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f9352b3
1
Parent(s):
954efbe
modfified application
Browse files- app.py +42 -75
- tools/__init__.py +8 -0
app.py
CHANGED
@@ -3,32 +3,41 @@ import gradio as gr
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import requests
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import inspect
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import pandas as pd
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# (Keep Constants as is)
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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# ---
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class BasicAgent:
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def __init__(self):
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def __call__(self, question: str) -> str:
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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if profile:
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username= f"{profile.username}"
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print(f"User logged in: {username}")
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else:
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print("User not logged in.")
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@@ -38,38 +47,28 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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questions_url = f"{api_url}/questions"
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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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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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agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
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print(agent_code)
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# 2. Fetch Questions
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print(f"Fetching questions from: {questions_url}")
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try:
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response = requests.get(questions_url, timeout=15)
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response.raise_for_status()
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questions_data = response.json()
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if not questions_data:
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print(f"Fetched {len(questions_data)} questions.")
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except requests.exceptions.RequestException as e:
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print(f"Error fetching questions: {e}")
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return f"Error fetching questions: {e}", None
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except requests.exceptions.JSONDecodeError as e:
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print(f"Error decoding JSON response from questions endpoint: {e}")
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print(f"Response text: {response.text[:500]}")
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return f"Error decoding server response for questions: {e}", None
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except Exception as e:
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print(f"An unexpected error occurred fetching questions: {e}")
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return f"An unexpected error occurred fetching questions: {e}", None
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# 3. Run your Agent
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results_log = []
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answers_payload = []
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print(f"Running agent on {len(questions_data)} questions...")
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@@ -84,19 +83,17 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
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except Exception as e:
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if not answers_payload:
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print("Agent did not produce any answers to submit.")
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return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
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# 4. Prepare Submission
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submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
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status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
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print(status_update)
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# 5. Submit
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print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
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try:
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response = requests.post(submit_url, json=submission_data, timeout=60)
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@@ -112,49 +109,22 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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print("Submission successful.")
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results_df = pd.DataFrame(results_log)
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return final_status, results_df
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except requests.exceptions.HTTPError as e:
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error_detail = f"Server responded with status {e.response.status_code}."
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try:
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error_json = e.response.json()
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error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
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except requests.exceptions.JSONDecodeError:
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error_detail += f" Response: {e.response.text[:500]}"
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status_message = f"Submission Failed: {error_detail}"
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print(status_message)
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results_df = pd.DataFrame(results_log)
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return status_message, results_df
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except requests.exceptions.Timeout:
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status_message = "Submission Failed: The request timed out."
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print(status_message)
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results_df = pd.DataFrame(results_log)
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return status_message, results_df
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except requests.exceptions.RequestException as e:
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status_message = f"Submission Failed:
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print(status_message)
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results_df = pd.DataFrame(results_log)
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return status_message, results_df
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except Exception as e:
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status_message = f"An unexpected error occurred during submission: {e}"
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print(status_message)
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results_df = pd.DataFrame(results_log)
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return status_message, results_df
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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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2. Log in to your Hugging Face account
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3.
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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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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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# 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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if __name__ == "__main__":
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print("\n" + "-"*30 + " App Starting " + "-"*30)
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# Check for SPACE_HOST and SPACE_ID at startup for information
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space_host_startup = os.getenv("SPACE_HOST")
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space_id_startup = os.getenv("SPACE_ID")
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if space_host_startup:
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print(f"✅ SPACE_HOST found: {space_host_startup}")
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else:
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print("ℹ️ SPACE_HOST environment variable not found (running locally?).")
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if space_id_startup:
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print(f"✅ SPACE_ID found: {space_id_startup}")
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print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
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print(f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
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print("ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
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print("-"*(60 + len(" App Starting ")) + "\n")
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demo.launch(debug=True, share=False)
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import requests
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import inspect
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import pandas as pd
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from huggingface_hub import notebook_login
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from transformers import Tool
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from tools.tool_math import SolveEquationTool, ExplainSolutionTool
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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# --- Math Solver Agent Definition ---
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class MathSolverAgent:
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def __init__(self):
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self.tools = [
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SolveEquationTool(),
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ExplainSolutionTool(),
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]
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print("MathSolverAgent initialized with tools.")
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def __call__(self, question: str) -> str:
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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for tool in self.tools:
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if tool.name in question.lower():
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return tool(question)
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# Fallback if no tool matched
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try:
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solution = self.tools[0](question) # Try solving equation
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explanation = self.tools[1](question) # Try explaining
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return f"{solution}\nExplanation:\n{explanation}"
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except Exception as e:
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return f"Could not solve the problem: {str(e)}"
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def run_and_submit_all(profile: gr.OAuthProfile | None):
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space_id = os.getenv("SPACE_ID")
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if profile:
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username = f"{profile.username}"
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print(f"User logged in: {username}")
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else:
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print("User not logged in.")
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questions_url = f"{api_url}/questions"
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submit_url = f"{api_url}/submit"
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try:
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agent = MathSolverAgent()
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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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agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
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print(agent_code)
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print(f"Fetching questions from: {questions_url}")
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try:
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response = requests.get(questions_url, timeout=15)
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response.raise_for_status()
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questions_data = response.json()
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if not questions_data:
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print("Fetched questions list is empty.")
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return "Fetched questions list is empty or invalid format.", None
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print(f"Fetched {len(questions_data)} questions.")
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except requests.exceptions.RequestException as e:
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print(f"Error fetching questions: {e}")
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return f"Error fetching questions: {e}", None
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results_log = []
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answers_payload = []
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print(f"Running agent on {len(questions_data)} questions...")
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answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
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except Exception as e:
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print(f"Error running agent on task {task_id}: {e}")
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
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if not answers_payload:
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print("Agent did not produce any answers to submit.")
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return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
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submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
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status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
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print(status_update)
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print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
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try:
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response = requests.post(submit_url, json=submission_data, timeout=60)
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print("Submission successful.")
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results_df = pd.DataFrame(results_log)
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return final_status, results_df
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except requests.exceptions.RequestException as e:
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status_message = f"Submission Failed: {e}"
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print(status_message)
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results_df = pd.DataFrame(results_log)
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return status_message, results_df
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# --- Gradio Interface ---
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with gr.Blocks() as demo:
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gr.Markdown("# Math Solver and Explainer Agent")
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gr.Markdown(
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"""
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**Instructions:**
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1. Modify this agent to use symbolic math tools and explanations.
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2. Log in to your Hugging Face account.
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3. Run the agent and submit all answers for scoring.
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"""
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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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if __name__ == "__main__":
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print("\n" + "-"*30 + " App Starting " + "-"*30)
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space_host_startup = os.getenv("SPACE_HOST")
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space_id_startup = os.getenv("SPACE_ID")
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if space_host_startup:
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print(f"✅ SPACE_HOST found: {space_host_startup}")
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else:
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print("ℹ️ SPACE_HOST environment variable not found (running locally?).")
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if space_id_startup:
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print(f"✅ SPACE_ID found: {space_id_startup}")
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print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
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print(f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
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print("ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
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print("-"*(60 + len(" App Starting ")) + "\n")
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print("Launching Gradio Interface for Math Solver Agent...")
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demo.launch(debug=True, share=False)
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tools/__init__.py
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# tools/__init__.py
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from .tool_math import SolveEquationTool, ExplainSolutionTool
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__all__ = [
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"SolveEquationTool",
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"ExplainSolutionTool",
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]
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