Ragulvasanth66 commited on
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  1. app.py +103 -128
app.py CHANGED
@@ -1,196 +1,171 @@
 
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.")
35
- return "Please Login to Hugging Face with the button.", None
36
 
37
  api_url = DEFAULT_API_URL
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...")
 
76
  for item in questions_data:
77
  task_id = item.get("task_id")
78
  question_text = item.get("question")
79
  if not task_id or question_text is None:
80
- print(f"Skipping item with missing task_id or question: {item}")
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
 
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)
103
  response.raise_for_status()
104
  result_data = response.json()
 
105
  final_status = (
106
- f"Submission Successful!\n"
107
- f"User: {result_data.get('username')}\n"
108
- f"Overall Score: {result_data.get('score', 'N/A')}% "
109
- f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
110
- f"Message: {result_data.get('message', 'No message received.')}"
111
  )
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
-
161
  gr.LoginButton()
 
 
 
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"βœ… SPACE_HOST found: {space_host_startup}")
182
- print(f" Runtime URL should be: https://{space_host_startup}.hf.space")
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"βœ… 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
+ # --- Standard Library Imports ---
2
  import os
 
3
  import requests
 
4
  import pandas as pd
5
+ import gradio as gr
6
+ from typing import Union
7
 
 
8
  # --- Constants ---
9
  DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
10
 
11
  # --- Basic Agent Definition ---
12
+ # You should modify this class to improve agent behavior.
13
  class BasicAgent:
14
  def __init__(self):
15
+ print("βœ… BasicAgent initialized.")
16
+
17
  def __call__(self, question: str) -> str:
18
+ # Print the incoming question (preview)
19
+ print(f"πŸ€– Agent received question: {question[:50]}...")
20
+
21
+ # Your logic goes here β€” modify as needed.
22
+ fixed_answer = "This is a default answer." # You can replace this with dynamic generation.
23
+
24
+ print(f"πŸ“€ Agent returns: {fixed_answer}")
25
  return fixed_answer
26
 
27
+
28
+ # --- Core Evaluation Logic ---
29
+ def run_and_submit_all(profile: Union[gr.OAuthProfile, None]):
30
  """
31
+ Core function that:
32
+ - Initializes the agent
33
+ - Fetches questions
34
+ - Generates answers
35
+ - Submits them to the scoring API
36
+ - Returns the final result and answers DataFrame
37
  """
38
+ space_id = os.getenv("SPACE_ID") # Optional but used to link to the repo
 
39
 
40
  if profile:
41
+ username = profile.username
42
+ print(f"πŸ‘€ Logged in user: {username}")
43
  else:
44
+ print("⚠️ User not logged in.")
45
+ return "Please login using Hugging Face Login button.", None
46
 
47
  api_url = DEFAULT_API_URL
48
  questions_url = f"{api_url}/questions"
49
  submit_url = f"{api_url}/submit"
50
+ agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Not available"
51
 
52
+ # 1. Instantiate the agent
53
  try:
54
  agent = BasicAgent()
55
  except Exception as e:
56
+ return f"❌ Error initializing agent: {e}", None
57
+
58
+ # 2. Fetch questions
59
+ print(f"πŸ“₯ 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
+ return "❌ Fetched questions list is empty or invalid.", None
66
+ print(f"βœ… {len(questions_data)} questions fetched.")
 
 
 
 
 
 
 
 
67
  except Exception as e:
68
+ return f"❌ Error fetching questions: {e}", None
 
69
 
70
+ # 3. Run the agent on all questions
71
  results_log = []
72
  answers_payload = []
73
+
74
+ print("🧠 Running agent on questions...")
75
  for item in questions_data:
76
  task_id = item.get("task_id")
77
  question_text = item.get("question")
78
  if not task_id or question_text is None:
 
