return tp openai

agent.py

@@ -172,4 +172,4 @@ def build_graph(provider: str = "openai"):
     builder.add_conditional_edges("assistant", tools_condition)
     builder.add_edge("tools", "assistant")
 
-    return builder.compile()
+    return builder.compile()

app.py

@@ -1,115 +1,204 @@
 import os
 import gradio as gr
 import requests
+import inspect
 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"
-SPACE_ID = "marjanns/Final_Assignment_Template"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+HF_TOKEN = os.getenv("HF_TOKEN")
+
 
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("SmartAgent initialized.")
+        self.graph = build_graph()
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        return "This is a default answer."
-
-def _username_from_profile(p):
-    if p is None:
-        return None
-    # object with attribute
-    u = getattr(p, "username", None)
-    if u:
-        return str(u)
-    # dict-style
-    if isinstance(p, dict):
-        return str(p.get("username") or "")
-    return None
-
-def run_and_submit_all(profile, evt=None):
-    space_id = SPACE_ID
-    username = _username_from_profile(profile)
-    if not username:
+        # 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") # 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:
-            return "Fetched questions list is empty or invalid format.", None
-    except Exception as e:
+             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
 
-    results_log, answers_payload = [], []
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
-        tid = item.get("task_id")
-        q = item.get("question")
-        if not tid or q is None:
+        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:
-            ans = agent(q)
+            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:
-            ans = f"AGENT ERROR: {e}"
-        answers_payload.append({"task_id": tid, "submitted_answer": ans})
-        results_log.append({"Task ID": tid, "Question": q, "Submitted Answer": ans})
+             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:
-        resp = requests.post(submit_url, json=submission_data, timeout=60)
-        resp.raise_for_status()
-        data = resp.json()
-        status = (
-            "Submission Successful!\n"
-            f"User: {data.get('username')}\n"
-            f"Overall Score: {data.get('score','N/A')}% "
-            f"({data.get('correct_count','?')}/{data.get('total_attempted','?')} correct)\n"
-            f"Message: {data.get('message','No message received.')}"
+        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.')}"
         )
-        return status, pd.DataFrame(results_log)
+        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:
-            detail = e.response.json().get("detail", e.response.text)
-        except Exception:
-            detail = e.response.text
-        return f"Submission Failed: HTTP {e.response.status_code}. Detail: {detail[:500]}", pd.DataFrame(results_log)
+            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:
-        return "Submission Failed: The request timed out.", pd.DataFrame(results_log)
+        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:
-        return f"Submission Failed: {e}", pd.DataFrame(results_log)
+        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("Click **Log in with Hugging Face** first. After you see your username below, press **Run**.")
-
-    login = gr.LoginButton()
-    user_state = gr.State()
-    whoami = gr.Markdown()
-
-    def store_and_echo(p):
-        u = _username_from_profile(p) or "(not logged in)"
-        return p, f"✅ Logged in as **{u}**" if u and u != "(not logged in)" else "❌ Not logged in"
-
-    login.click(store_and_echo, inputs=login, outputs=[user_state, whoami])
+    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, inputs=[user_state], outputs=[status_output, results_table])
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    demo.launch(debug=True, share=True)  
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    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)

requirements.txt

@@ -50,8 +50,29 @@
 # requests
 # smolagents
 # smolagents[openai]
-gradio[oauth]>=4.44.0
-requests>=2.31.0
-pandas>=2.2.0
-itsdangerous>=2.1.2
-authlib>=1.3.0
+# gradio[oauth]>=4.44.0
+# requests>=2.31.0
+# pandas>=2.2.0
+# itsdangerous>=2.1.2
+# authlib>=1.3.0
+gradio
+requests
+langchain
+langchain-community
+langchain-core
+langchain-google-genai
+langchain-huggingface
+langchain-groq
+langchain_openai
+langchain-tavily
+langchain-chroma
+langgraph
+huggingface_hub
+sentence-transformers
+arxiv
+pymupdf
+wikipedia
+pgvector
+python-dotenv
+protobuf==3.20.*
+chromadb