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| import os, threading | |
| import gradio as gr | |
| from crew import run_crew | |
| from utils import get_questions | |
| def ask(question, openai_api_key, gemini_api_key, anthropic_api_key, file_name = ""): | |
| """ | |
| Ask General AI Assistant a question to answer. | |
| Args: | |
| question (str): The question to answer | |
| openai_api_key (str): OpenAI API key | |
| gemini_api_key (str): Gemini API key | |
| anthropic_api_key (str): Anthropic API key | |
| file_name (str): Optional file name | |
| Returns: | |
| str: The answer to the question | |
| """ | |
| if not question: | |
| raise gr.Error("Question is required.") | |
| if not openai_api_key: | |
| raise gr.Error("OpenAI API Key is required.") | |
| if not gemini_api_key: | |
| raise gr.Error("Gemini API Key is required.") | |
| if not anthropic_api_key: | |
| raise gr.Error("Anthropic API Key is required.") | |
| if file_name: | |
| file_name = f"data/{file_name}" | |
| lock = threading.Lock() | |
| with lock: | |
| answer = "" | |
| try: | |
| os.environ["OPENAI_API_KEY"] = openai_api_key | |
| os.environ["GEMINI_API_KEY"] = gemini_api_key | |
| os.environ["MODEL_API_KEY"] = anthropic_api_key | |
| answer = run_crew(question, file_name) | |
| except Exception as e: | |
| raise gr.Error(e) | |
| finally: | |
| del os.environ["OPENAI_API_KEY"] | |
| del os.environ["GEMINI_API_KEY"] | |
| del os.environ["MODEL_API_KEY"] | |
| return answer | |
| gr.close_all() | |
| with gr.Blocks() as grady: | |
| gr.Markdown("## Grady - General AI Assistant") | |
| with gr.Tab("Solution"): | |
| gr.Markdown(os.environ.get("DESCRIPTION")) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| with gr.Row(): | |
| question = gr.Textbox( | |
| label="Question *", | |
| placeholder="In the 2025 Gradio Agents & MCP Hackathon, what percentage of participants submitted a solution during the last 24 hours?", | |
| interactive=True | |
| ) | |
| with gr.Row(): | |
| level = gr.Radio( | |
| choices=[1, 2, 3], | |
| label="GAIA Benchmark Level", | |
| interactive=True, | |
| scale=1 | |
| ) | |
| ground_truth = gr.Textbox( | |
| label="Ground Truth", | |
| interactive=True, | |
| scale=1 | |
| ) | |
| file_name = gr.Textbox( | |
| label="File Name", | |
| interactive=True, | |
| scale=2 | |
| ) | |
| with gr.Row(): | |
| openai_api_key = gr.Textbox( | |
| label="OpenAI API Key *", | |
| type="password", | |
| placeholder="sk‑...", | |
| interactive=True | |
| ) | |
| gemini_api_key = gr.Textbox( | |
| label="Gemini API Key *", | |
| type="password", | |
| interactive=True | |
| ) | |
| anthropic_api_key = gr.Textbox( | |
| label="Anthropic API Key *", | |
| type="password", | |
| placeholder="sk-ant-...", | |
| interactive=True | |
| ) | |
| with gr.Row(): | |
| clear_btn = gr.ClearButton( | |
| components=[question, level, ground_truth, file_name] | |
| ) | |
| submit_btn = gr.Button("Submit", variant="primary") | |
| with gr.Column(scale=1): | |
| answer = gr.Textbox( | |
| label="Answer", | |
| lines=1, | |
| interactive=False | |
| ) | |
| submit_btn.click( | |
| fn=ask, | |
| inputs=[question, openai_api_key, gemini_api_key, anthropic_api_key, file_name], | |
| outputs=answer | |
| ) | |
| QUESTION_FILE_PATH = "data/gaia_validation.jsonl" | |
| gr.Examples( | |
| label="GAIA Benchmark Level 1 Problems", | |
| examples=get_questions(QUESTION_FILE_PATH, 1), | |
| inputs=[question, level, ground_truth, file_name, openai_api_key, gemini_api_key, anthropic_api_key], | |
| outputs=answer, | |
| cache_examples=False | |
| ) | |
| gr.Examples( | |
| label="GAIA Benchmark Level 2 Problems", | |
| examples=get_questions(QUESTION_FILE_PATH, 2), | |
| inputs=[question, level, ground_truth, file_name, openai_api_key, gemini_api_key, anthropic_api_key], | |
| outputs=answer, | |
| cache_examples=False | |
| ) | |
| gr.Examples( | |
| label="GAIA Benchmark Level 3 Problems", | |
| examples=get_questions(QUESTION_FILE_PATH, 3), | |
| inputs=[question, level, ground_truth, file_name, openai_api_key, gemini_api_key, anthropic_api_key], | |
| outputs=answer, | |
| cache_examples=False | |
| ) | |
| with gr.Tab("Documentation"): | |
| gr.Markdown(os.environ.get("DOCUMENTATION")) | |
| grady.launch(mcp_server=True) |