79
  continue
80
  try:
81
  submitted_answer = agent(question_text)
82
  answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
83
+ results_log.append({
84
+ "Task ID": task_id,
85
+ "Question": question_text,
86
+ "Submitted Answer": submitted_answer
87
+ })
88
  except Exception as e:
89
+ results_log.append({
90
+ "Task ID": task_id,
91
+ "Question": question_text,
92
+ "Submitted Answer": f"AGENT ERROR: {e}"
93
+ })
94
 
95
  if not answers_payload:
96
+ return "❌ Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
97
 
98
+ # 4. Prepare Submission
99
+ submission_data = {
100
+ "username": username,
101
+ "agent_code": agent_code,
102
+ "answers": answers_payload
103
+ }
104
+ print(f"πŸš€ Submitting {len(answers_payload)} answers...")
105
 
106
+ # 5. Submit answers to scoring endpoint
 
107
  try:
108
  response = requests.post(submit_url, json=submission_data, timeout=60)
109
  response.raise_for_status()
110
  result_data = response.json()
111
+
112
  final_status = (
113
+ f"βœ… Submission Successful!\n"
114
+ f"πŸ‘€ User: {result_data.get('username')}\n"
115
+ f"πŸ“Š Score: {result_data.get('score')}% "
116
+ f"({result_data.get('correct_count')}/{result_data.get('total_attempted')} correct)\n"
117
+ f"πŸ“ Message: {result_data.get('message', 'No message.')}"
118
  )
119
+ return final_status, pd.DataFrame(results_log)
120
+
 
121
  except requests.exceptions.HTTPError as e:
122
+ return f"❌ Submission Failed (HTTP error): {e}", pd.DataFrame(results_log)
 
 
 
 
 
 
 
 
 
123
  except requests.exceptions.Timeout:
124
+ return "❌ Submission Failed: Request timed out.", pd.DataFrame(results_log)
 
 
 
 
 
 
 
 
125
  except Exception as e:
126
+ return f"❌ Submission Failed: {e}", pd.DataFrame(results_log)
 
 
 
127
 
128
 
129
+ # --- Gradio UI Setup ---
130
  with gr.Blocks() as demo:
131
+ gr.Markdown("# πŸ€– Basic Agent Evaluation Tool")
132
+ gr.Markdown("""
133
+ ### πŸ›  Instructions:
134
+ 1. Clone this Hugging Face Space.
135
+ 2. Implement your own logic in the `BasicAgent` class.
136
+ 3. Login with your Hugging Face account.
137
+ 4. Press the button to run all questions through your agent and submit.
138
+
139
+ **Note:** It may take some time depending on the number of questions and agent logic.
140
+ """)
141
+
142
+ # Login and button interface
 
 
 
 
143
  gr.LoginButton()
144
+ run_button = gr.Button("▢️ Run Evaluation & Submit All Answers")
145
+ status_output = gr.Textbox(label="πŸ“ Submission Status", lines=5, interactive=False)
146
+ results_table = gr.DataFrame(label="πŸ“„ Agent Answers Log", wrap=True)
147
 
148
+ # Hook button click to function
149
+ run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 
 
 
 
 
 
 
150
 
151
+ # --- Local App Runner ---
152
  if __name__ == "__main__":
153
+ print("\n" + "-" * 30 + " πŸš€ App Starting " + "-" * 30)
154
+ space_host = os.getenv("SPACE_HOST")
155
+ space_id = os.getenv("SPACE_ID")
 
 
 
 
 
 
 
156
 
157
+ if space_host:
158
+ print(f"βœ… SPACE_HOST: {space_host}")
159
+ print(f"🌍 App URL: https://{space_host}.hf.space")
 
160
  else:
161
+ print("ℹ️ SPACE_HOST not found (running locally?)")
162
 
163
+ if space_id:
164
+ print(f"βœ… SPACE_ID: {space_id}")
165
+ print(f"πŸ“¦ Repo: https://huggingface.co/spaces/{space_id}")
166
+ else:
167
+ print("ℹ️ SPACE_ID not set")
168
 
169
+ print("-" * 60)
170
+ print("πŸ”§ Launching Gradio app...")
171
+ demo.launch(debug=True, share=False)