initial commit 7/20

.gitignore

@@ -0,0 +1,21 @@
+*.csv
+*.xlsx
+*.py
+*.png
+*.jpg
+*.jpeg
+*.gif
+*.mp3
+*.wav
+*.mp4
+*.avi
+*.mov
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.db
+*.env
+*.envrc
+*.log
+.vscode/

agent.py

@@ -0,0 +1,208 @@
+import base64
+import logging
+import os
+from io import BytesIO
+from typing import Any
+
+from smolagents import (
+    CodeAgent,
+    DuckDuckGoSearchTool,
+    OpenAIServerModel,
+    SpeechToTextTool,
+    VisitWebpageTool,
+    WikipediaSearchTool,
+    tool,
+)
+
+system_prompt = """You are an AI Agent that is tasked to answer questions in a concise and accurate manner.
+I will ask you a question and provide you with additional context if available.
+Context can be in the form of Data(data), Code(code), Audio(audio), or Images(image_url).
+Context is provided by specifying the content type followed by the content itself.
+
+For example: code: print("Hello World") or Data: [1, 2, 3, 4, 5] or audio: [base64 encoded audio] or image_url: [base64 encoded image].
+
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
+DO NOT use formatting such as bold, italics, or code blocks in your final answer.
+DO NOT use sources, references, or abbreviations in your final answer.
+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.
+If you are asked for a specific number format, follow the instructions carefully.
+If you are asked for a number only answer with the number itself, without any additional text or formatting.
+If you are asked for a string only answer with the string itself, without any additional text or formatting.
+If you are asked for a list only answer with the list itself, without any additional text or formatting.
+
+Finish your Answer with the following template:
+FINAL ANSWER: [YOUR FINAL ANSWER]
+
+For example, if the question is "What is the capital of France?", you should answer:
+FINAL ANSWER: Paris
+
+If the question is "What is 2 + 2?", you should answer:
+FINAL ANSWER: 4
+
+If the question is "What is 1 divided by 2, answer with 2 digits after the decimal point?", you should answer:
+FINAL ANSWER: 0.50
+
+Think step by step, and use the tools provided to gather information if necessary.
+Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
+"""
+
+
+# def is_correct_format(answer: str, _) -> bool:
+#     """Check if the answer contains a final answer in the correct format.
+
+#     Args:
+#         answer: The answer to check.
+
+#     Returns:
+#         True if the answer contains a final answer, False otherwise.
+#         This ensures the final output is in the correct format.
+#     """
+#     return (
+#         "ANSWER:" in answer
+#         or "FINAL ANSWER:" in answer
+#         or "Answer:" in answer
+#         or "Final Answer:" in answer
+#         or "answer:" in answer
+#         or "final answer:" in answer
+#         or "answer:" in answer.lower()
+#         or "final answer:" in answer.lower()
+#     )
+
+
+@tool
+def wikipedia_suggested_page(query: str) -> str:
+    """Search Wikipedia for suggested pages based on the query.
+
+    Args:
+        query: The search query. The query should be coarse and not provide too many details.
+            E.g. "Python programming" or "Artificial Intelligence".
+
+    Returns:
+        A list of suggested page titles. Pages are \n separated.
+    """
+    from wikipedia import suggest
+
+    try:
+        return suggest(query)
+    except Exception as e:
+        logging.error(f"Error fetching Wikipedia suggestions for '{query}': {e}")
+        return f"Error fetching suggestions: {e}"
+
+
+@tool
+def wikipedia_page(title: str) -> str:
+    """Search Wikipedia for a page based on the title.
+
+    Args:
+        title: The title of the Wikipedia page to search for.
+
+    Returns:
+        The content of the Wikipedia page.
+    """
+    from wikipedia import page
+
+    try:
+        return page(title, auto_suggest=True).content
+    except Exception as e:
+        logging.error(f"Error fetching Wikipedia page for '{title}': {e}")
+        return f"Error fetching page: {e}"
+
+
+class BasicAgent:
+    def __init__(self):
+        model = OpenAIServerModel(
+            model_id="gpt-4o-mini",
+            api_key=os.getenv("OPENAI_API_KEY"),
+        )
+        search = DuckDuckGoSearchTool(max_results=5)
+        # speech_to_text = SpeechToTextTool()
+        visitor = VisitWebpageTool(max_output_length=4000)
+        wiki_search = WikipediaSearchTool()
+        self.agent = CodeAgent(
+            max_steps=5,
+            verbosity_level=0,
+            tools=[
+                search,
+                # speech_to_text,
+                visitor,
+                wiki_search,
+                wikipedia_suggested_page,
+                wikipedia_page,
+            ],
+            model=model,
+            instructions=system_prompt,
+            additional_authorized_imports=["pandas", "numpy"],
+            use_structured_outputs_internally=True,
+            add_base_tools=True,
+            # final_answer_checks=[is_correct_format],
+        )
+        logging.info(
+            f"System prompt set for BasicAgent: {self.agent.memory.system_prompt}"
+        )
+
+    def __call__(self, question: str, content, content_type) -> Any:
+        match content_type:
+            case "xlsx":
+                additional_args = {"data": content}
+            case "py":
+                additional_args = {"code": content}
+            case "audio":
+                additional_args = {"audio": content}
+            case "png":
+                buffer = BytesIO()
+                content.save(buffer, format="PNG")
+                buffer.seek(0)
+                image_content = (
+                    "data:image/png;base64,"
+                    + base64.b64encode(buffer.getvalue()).decode("utf-8")
+                )
+                additional_args = {"image_url": image_content}
+            case _:
+                additional_args = None
+        response = self.agent.run(
+            question,
+            additional_args=additional_args,
+            images=[content] if content_type == "png" else None,
+            reset=True,
+        )
+        return response
+
+    @staticmethod
+    def formatting(answer: str) -> str:
+        """Extract the final answer from the response."""
+        if "FINAL ANSWER:" in answer:
+            answer = answer.split("FINAL ANSWER:")[-1].strip()
+        if "ANSWER:" in answer:
+            answer = answer.split("ANSWER:")[-1].strip()
+        if "Answer:" in answer:
+            answer = answer.split("Answer:")[-1].strip()
+        if "Final Answer:" in answer:
+            answer = answer.split("Final Answer:")[-1].strip()
+        if "answer:" in answer.lower():
+            answer = answer.split("answer:")[-1].strip()
+        if "final answer:" in answer.lower():
+            answer = answer.split("final answer:")[-1].strip()
+        if "answer is:" in answer.lower():
+            answer = answer.split("answer is:")[-1].strip()
+        if "is:" in answer.lower():
+            answer = answer.split("is:")[-1].strip()
+        if "**" in answer:
+            answer = answer.split("**")[-1].strip().replace("**", "")
+        if "```" in answer:
+            answer = answer.split("```")[-1].strip().replace("```", "")
+        if "```python" in answer:
+            answer = answer.split("```python")[-1].strip().replace("```", "")
+        if "```json" in answer:
+            answer = answer.split("```json")[-1].strip().replace("```", "")
+        if "```yaml" in answer:
+            answer = answer.split("```yaml")[-1].strip().replace("```", "")
+        if "```txt" in answer:
+            answer = answer.split("```txt")[-1].strip().replace("```", "")
+        answer = answer.capitalize()
+        
+        answer = answer.replace('"', '').strip()
+        answer = answer.replace("'", "").strip()
+        answer = answer.replace("[", "").replace("]", "").strip()
+        return answer.strip()  # Fallback to return the whole answer if no specific format found

