTool()
wikipediaLookup = WikipediaLookupTool()
webSearch = web_search_DuckDuckGoSearchTool()

#Agent
agent_codeagent = CodeAgent(
    model=model,
    tools=[final_answer, wikipediaLookup, visitWebpage, webSearch], ## add your tools here (don't remove final answer) duckDuckGoSearch,
    max_steps=3,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name=None,
    description=None
    #prompt_templates=prompt_templates
)

# Gradio handler that runs the agent
def run_once(state):
    if state is not None:
        return "Already run once. Refresh to rerun.", state

    status_message, questions_data = load_questions_from_file()
    if questions_data is None or len(questions_data) == 0:
        return "No questions found or failed to load.", None

    question = questions_data[0]
    question_text = question["question"]
    task_id = question["task_id"]
    print(f"\nTask ID: {task_id}")
    print(f"Question: {question_text}")

    try:
        answer = agent_codeagent(question_text)
        output = f"Answer to task {task_id}:\n{answer}"
        return output, output
    except Exception as e:
        return f"Error running agent: {e}", None

 

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## Run AI Agent Once")

    output_text = gr.Textbox(label="Agent Output", lines=10)
    run_button = gr.Button("Run Agent")
    state = gr.State()  # cache variable to prevent re-runs

    run_button.click(fn=run_once, inputs=state, outputs=[output_text, state])

# Launch the interface
demo.launch()