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app.py

@@ -1,6 +1,5 @@
 import os
-
-# import gradio as gr
+import gradio as gr
 import requests
 import inspect
 import pandas as pd
@@ -79,8 +78,7 @@ class BasicAgent:
         return final_answer
 
 
-# async def run_and_submit_all(profile: gr.OAuthProfile | None):
-async def run_and_submit_all():
+async def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
@@ -88,12 +86,12 @@ async def run_and_submit_all():
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID")  # 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
+    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
     username = "benjosaur"
 
     api_url = DEFAULT_API_URL
@@ -226,181 +224,90 @@ async def run_and_submit_all():
 
 
 # # --- 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])
-
-# async def main():
-#     agent = BasicAgent()
-#     api_url = DEFAULT_API_URL
-#     questions_url = f"{api_url}/questions"
-#     print(f"Fetching questions from: {questions_url}")
-
-#     response = requests.get(questions_url, timeout=15)
-#     response.raise_for_status()
-#     questions_data = response.json()
-
-#     # 3. Run your Agent
-#     results_log = []
-#     answers_payload = []
-#     print(f"Running agent on {len(questions_data)} questions...")
-#     item = questions_data[0]
-#     task_id = item.get("task_id")
-#     question_text = item.get("question")
-#     file_name = item.get("file_name")
-#     submitted_answer = await agent(question_text, task_id, file_name)
-#     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,
-#         }
-#     )
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
 
-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)
-    # asyncio.run(run_and_submit_all())
+        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.
 
-    answers_payload = [
-        {
-            "task_id": "a1e91b78-d3d8-4675-bb8d-62741b4b68a6",
-            "submitted_answer": "3",
-        },
-        {
-            "task_id": "2d83110e-a098-4ebb-9987-066c06fa42d0",
-            "submitted_answer": "right",
-        },
-        {
-            "task_id": "cca530fc-4052-43b2-b130-b30968d8aa44",
-            "submitted_answer": "Qxb3",
-        },
-        {
-            "task_id": "4fc2f1ae-8625-45b5-ab34-ad4433bc21f8",
-            "submitted_answer": "Ian Rose",
-        },
-        {
-            "task_id": "6f37996b-2ac7-44b0-8e68-6d28256631b4",
-            "submitted_answer": "a, b, c",
-        },
-        {
-            "task_id": "9d191bce-651d-4746-be2d-7ef8ecadb9c2",
-            "submitted_answer": "Extremely",
-        },
-        {
-            "task_id": "cabe07ed-9eca-40ea-8ead-410ef5e83f91",
-            "submitted_answer": "Undetermined",
-        },
-        {
-            "task_id": "3cef3a44-215e-4aed-8e3b-b1e3f08063b7",
-            "submitted_answer": "broccoli, celery, corn, green beans, lettuce, sweet potatoes, zucchini",
-        },
-        {
-            "task_id": "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3",
-            "submitted_answer": "cornstarch, freshly squeezed lemon juice, granulated sugar, pure vanilla extract, ripe strawberries",
-        },
-        {
-            "task_id": "305ac316-eef6-4446-960a-92d80d542f82",
-            "submitted_answer": "Piotr",
-        },
-        {
-            "task_id": "3f57289b-8c60-48be-bd80-01f8099ca449",
-            "submitted_answer": "525",
-        },
-        {
-            "task_id": "1f975693-876d-457b-a649-393859e79bf3",
-            "submitted_answer": "132, 133, 134, 197, 245",
-        },
-        {
-            "task_id": "840bfca7-4f7b-481a-8794-c560c340185d",
-            "submitted_answer": "unknown",
-        },
-        {
-            "task_id": "bda648d7-d618-4883-88f4-3466eabd860e",
-            "submitted_answer": "Saint Petersburg",
-        },
-        {
-            "task_id": "cf106601-ab4f-4af9-b045-5295fe67b37d",
-            "submitted_answer": "LIE",
-        },
-        {
-            "task_id": "a0c07678-e491-4bbc-8f0b-07405144218f",
-            "submitted_answer": "Itoh, Unknown",
-        },
-        {
-            "task_id": "7bd855d8-463d-4ed5-93ca-5fe35145f733",
-            "submitted_answer": "56973.00",
-        },
-        {
-            "task_id": "5a0c1adf-205e-4841-a666-7c3ef95def9d",
-            "submitted_answer": "Claus",
-        },
-    ]
+        ---
+        **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])
+
+
+async def main():
+    agent = BasicAgent()
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-    submission_data = {
-        "username": "benjosaur",
-        "agent_code": "https://huggingface.co/spaces/Benjosaur/Final_Assignment_Template/tree/main",
-        "answers": answers_payload,
-    }
-    response = requests.post(submit_url, json=submission_data, timeout=60)
-    response.raise_for_status()
-    if not response.ok:
-        print("Error submitting results!")
-        print("Status code:", response.status_code)
-        print("Response text:", response.text)
+    print(f"Fetching questions from: {questions_url}")
+
+    response = requests.get(questions_url, timeout=15)
     response.raise_for_status()
-    result_data = response.json()
-    print(result_data)
+    questions_data = response.json()
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    item = questions_data[0]
+    task_id = item.get("task_id")
+    question_text = item.get("question")
+    file_name = item.get("file_name")
+    submitted_answer = await agent(question_text, task_id, file_name)
+    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,
+        }
+    )
+
+
+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)
+    # asyncio.run(run_and_submit_all())