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Update app.py
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app.py
CHANGED
@@ -14,10 +14,22 @@ class BasicAgent:
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def __init__(self):
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print("BasicAgent initialized.")
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def __call__(self, question: str) -> str:
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print(f"
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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@@ -39,8 +51,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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submit_url = f"{api_url}/submit"
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# 1. Instantiate Agent ( modify this part to create your agent)
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except Exception as e:
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print(f"Error instantiating agent: {e}")
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return f"Error initializing agent: {e}", None
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@@ -142,32 +161,23 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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# --- Build Gradio Interface using Blocks ---
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with gr.Blocks() as demo:
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gr.Markdown("#
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gr.Markdown(
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"""
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**Instructions:**
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1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
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2. Log in to your Hugging Face account using the button below. This uses your HF username for submission.
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3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
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---
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**Disclaimers:**
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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).
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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.
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"""
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)
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gr.LoginButton()
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status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
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# Removed max_rows=10 from DataFrame constructor
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results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
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run_button.click(
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fn=run_and_submit_all,
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outputs=[status_output, results_table]
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)
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def __init__(self):
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print("BasicAgent initialized.")
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def __call__(self, question: str) -> str:
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print(f"BasicAgent received: {question[:50]}...")
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return "This is a default answer."
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class EchoAgent:
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def __init__(self):
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print("EchoAgent initialized.")
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def __call__(self, question: str) -> str:
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print(f"EchoAgent received: {question[:50]}...")
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return f"You said: {question}"
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class LengthAgent:
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def __init__(self):
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print("LengthAgent initialized.")
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def __call__(self, question: str) -> str:
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print(f"LengthAgent received: {question[:50]}...")
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return f"Length of input: {len(question)}"
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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submit_url = f"{api_url}/submit"
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# 1. Instantiate Agent ( modify this part to create your agent)
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try:
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if agent_name == "BasicAgent":
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agent = BasicAgent()
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elif agent_name == "EchoAgent":
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agent = EchoAgent()
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elif agent_name == "LengthAgent":
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agent = LengthAgent()
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else:
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raise ValueError(f"Unknown agent selected: {agent_name}")
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except Exception as e:
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print(f"Error instantiating agent: {e}")
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return f"Error initializing agent: {e}", None
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# --- Build Gradio Interface using Blocks ---
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with gr.Blocks() as demo:
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gr.Markdown("# Multi-Agent Evaluation Runner")
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gr.LoginButton()
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agent_dropdown = gr.Dropdown(
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choices=["BasicAgent", "EchoAgent", "LengthAgent"],
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label="Select Agent to Run",
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value="BasicAgent"
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)
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run_button = gr.Button("Run Evaluation & Submit All Answers")
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status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
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results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
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run_button.click(
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fn=run_and_submit_all,
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inputs=[gr.OAuthProfile(), agent_dropdown],
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outputs=[status_output, results_table]
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)
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