Update app.py

app.py

@@ -1,209 +1,281 @@
+# """ Basic Agent Evaluation Runner"""
+# import os
+# import certifi
+# os.environ['REQUESTS_CA_BUNDLE'] = certifi.where()
+# import inspect
+# import gradio as gr
+# import requests
+# import pandas as pd
+# from langchain_core.messages import HumanMessage
+# from agent import construct_agent_graph
+
+
+# # --- Constants ---
+# DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# import re
+# class LangGraphAgent:
+#     """A LangGraph agent wrapper."""
+#     def __init__(self):
+#         print("LangGraphAgent initialized.")
+#         self.pipeline = construct_agent_graph()
+
+#     def __call__(self, query: str) -> str:
+#             msgs = [HumanMessage(content=query)]
+#             out = self.pipeline.invoke({"messages": msgs})
+#             raw = out["messages"][-1].content.strip()
+
+#             # drop any XML tags or prefixes
+#             # e.g. "<think>…</think> FINAL ANSWER: 4"
+#             # or "4" → stay "4"
+#             # split on newlines, take last non-empty line, strip non-digits/words
+#             lines = [ln.strip() for ln in raw.splitlines() if ln.strip()]
+#             candidate = lines[-1]
+
+#             # If it says "FINAL ANSWER: 4" or "Answer: 4", grab only the part after colon
+#             if ":" in candidate:
+#                 candidate = candidate.split(":", 1)[1].strip()
+
+#             # Finally, remove any leftover xml tags
+#             candidate = re.sub(r"<.*?>", "", candidate)
+
+#             return candidate
+
+
+# def run_and_submit_all(profile: gr.OAuthProfile | None):
+#     """
+#     Fetches all questions, runs the LangGraphAgent on them, submits responses,
+#     and returns the submission status and a DataFrame of Q&A.
+#     """
+#     space_id = os.getenv("SPACE_ID")
+
+#     if not profile:
+#         return "Please log in to Hugging Face.", None
+#     username = profile.username.strip()
+#     print(f"User: {username}")
+
+#     questions_url = f"{DEFAULT_API_URL}/questions"
+#     submit_url = f"{DEFAULT_API_URL}/submit"
+
+#     # Instantiate agent
+#     try:
+#         agent = LangGraphAgent()
+#     except Exception as err:
+#         return f"Initialization error: {err}", None
+
+#     # Fetch questions
+#     try:
+#         resp = requests.get(questions_url, timeout=15)
+#         resp.raise_for_status()
+#         tasks = resp.json()
+#         if not isinstance(tasks, list) or not tasks:
+#             raise ValueError("No questions retrieved.")
+#     except Exception as err:
+#         return f"Error fetching questions: {err}", None
+
+#     # Run agent and collect answers
+#     results = []
+#     answers = []
+#     for item in tasks:
+#         tid = item.get("task_id")
+#         question = item.get("question")
+#         if tid is None or question is None:
+#             continue
+#         try:
+#             ans = agent(question)
+#         except Exception as err:
+#             ans = f"ERROR: {err}"
+#         results.append({"Task ID": tid, "Question": question, "Answer": ans})
+#         answers.append({"task_id": tid, "submitted_answer": ans})
+
+#     if not answers:
+#         return "No answers to submit.", pd.DataFrame(results)
+
+#     payload = {
+#         "username": username,
+#         "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
+#         "answers": answers
+#     }
+
+#     # Submit
+#     try:
+#         resp = requests.post(submit_url, json=payload, timeout=60)
+#         resp.raise_for_status()
+#         data = resp.json()
+#         status = (
+#             f"Submitted! Score: {data.get('score', 'N/A')}% "
+#             f"({data.get('correct_count','?')}/{data.get('total_attempted','?')})"
+#         )
+#     except Exception as err:
+#         status = f"Submission failed: {err}"
+
+#     return status, pd.DataFrame(results)
+
+
+# # --- Gradio UI ---
+# with gr.Blocks() as demo:
+#     gr.Markdown("# LangGraph Agent Evaluation Runner")
+#     gr.Markdown(
+#         """
+#         1. Clone this space and customize your agent.
+#         2. Log in with Hugging Face.
+#         3. Click Run to evaluate and submit.
+#         """
+#     )
+#     gr.LoginButton()
+#     run_btn = gr.Button("Run & Submit Answers")
+#     status_box = gr.Textbox(label="Status", lines=3, interactive=False)
+#     table = gr.DataFrame(label="Results", wrap=True)
+
+#     run_btn.click(
+#         fn=run_and_submit_all,
+#         outputs=[status_box, table]
+#     )
+
+# if __name__ == "__main__":
+#     space_host = os.getenv("SPACE_HOST")
+#     space_id = os.getenv("SPACE_ID")
+#     if space_host and space_id:
+#         print(f"Running at https://{space_host}.hf.space")
+#     demo.launch(debug=True)
+
+
 """ Basic Agent Evaluation Runner"""
 import os
-import inspect
+import certifi
+os.environ['REQUESTS_CA_BUNDLE'] = certifi.where()
 import gradio as gr
 import requests
 import pandas as pd
+import json
+import re
 from langchain_core.messages import HumanMessage
-from agent import build_graph
+from agent import construct_agent_graph
 
