Update app.py

app.py

@@ -94,272 +94,3 @@ def run_and_submit_one():
 
 run_and_submit_one()       
 
-
-#Client setup
-#token = HF_TOKEN 
-#model_repo_id = chat_completion eller question_answering
-
-# llm = LLMFunction.from_huggingface_inference_api(
-#     repo_id="google/flan-t5-base",  # 
-#     token="HF_TOKEN "
-# )
-
-# agent = CodeAgent(llm=llm)
-# response = agent("Translate 'How are you?' to German.")
-# print(response)
-
-
-
-
-#----
-# client = InferenceClient(
-#     provider="hf-inference",
-#     api_key=os.environ["HF_TOKEN"],
-# )
-
-# completion = client.chat.completions.create(
-#     model="tiiuae/falcon-rw-1b",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": "What is the capital of France?"
-#         }
-#     ],
-# )
-
-# completion = client.chat.completions.create(
-#     model="sarvamai/sarvam-m",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": "What is the capital of France?"
-#         }
-#     ],
-# )
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-
-#Tools
-
-
-#Model
-model = InferenceClientModel() #Default
-
-#Agent
-code_agent = CodeAgent(tools=[], model=model)
-#code_agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)
-
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-
-class BasicCodeAgent:
-    def __init__(self):
-        model = InferenceClientModel() #Cause of 
-        
-        # model = HfApiModel(
-        # max_tokens=2096,
-        # temperature=0.5,
-        # model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-        # custom_role_conversions=None,
-        # )
-        
-        self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)
-        print("BasicCodeAgent initialized.")
-        
-    def __call__(self, question: str) -> str:
-        print(f"BasicCodeAgent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        #answer = self.agent.run(question)
-        print(f"BasicCodeAgent returning fixed answer: {fixed_answer}")
-        return answer
-
-# def run_and_submit_all( profile: gr.OAuthProfile | None):
-#     """
-#     Fetches all questions, runs the BasicAgent on them, submits all answers,
-#     and displays the results.
-#     """
-#     # --- Determine HF Space Runtime URL and Repo URL ---
-#     space_id = os.getenv("/Synnove/Final_Assignment_Template") # Get the SPACE_ID for sending link to the code
-
-#     if profile:
-#         username= f"{profile.username}"
-#         print(f"User logged in: {username}")
-#     else:
-#         print("User not logged in.")
-#         return "Please Login to Hugging Face with the button.", None
-
-#     api_url = "https://agents-course-unit4-scoring.hf.space"
-#     questions_url = f"{api_url}/questions"
-#     submit_url = f"{api_url}/submit"
-
-#     # 1. Instantiate Agent ( modify this part to create your agent)
-#     try:
-#         #agent = BasicAgent()
-#         agent = BasicCodeAgent()
-        
-#     except Exception as e:
-#         print(f"Error instantiating agent: {e}")
-#         return f"Error initializing agent: {e}", None
-#     # 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)
-#     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-#     print(agent_code)
-
-#     # 2. Fetch Questions
-#     print(f"Fetching questions from: {questions_url}")
-#     try:
-#         response = requests.get(questions_url, timeout=15)
-#         response.raise_for_status()
-#         questions_data = response.json()
-#         if not questions_data:
-#              print("Fetched questions list is empty.")
-#              return "Fetched questions list is empty or invalid format.", None
-#         print(f"Fetched {len(questions_data)} questions.")
-#     except requests.exceptions.RequestException as e:
-#         print(f"Error fetching questions: {e}")
-#         return f"Error fetching questions: {e}", None
-#     except requests.exceptions.JSONDecodeError as e:
-#          print(f"Error decoding JSON response from questions endpoint: {e}")
-#          print(f"Response text: {response.text[:500]}")
-#          return f"Error decoding server response for questions: {e}", None
-#     except Exception as e:
-#         print(f"An unexpected error occurred fetching questions: {e}")
-#         return f"An unexpected error occurred fetching questions: {e}", None
-
-#     # 3. Run your Agent
-#     results_log = []
-#     answers_payload = []
-#     print(f"Running agent on {len(questions_data)} questions...")
-#     print(questions_data)
-#     for item in questions_data:
-#         task_id = item.get("task_id")
-#         question_text = item.get("question")
-#         if not task_id or question_text is None:
-#             print(f"Skipping item with missing task_id or question: {item}")
-#             continue
-#         try:
