reverted app.py to original

app.py

@@ -1,204 +1,193 @@
-import asyncio
-import json
 import os
-from pathlib import Path
 
-import aiohttp
 import gradio as gr
 import pandas as pd
+import requests
 
 from agent import Agent
+from src.tracing import add_tracing
 
-# --- Constants --------------------------------------------------------------
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-CACHE_PATH = Path("answers_cache.json")  # local answer cache
-MAX_CONCURRENCY = int(os.getenv("MAX_CONCURRENCY", 8))  # tune if needed
-# ----------------------------------------------------------------------------
-
-
-# ---------------------------------------------------------------------------
-# Small helpers
-# ---------------------------------------------------------------------------
-def load_cache() -> dict[str, str]:
-    if CACHE_PATH.is_file():
-        try:
-            return json.loads(CACHE_PATH.read_text())
-        except Exception:
-            print("⚠️  Cache file corrupt – starting fresh.")
-    return {}
-
-
-def save_cache(cache: dict[str, str]) -> None:
-    tmp = CACHE_PATH.with_suffix(".tmp")
-    tmp.write_text(json.dumps(cache, ensure_ascii=False, indent=2))
-    tmp.replace(CACHE_PATH)
-
-
-# ---------------------------------------------------------------------------
-# Core async logic
-# ---------------------------------------------------------------------------
-async def _fetch_questions(
-    session: aiohttp.ClientSession, url: str
-) -> list[dict]:
-    async with session.get(url, timeout=15) as r:
-        r.raise_for_status()
-        return await r.json()
-
-
-async def _submit_answers(session: aiohttp.ClientSession, url: str, data: dict):
-    async with session.post(url, json=data, timeout=60) as r:
-        r.raise_for_status()
-        return await r.json()
-
-
-def _run_agent_sync(
-    agent: Agent,
-    question: str,
-    task_id: str | int,
-    file_name: str,
-    cache: dict[str, str],
-) -> tuple[str | int, str]:
-    """
-    Run the agent in a threadpool (because most agents are sync / blocking),
-    respecting concurrency limits, and fill the cache.
-    """
-    if str(task_id) in cache:
-        return task_id, cache[str(task_id)]
+add_tracing()
 
-    answer = agent(
-        question, task_id, file_name, DEFAULT_API_URL
-    )  # execute in default thread‑pool
-    cache[str(task_id)] = answer
-    return task_id, answer
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 
-async def _async_main(profile: gr.OAuthProfile | None):
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Async counterpart of run_and_submit_all; returns (status_msg, results_df)
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
     """
-    if not profile:
-        return "Please login with the Hugging Face button.", None
-
-    username = profile.username
-    space_id = os.getenv("SPACE_ID")
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    # --- 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
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Build agent (sync, cheap)
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
         agent = Agent()
     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
-    async with aiohttp.ClientSession() as session:
+    # 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:
-            questions = await _fetch_questions(session, questions_url)
-        except Exception as e:
-            return f"Error fetching questions: {e}", None
-
-        if not questions:
-            return "Fetched questions list is empty.", None
-
-        # 3. Run agent with cache
-        cache = load_cache()
-        results = [
-            _run_agent_sync(
-                agent, q["question"], q["task_id"], q["file_name"], cache
+            submitted_answer = agent(question_text)
+            answers_payload.append(
+                {"task_id": task_id, "submitted_answer": submitted_answer}
             )
-            for q in questions
-            if q.get("task_id") and q.get("question") is not None
-        ]
-
-        results_log: list[dict] = []
-        answers_json: list[dict] = []
-
-        for task_id, answer in results:
-            answers_json.append(
-                {"task_id": task_id, "submitted_answer": 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": next(
-                        (
-                            q["question"]
-                            for q in questions
-                            if q["task_id"] == task_id
-                        ),
-                        "",
-                    ),
-                    "Submitted Answer": answer,
+                    "Question": question_text,
+                    "Submitted Answer": f"AGENT ERROR: {e}",
                 }
             )
 
-        # 3b. Persist cache for later runs
-        save_cache(cache)
-
-        if not answers_json:
-            return "Agent produced no answers.", pd.DataFrame(results_log)
+    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. Submit
-        submission = {
-            "username": username.strip(),
-            "agent_code": agent_code,
-            "answers": answers_json,
-        }
+    # 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:
-            result = await _submit_answers(session, submit_url, submission)
-        except Exception as e:
-            status = f"Submission failed: {e}"
-            return status, pd.DataFrame(results_log)
-
-    # Format success message
-    final = (
-        "Submission Successful!\n"
-        f"User: {result.get('username')}\n"
-        f"Overall Score: {result.get('score', 'N/A')}% "
-        f"({result.get('correct_count', '?')}/{result.get('total_attempted', '?')} correct)\n"
-        f"Message: {result.get('message', 'No message received.')}"
-    )
-    return final, pd.DataFrame(results_log)
-
-
-# ---------------------------------------------------------------------------
-# Thin sync wrapper required by gradio
-# ---------------------------------------------------------------------------
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Synchronous façade expected by Gradio; just dispatches to asyncio.
-    """
-    return asyncio.run(_async_main(profile))
+            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
 
 
-# ---------------------------------------------------------------------------
-# Gradio UI (unchanged except for doc‑string tweaks)
-# ---------------------------------------------------------------------------
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Async‑cached Agent Evaluation Runner")
-
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
-        **Quick‑start**
+        **Instructions:**
+
+        1.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        2.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
 
-        1. Log in with the HF button (needed for ranking).
-        2. Hit **Run Evaluation & Submit All Answers** – answers are cached
-           locally so reruns are instant; agent calls & HTTP are parallel.
+        ---
+        **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_btn = gr.Button("Run Evaluation & Submit All Answers")
-    status_box = gr.Textbox(label="Status / Submission Result", lines=5)
-    results_table = gr.DataFrame(label="Questions & Answers", wrap=True)
+    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_btn.click(
-        fn=run_and_submit_all,
-        outputs=[status_box, results_table],
+    run_button.click(
+        fn=run_and_submit_all, outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":