First pass version

.gitignore

@@ -0,0 +1,2 @@
+.env
+*pycache*

app.py

@@ -3,6 +3,9 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from dotenv import load_dotenv
+from smolagents import DuckDuckGoSearchTool, OpenAIServerModel, CodeAgent, Tool
+from blablador import Models
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -10,25 +13,68 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+load_dotenv()
+
+
 class BasicAgent:
+
     def __init__(self):
-        print("BasicAgent initialized.")
+        models = Models(api_key=os.getenv("Blablador_API_KEY")).get_model_ids()
+        model_id_blablador = 4
+        model_name = " ".join(
+            models[model_id_blablador].split(" - ")[1].split()[:2])
+        print("The agent uses the following model:", model_name)
+        answer_llm = OpenAIServerModel(
+            model_id=models[model_id_blablador],
+            api_base="https://helmholtz-blablador.fz-juelich.de:8000/v1",
+            api_key=os.getenv("Blablador_API_KEY"),
+            flatten_messages_as_text=True,
+            temperature=0.2)
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool()],
+            model=answer_llm,
+            planning_interval=3,
+            max_steps=10,
+            # verbosity_level=LogLevel.ERROR,
+        )
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+        SYSTEM_PROMPT = "You are a general AI assistant. I will ask you a question. " \
+        "Report your thoughts, and finish your answer with the following template: " \
+        "FINAL ANSWER: [YOUR FINAL ANSWER]. " \
+        "YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. " \
+        "If you are asked for a number, don't use comma to write your number neither use units " \
+        "such as $ or percent sign unless specified otherwise. " \
+        "If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), " \
+        "and write the digits in plain text unless specified otherwise. " \
+        "If you are asked for a comma separated list, " \
+        "apply the above rules depending of whether the element to be put in the list is a number or a string."
+
+        # Combine system prompt with the user question
+        full_prompt = f"{SYSTEM_PROMPT}\n\nQuestion: {question}"
+
+        try:
+            answer = self.agent.run(full_prompt)
+            print(f"Agent returning answer: {answer}")
+            return answer
+        except Exception as e:
+            print(f"Error running agent: {e}")
+            return f"Error: {e}"
+
+
+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
+    space_id = os.getenv(
+        "SPACE_ID")  # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -55,16 +101,16 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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("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
+        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
@@ -79,20 +125,41 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
+
+        file_name = item.get("file_name")
+        file_ext = None
+        file_url = None
+
         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})
+            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}"})
+            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)
+        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}
+    # 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)
 
@@ -107,8 +174,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             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.')}"
-        )
+            f"Message: {result_data.get('message', 'No message received.')}")
         print("Submission successful.")
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
@@ -143,8 +209,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
+    gr.Markdown("""
         **Instructions:**
 
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
@@ -155,42 +220,49 @@ with gr.Blocks() as demo:
         **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)
+    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)
+    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])
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    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
+    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")
+        print(
+            f"   Runtime URL should be: https://{space_host_startup}.hf.space")
     else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+        print(
+            "ℹ️  SPACE_HOST environment variable not found (running locally?)."
+        )
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    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")
+        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(
+            "ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."
+        )
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    print("-" * (60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)

blablador.py

@@ -0,0 +1,29 @@
+import requests
+import json
+
+
+class Models():
+
+    def __init__(self, api_key):
+        self.api_key = api_key
+        self.headers = {
+            'accept': 'application/json',
+            'Authorization': f'Bearer {api_key}'
+        }
+
+    url = "https://helmholtz-blablador.fz-juelich.de:8000/v1/models"
+
+    def get_model_data(self):
+        response = requests.get(url=self.url, headers=self.headers)
+        response = json.loads(response.text)
+        return (response["data"])
+
+    def get_model_ids(self):
+        response = requests.get(url=self.url, headers=self.headers)
+        response = json.loads(response.text)
+
+        ids = []
+        for model in response["data"]:
+            ids.append(model["id"])
+
+        return (ids)