MartinHummel commited on
Commit
f9352b3
·
1 Parent(s): 954efbe

modfified application

Browse files
Files changed (2) hide show
  1. app.py +42 -75
  2. tools/__init__.py +8 -0
app.py CHANGED
@@ -3,32 +3,41 @@ import gradio as gr
3
  import requests
4
  import inspect
5
  import pandas as pd
 
 
 
6
 
7
- # (Keep Constants as is)
8
  # --- Constants ---
9
  DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
10
 
11
- # --- Basic Agent Definition ---
12
- # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
13
- class BasicAgent:
14
  def __init__(self):
15
- print("BasicAgent initialized.")
 
 
 
 
 
16
  def __call__(self, question: str) -> str:
17
  print(f"Agent received question (first 50 chars): {question[:50]}...")
18
- fixed_answer = "This is a default answer."
19
- print(f"Agent returning fixed answer: {fixed_answer}")
20
- return fixed_answer
21
-
22
- def run_and_submit_all( profile: gr.OAuthProfile | None):
23
- """
24
- Fetches all questions, runs the BasicAgent on them, submits all answers,
25
- and displays the results.
26
- """
27
- # --- Determine HF Space Runtime URL and Repo URL ---
28
- space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
 
 
29
 
30
  if profile:
31
- username= f"{profile.username}"
32
  print(f"User logged in: {username}")
33
  else:
34
  print("User not logged in.")
@@ -38,38 +47,28 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
38
  questions_url = f"{api_url}/questions"
39
  submit_url = f"{api_url}/submit"
40
 
41
- # 1. Instantiate Agent ( modify this part to create your agent)
42
  try:
43
- agent = BasicAgent()
44
  except Exception as e:
45
  print(f"Error instantiating agent: {e}")
46
  return f"Error initializing agent: {e}", None
47
- # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
48
  agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
49
  print(agent_code)
50
 
51
- # 2. Fetch Questions
52
  print(f"Fetching questions from: {questions_url}")
53
  try:
54
  response = requests.get(questions_url, timeout=15)
55
  response.raise_for_status()
56
  questions_data = response.json()
57
  if not questions_data:
58
- print("Fetched questions list is empty.")
59
- return "Fetched questions list is empty or invalid format.", None
60
  print(f"Fetched {len(questions_data)} questions.")
61
  except requests.exceptions.RequestException as e:
62
  print(f"Error fetching questions: {e}")
63
  return f"Error fetching questions: {e}", None
64
- except requests.exceptions.JSONDecodeError as e:
65
- print(f"Error decoding JSON response from questions endpoint: {e}")
66
- print(f"Response text: {response.text[:500]}")
67
- return f"Error decoding server response for questions: {e}", None
68
- except Exception as e:
69
- print(f"An unexpected error occurred fetching questions: {e}")
70
- return f"An unexpected error occurred fetching questions: {e}", None
71
 
72
- # 3. Run your Agent
73
  results_log = []
74
  answers_payload = []
75
  print(f"Running agent on {len(questions_data)} questions...")
@@ -84,19 +83,17 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
84
  answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
85
  results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
86
  except Exception as e:
87
- print(f"Error running agent on task {task_id}: {e}")
88
- results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
89
 
90
  if not answers_payload:
91
  print("Agent did not produce any answers to submit.")
92
  return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
93
 
94
- # 4. Prepare Submission
95
  submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
96
  status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
97
  print(status_update)
98
 