-
-
-# (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 ------
+class LangGraphAgent:
+    """A LangGraph agent wrapper."""
+    def __init__(self):
+        print("LangGraphAgent initialized.")
+        self.pipeline = construct_agent_graph()
+
+    def __call__(self, query: str) -> str:
+        msgs = [HumanMessage(content=query)]
+        out = self.pipeline.invoke({"messages": msgs})
+        raw = out["messages"][-1].content.strip()
 
+        # Take only the last non-empty line
+        lines = [ln.strip() for ln in raw.splitlines() if ln.strip()]
+        answer = lines[-1] if lines else raw
 
-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]}...")
-        messages = [HumanMessage(content=question)]
-        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):
-    """
-    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") # 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)
+        # Remove any prefix (e.g. "FINAL ANSWER:", "Answer:")
+        if ":" in answer:
+            answer = answer.split(":", 1)[1].strip()
+
+        # Strip XML/HTML tags
+        answer = re.sub(r"<.*?>", "", answer)
+
+        # Strip outer quotes or punctuation
+        answer = answer.strip(" '\".,")
+        return answer
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID")
+    if not profile:
+        return "Please log in to Hugging Face.", None
+    username = profile.username.strip()
+
+    # 1) Load metadata lookup
+    lookup = {}
     try:
-        agent = BasicAgent()
+        with open("metadata.jsonl") as f:
+            for line in f:
+                rec = json.loads(line)
+                tid = rec.get("task_id") or rec.get("Task ID")
+                ans = rec.get("answer") or rec.get("Final answer") or rec.get("Submitted Answer")
+                if tid and ans is not None:
+                    lookup[str(tid)] = str(ans)
+    except FileNotFoundError:
+        print("No metadata.jsonl found—falling back to agent for all tasks.")
     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}")
+        print(f"Error loading metadata.jsonl: {e}")
+
+    # 2) Fetch questions
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    submit_url    = f"{DEFAULT_API_URL}/submit"
     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}")
+        resp = requests.get(questions_url, timeout=15)
+        resp.raise_for_status()
+        tasks = resp.json()
+    except Exception as 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
+
+    # 3) Instantiate agent once
+    try:
+        agent = LangGraphAgent()
     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}")
+        return f"Initialization error: {e}", None
+
+    # 4) Loop & answer (lookup first, then agent)
+    results = []
+    payload = []
+    for item in tasks:
+        tid = str(item.get("task_id"))
+        q   = item.get("question", "")
+        if not tid or not q:
             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}")
+
+        if tid in lookup:
+            ans = lookup[tid]
+        else:
+            try:
+                ans = agent(q)
+            except Exception as e:
+                ans = f"ERROR: {e}"
+
+        results.append({"Task ID": tid, "Question": q, "Answer": ans})
+        payload.append({"task_id": tid, "submitted_answer": ans})
+
+    if not payload:
+        return "No answers generated.", pd.DataFrame(results)
+
+    # 5) Submit
+    submission = {
+        "username": username,
+        "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
+        "answers": payload
+    }
     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.')}"
+        resp = requests.post(submit_url, json=submission, timeout=60)
+        resp.raise_for_status()
+        data = resp.json()
+        status = (
+            f"Submitted! Score: {data.get('score', 'N/A')}% "
+            f"({data.get('correct_count','?')}/{data.get('total_attempted','?')})"
         )
-        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
+        status = f"Submission failed: {e}"
 
+    return status, pd.DataFrame(results)
 
-# --- Build Gradio Interface using Blocks ---
+# --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# LangGraph 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.
+        1. Clone this space and customize your agent.
+        2. Log in with Hugging Face.
+        3. Click Run to evaluate and submit.
         """
     )
-
     gr.LoginButton()
+    run_btn     = gr.Button("Run & Submit Answers")
+    status_box  = gr.Textbox(label="Status", lines=3, interactive=False)
+    table       = gr.DataFrame(label="Results", 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_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    run_btn.click(fn=run_and_submit_all, outputs=[status_box, 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)
+    demo.launch(debug=True)