-#             submitted_answer = agent(question_text)
-#             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-#             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-#         except Exception as e:
-#              print(f"Error running agent on task {task_id}: {e}")
-#              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-#     if not answers_payload:
-#         print("Agent did not produce any answers to submit.")
-#         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-#     # 4. Prepare Submission 
-#     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-#     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-#     print(status_update)
-
-#     # 5. Submit
-#     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-#     try:
-#         response = requests.post(submit_url, json=submission_data, timeout=60)
-#         response.raise_for_status()
-#         result_data = response.json()
-#         final_status = (
-#             f"Submission Successful!\n"
-#             f"User: {result_data.get('username')}\n"
-#             f"Overall Score: {result_data.get('score', 'N/A')}% "
-#             f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-#             f"Message: {result_data.get('message', 'No message received.')}"
-#         )
-#         print("Submission successful.")
-#         results_df = pd.DataFrame(results_log)
-#         return final_status, results_df
-#     except requests.exceptions.HTTPError as e:
-#         error_detail = f"Server responded with status {e.response.status_code}."
-#         try:
-#             error_json = e.response.json()
-#             error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-#         except requests.exceptions.JSONDecodeError:
-#             error_detail += f" Response: {e.response.text[:500]}"
-#         status_message = f"Submission Failed: {error_detail}"
-#         print(status_message)
-#         results_df = pd.DataFrame(results_log)
-#         return status_message, results_df
-#     except requests.exceptions.Timeout:
-#         status_message = "Submission Failed: The request timed out."
-#         print(status_message)
-#         results_df = pd.DataFrame(results_log)
-#         return status_message, results_df
-#     except requests.exceptions.RequestException as e:
-#         status_message = f"Submission Failed: Network error - {e}"
-#         print(status_message)
-#         results_df = pd.DataFrame(results_log)
-#         return status_message, results_df
-#     except run_and_submit_allxception as e:
-#         status_message = f"An unexpected error occurred during submission: {e}"
-#         print(status_message)
-#         results_df = pd.DataFrame(results_log)
-#         return status_message, results_df
-
-
-# # --- Build Gradio Interface using Blocks ---
-# with gr.Blocks() as demo:
-#     gr.Markdown("# Basic Agent Evaluation Runner")
-#     gr.Markdown(
-#         """
-#         **Instructions:**
-
-#         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-#         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-#         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-#         ---
-#         **Disclaimers:**
-#         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).
-#         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.
-#         """
-#     )
-
-#     gr.LoginButton()
-
-#     run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-#     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-#     # Removed max_rows=10 from DataFrame constructor
-#     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-#     run_button.click(
-#         fn=run_and_submit_all,
-#         outputs=[status_output, results_table]
-#     )
-
-# if __name__ == "__main__":
-#     print("\n" + "-"*30 + " App Starting " + "-"*30)
-#     # Check for SPACE_HOST and SPACE_ID at startup for information
-#     space_host_startup = os.getenv("SPACE_HOST")
-#     space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-#     if space_host_startup:
-#         print(f"✅ SPACE_HOST found: {space_host_startup}")
-#         print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-#     else:
-#         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-#     if space_id_startup: # Print repo URLs if SPACE_ID is found
-#         print(f"✅ SPACE_ID found: {space_id_startup}")
-#         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-#         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-#     else:
-#         print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-#     print("-"*(60 + len(" App Starting ")) + "\n")
-
-#     print("Launching Gradio Interface for Basic Agent Evaluation...")
-#     demo.launch(debug=True, share=False)