99
- # 5. Submit
100
  print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
101
  try:
102
  response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -112,49 +109,22 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
112
  print("Submission successful.")
113
  results_df = pd.DataFrame(results_log)
114
  return final_status, results_df
115
- except requests.exceptions.HTTPError as e:
116
- error_detail = f"Server responded with status {e.response.status_code}."
117
- try:
118
- error_json = e.response.json()
119
- error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
120
- except requests.exceptions.JSONDecodeError:
121
- error_detail += f" Response: {e.response.text[:500]}"
122
- status_message = f"Submission Failed: {error_detail}"
123
- print(status_message)
124
- results_df = pd.DataFrame(results_log)
125
- return status_message, results_df
126
- except requests.exceptions.Timeout:
127
- status_message = "Submission Failed: The request timed out."
128
- print(status_message)
129
- results_df = pd.DataFrame(results_log)
130
- return status_message, results_df
131
  except requests.exceptions.RequestException as e:
132
- status_message = f"Submission Failed: Network error - {e}"
133
- print(status_message)
134
- results_df = pd.DataFrame(results_log)
135
- return status_message, results_df
136
- except Exception as e:
137
- status_message = f"An unexpected error occurred during submission: {e}"
138
  print(status_message)
139
  results_df = pd.DataFrame(results_log)
140
  return status_message, results_df
141
 
142
-
143
- # --- Build Gradio Interface using Blocks ---
144
  with gr.Blocks() as demo:
145
- gr.Markdown("# Basic Agent Evaluation Runner")
146
  gr.Markdown(
147
  """
148
  **Instructions:**
149
 
150
- 1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
151
- 2. Log in to your Hugging Face account using the button below. This uses your HF username for submission.
152
- 3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
153
-
154
- ---
155
- **Disclaimers:**
156
- 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).
157
- 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.
158
  """
159
  )
160
 
@@ -163,7 +133,6 @@ with gr.Blocks() as demo:
163
  run_button = gr.Button("Run Evaluation & Submit All Answers")
164
 
165
  status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
166
- # Removed max_rows=10 from DataFrame constructor
167
  results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
168
 
169
  run_button.click(
@@ -173,9 +142,8 @@ with gr.Blocks() as demo:
173
 
174
  if __name__ == "__main__":
175
  print("\n" + "-"*30 + " App Starting " + "-"*30)
176
- # Check for SPACE_HOST and SPACE_ID at startup for information
177
  space_host_startup = os.getenv("SPACE_HOST")
178
- space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
179
 
180
  if space_host_startup:
181
  print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -183,7 +151,7 @@ if __name__ == "__main__":
183
  else:
184
  print("ℹ️ SPACE_HOST environment variable not found (running locally?).")
185
 
186
- if space_id_startup: # Print repo URLs if SPACE_ID is found
187
  print(f"✅ SPACE_ID found: {space_id_startup}")
188
  print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
189
  print(f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
@@ -191,6 +159,5 @@ if __name__ == "__main__":
191
  print("ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
192
 
193
  print("-"*(60 + len(" App Starting ")) + "\n")
194
-
195
- print("Launching Gradio Interface for Basic Agent Evaluation...")
196
- demo.launch(debug=True, share=False)
 
3
  import requests
4
  import inspect
5
  import pandas as pd
6
+ from huggingface_hub import notebook_login
7
+ from transformers import Tool
8
+ from tools.tool_math import SolveEquationTool, ExplainSolutionTool
9
 
 
10
  # --- Constants ---
11
  DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
12
 
13
+ # --- Math Solver Agent Definition ---
14
+ class MathSolverAgent:
 
15
  def __init__(self):
16
+ self.tools = [
17
+ SolveEquationTool(),
18
+ ExplainSolutionTool(),
19
+ ]
20
+ print("MathSolverAgent initialized with tools.")
21
+
22
  def __call__(self, question: str) -> str:
23
  print(f"Agent received question (first 50 chars): {question[:50]}...")
24
+ for tool in self.tools:
25
+ if tool.name in question.lower():
26
+ return tool(question)
27
+
28
+ # Fallback if no tool matched
29
+ try:
30
+ solution = self.tools[0](question) # Try solving equation
31
+ explanation = self.tools[1](question) # Try explaining
32
+ return f"{solution}\nExplanation:\n{explanation}"
33
+ except Exception as e:
34
+ return f"Could not solve the problem: {str(e)}"
35
+
36
+ def run_and_submit_all(profile: gr.OAuthProfile | None):
37
+ space_id = os.getenv("SPACE_ID")
38
 
39
  if profile:
40
+ username = f"{profile.username}"
41
  print(f"User logged in: {username}")
42
  else:
43
  print("User not logged in.")
 
