mirjam-m commited on
Commit
2db8573
·
1 Parent(s): 81917a3
Files changed (1) hide show
  1. app.py +54 -28
app.py CHANGED
@@ -1,34 +1,39 @@
 
1
  import os
 
2
  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.")
@@ -55,16 +60,16 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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
@@ -81,18 +86,36 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
81
  continue
82
  try:
83
  submitted_answer = agent(question_text)
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
 
@@ -162,20 +185,19 @@ with gr.Blocks() as demo:
162
 
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(
170
- fn=run_and_submit_all,
171
- outputs=[status_output, results_table]
172
- )
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}")
@@ -183,14 +205,18 @@ 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")
 
 
190
  else:
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)
 
1
+ import inspect
2
  import os
3
+
4
  import gradio as gr
 
 
5
  import pandas as pd
6
+ import requests
7
+
8
 
9
  # (Keep Constants as is)
10
  # --- Constants ---
11
  DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
12
 
13
+
14
  # --- Basic Agent Definition ---
15
  # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
16
  class BasicAgent:
17
  def __init__(self):
18
  print("BasicAgent initialized.")
19
+
20
  def __call__(self, question: str) -> str:
21
  print(f"Agent received question (first 50 chars): {question[:50]}...")
22
  fixed_answer = "This is a default answer."
23
  print(f"Agent returning fixed answer: {fixed_answer}")
24
  return fixed_answer
25
 
26
+
27
+ def run_and_submit_all(profile: gr.OAuthProfile | None):
28
  """
29
  Fetches all questions, runs the BasicAgent on them, submits all answers,
30
  and displays the results.
31
  """
32
  # --- Determine HF Space Runtime URL and Repo URL ---
33
+ space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
34
 
35
  if profile:
36
+ username = f"{profile.username}"
37
  print(f"User logged in: {username}")
38
  else:
39
  print("User not logged in.")
 
60
  response.raise_for_status()
61
  questions_data = response.json()
62
  if not questions_data:
63
+ print("Fetched questions list is empty.")
64
+ return "Fetched questions list is empty or invalid format.", None
65
  print(f"Fetched {len(questions_data)} questions.")
66
  except requests.exceptions.RequestException as e:
67
  print(f"Error fetching questions: {e}")
68
  return f"Error fetching questions: {e}", None
69
  except requests.exceptions.JSONDecodeError as e:
70
+ print(f"Error decoding JSON response from questions endpoint: {e}")
71
+ print(f"Response text: {response.text[:500]}")
72
+ return f"Error decoding server response for questions: {e}", None
73
  except Exception as e:
74
  print(f"An unexpected error occurred fetching questions: {e}")
75
  return f"An unexpected error occurred fetching questions: {e}", None
 
86
  continue
87
  try:
88
  submitted_answer = agent(question_text)
89
+ answers_payload.append(
90
+ {"task_id": task_id, "submitted_answer": submitted_answer}
91
+ )
92
+ results_log.append(
93
+ {
94
+ "Task ID": task_id,
95
+ "Question": question_text,
96
+ "Submitted Answer": submitted_answer,
97
+ }
98
+ )
99
  except Exception as e:
100
+ print(f"Error running agent on task {task_id}: {e}")
101
+ results_log.append(
102
+ {
103
+ "Task ID": task_id,
104
+ "Question": question_text,
105
+ "Submitted Answer": f"AGENT ERROR: {e}",
106
+ }
107
+ )
108
 
109
  if not answers_payload:
110
  print("Agent did not produce any answers to submit.")
111
  return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
112
 
113
+ # 4. Prepare Submission
114
+ submission_data = {
115
+ "username": username.strip(),
116
+ "agent_code": agent_code,
117
+ "answers": answers_payload,
118
+ }
119
  status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
120
  print(status_update)
121
 
 
185
 
186
  run_button = gr.Button("Run Evaluation & Submit All Answers")
187
 
188
+ status_output = gr.Textbox(
189
+ label="Run Status / Submission Result", lines=5, interactive=False
190
+ )
191
  # Removed max_rows=10 from DataFrame constructor
192
  results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
193
 
194
+ run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 
 
195
 
196
  if __name__ == "__main__":
197
+ print("\n" + "-" * 30 + " App Starting " + "-" * 30)
198
  # Check for SPACE_HOST and SPACE_ID at startup for information
199
  space_host_startup = os.getenv("SPACE_HOST")
200
+ space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
201
 
202
  if space_host_startup:
203
  print(f"✅ SPACE_HOST found: {space_host_startup}")
 
205
  else:
206
  print("ℹ️ SPACE_HOST environment variable not found (running locally?).")
207
 
208
+ if space_id_startup: # Print repo URLs if SPACE_ID is found
209
  print(f"✅ SPACE_ID found: {space_id_startup}")
210
  print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
211
+ print(
212
+ f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main"
213
+ )
214
  else:
215
+ print(
216
+ "ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."
217
+ )
218
 
219
+ print("-" * (60 + len(" App Starting ")) + "\n")
220
 
221
  print("Launching Gradio Interface for Basic Agent Evaluation...")
222
+ demo.launch(debug=True, share=False)