Update app.py

app.py

@@ -12,7 +12,6 @@ from agent import build_graph
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-#DEFAULT_API_URL= "https://neda1-agent-final.hf.space.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
@@ -26,11 +25,11 @@ class BasicAgent:
 
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # Wrap the question in a HumanMessage from langchain_core
         messages = [HumanMessage(content=question)]
-        messages = self.graph.invoke({"messages": messages})
-        answer = messages['messages'][-1].content
-        return answer[14:]
+        result = self.graph.invoke({"messages": messages})
+        answer = result['messages'][-1].content
+        return answer  # kein [14:] mehr nötig!
+
 
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
@@ -39,7 +38,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID", "https://huggingface.co/spaces/Neda1/agent_final")# Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
         username= f"{profile.username}"
@@ -188,7 +187,6 @@ if __name__ == "__main__":
     # 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}")
@@ -206,47 +204,257 @@ if __name__ == "__main__":
     print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    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.
-            """
-        )
+    demo.launch(debug=True, share=False)
+
+# """ Basic Agent Evaluation Runner"""
+# import os
+# import inspect
+# import gradio as gr
+# import requests
+# import pandas as pd
+# from langchain_core.messages import HumanMessage
+# from agent import build_graph
+
+
+
+# # (Keep Constants as is)
+# # --- Constants ---
+# DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+# #DEFAULT_API_URL= "https://neda1-agent-final.hf.space.hf.space"
+
+# # --- Basic Agent Definition ---
+# # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
+# class BasicAgent:
+#     """A langgraph agent."""
+#     def __init__(self):
+#         print("BasicAgent initialized.")
+#         self.graph = build_graph()
+
+#     def __call__(self, question: str) -> str:
+#         print(f"Agent received question (first 50 chars): {question[:50]}...")
+#         # Wrap the question in a HumanMessage from langchain_core
+#         messages = [HumanMessage(content=question)]
+#         messages = self.graph.invoke({"messages": messages})
+#         answer = messages['messages'][-1].content
+#         return answer[14:]
+
+
+# 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("SPACE_ID", "https://huggingface.co/spaces/Neda1/agent_final")# 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 = DEFAULT_API_URL
+#     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()
+#     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...")
+#     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 Exception 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...")
+#     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()
+#         gr.LoginButton()
     
-        run_button = gr.Button("Run Evaluation & Submit All Answers")
+#         run_button = gr.Button("Run Evaluation & Submit All Answers")
     
-        status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-        results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+#         status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+#         results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
     
-        run_button.click(
-            fn=run_and_submit_all,
-            outputs=[status_output, results_table]
-        )
+#         run_button.click(
+#             fn=run_and_submit_all,
+#             outputs=[status_output, results_table]
+#         )
     
-        # ✅ Manual Test Interface (Put inside Blocks!)
-        gr.Markdown("## Ask your Agent Any Question (Manual Test)")
-        free_question = gr.Textbox(label="Ask your own question")
-        free_response = gr.Textbox(label="Agent's Response", interactive=False)
+#         # ✅ Manual Test Interface (Put inside Blocks!)
+#         gr.Markdown("## Ask your Agent Any Question (Manual Test)")
+#         free_question = gr.Textbox(label="Ask your own question")
+#         free_response = gr.Textbox(label="Agent's Response", interactive=False)
     
-        test_agent = BasicAgent()
+#         test_agent = BasicAgent()
     
-        def run_custom_query(q):
-            return test_agent(q)
+#         def run_custom_query(q):
+#             return test_agent(q)
 
-    free_question.submit(fn=run_custom_query, inputs=free_question, outputs=free_response)
+#     free_question.submit(fn=run_custom_query, inputs=free_question, outputs=free_response)
 
 
 
 
     
-    demo.launch(debug=True, share=False)
+#     demo.launch(debug=True, share=False)