47
  questions_url = f"{api_url}/questions"
48
  submit_url = f"{api_url}/submit"
49
 
 
50
  try:
51
+ agent = MathSolverAgent()
52
  except Exception as e:
53
  print(f"Error instantiating agent: {e}")
54
  return f"Error initializing agent: {e}", None
55
+
56
  agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
57
  print(agent_code)
58
 
 
59
  print(f"Fetching questions from: {questions_url}")
60
  try:
61
  response = requests.get(questions_url, timeout=15)
62
  response.raise_for_status()
63
  questions_data = response.json()
64
  if not questions_data:
65
+ print("Fetched questions list is empty.")
66
+ return "Fetched questions list is empty or invalid format.", None
67
  print(f"Fetched {len(questions_data)} questions.")
68
  except requests.exceptions.RequestException as e:
69
  print(f"Error fetching questions: {e}")
70
  return f"Error fetching questions: {e}", None
 
 
 
 
 
 
 
71
 
 
72
  results_log = []
73
  answers_payload = []
74
  print(f"Running agent on {len(questions_data)} questions...")
 
83
  answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
84
  results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
85
  except Exception as e:
86
+ print(f"Error running agent on task {task_id}: {e}")
87
+ results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
88
 
89
  if not answers_payload:
90
  print("Agent did not produce any answers to submit.")
91
  return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
92
 
 
93
  submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
94
  status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
95
  print(status_update)
96
 
 
97
  print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
98
  try:
99
  response = requests.post(submit_url, json=submission_data, timeout=60)
 
109
  print("Submission successful.")
110
  results_df = pd.DataFrame(results_log)
111
  return final_status, results_df
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
112
  except requests.exceptions.RequestException as e:
113
+ status_message = f"Submission Failed: {e}"
 
 
 
 
 
114
  print(status_message)
115
  results_df = pd.DataFrame(results_log)
116
  return status_message, results_df
117
 
118
+ # --- Gradio Interface ---
 
119
  with gr.Blocks() as demo:
120
+ gr.Markdown("# Math Solver and Explainer Agent")
121
  gr.Markdown(
122
  """
123
  **Instructions:**
124
 
125
+ 1. Modify this agent to use symbolic math tools and explanations.
126
+ 2. Log in to your Hugging Face account.
127
+ 3. Run the agent and submit all answers for scoring.
 
 
 
 
 
128
  """
129
  )
130
 
 
133
  run_button = gr.Button("Run Evaluation & Submit All Answers")
134
 
135
  status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
 
136
  results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
137
 
138
  run_button.click(
 
142
 
143
  if __name__ == "__main__":
144
  print("\n" + "-"*30 + " App Starting " + "-"*30)
 
145
  space_host_startup = os.getenv("SPACE_HOST")
146
+ space_id_startup = os.getenv("SPACE_ID")
147
 
148
  if space_host_startup:
149
  print(f"✅ SPACE_HOST found: {space_host_startup}")
 
151
  else:
152
  print("ℹ️ SPACE_HOST environment variable not found (running locally?).")
153
 
154
+ if space_id_startup:
155
  print(f"✅ SPACE_ID found: {space_id_startup}")
156
  print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
157
  print(f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
 
159
  print("ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
160
 
161
  print("-"*(60 + len(" App Starting ")) + "\n")
162
+ print("Launching Gradio Interface for Math Solver Agent...")
163
+ demo.launch(debug=True, share=False)
 
tools/__init__.py ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ # tools/__init__.py
2
+
3
+ from .tool_math import SolveEquationTool, ExplainSolutionTool
4
+
5
+ __all__ = [
6
+ "SolveEquationTool",
7
+ "ExplainSolutionTool",
8
+ ]