app.py

@@ -1,34 +1,38 @@
+import inspect
 import os
+
 import gradio as gr
-import requests
-import inspect
 import pandas as pd
+import requests
 
 # (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 BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
+
     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):
+
+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 +59,12 @@ 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
     except Exception as e:
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
@@ -81,18 +81,36 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             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})
+            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)
 
-    # 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)
 
@@ -162,20 +180,19 @@ with gr.Blocks() as demo:
 
     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)
 
-    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}")
@@ -183,14 +200,18 @@ if __name__ == "__main__":
     else:
         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)

app_dev.py

@@ -0,0 +1,133 @@
+import os
+
+import gradio as gr
+import pandas as pd
+import requests
+from langfuse import get_client
+from openinference.instrumentation.smolagents import SmolagentsInstrumentor
+
+from agent import BasicAgent
+from fetch import DEFAULT_API_URL, fetch_questions, run_agent
+
+submit_url = f"{DEFAULT_API_URL}/submit"
+
+langfuse = get_client()
+
+# Verify connection
+if langfuse.auth_check():
+    print("Langfuse client is authenticated and ready!")
+else:
+    print("Authentication failed. Please check your credentials and host.")
+
+SmolagentsInstrumentor().instrument()
+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
+    # 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)
+    agent = BasicAgent()
+    questions_data = fetch_questions()
+    answers_payload, results_log = run_agent(agent, questions_data)
+    # 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:
+            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
+
+
+# --- 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])
+
+
+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
+
+    demo.launch(debug=True, share=False)

fetch.py

@@ -0,0 +1,139 @@
+import logging
+from io import BytesIO
+
+import pandas as pd
+import requests
+from PIL import Image
+
+from agent import BasicAgent
+
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+questions_url = f"{DEFAULT_API_URL}/questions"
+files_url = f"{DEFAULT_API_URL}/files"
+
+
+def fetch_questions():
+    """
+    Fetches questions from the API.
+    Returns a list of questions or an error message.
+    """
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+            logging.warning("Fetched questions list is empty.")
+            return None
+        logging.info(f"Fetched {len(questions_data)} questions.")
+
+        for question in questions_data:
+            content, content_type = _load_files(question)
+            if content is not None:
+                question["file_content"] = content
+                question["file_type"] = content_type
+        return questions_data
+    except requests.exceptions.RequestException as e:
+        logging.error(f"Error fetching questions: {e}")
+        return None
+    except Exception as e:
+        logging.error(f"An unexpected error occurred fetching questions: {e}")
+        return None
+    return questions_data
+
+
+def _load_files(question_data: dict):
+    if file_name := question_data.get("file_name"):
+        extension = file_name.split(".")[-1]
+        if extension not in ["xlsx", "png", "py", "mp3", "wav"]:
+            logging.warning(
+                f"File {file_name} has an unsupported extension. Skipping file loading."
+            )
+            return None, None  # Ensure a tuple is always returned
+        if task_id := question_data.get("task_id"):
+            try:
+                if extension == "mp3" or extension == "wav":
+                    return f"{files_url}/{task_id}", "audio"
+                response = requests.get(f"{files_url}/{task_id}", timeout=15)
+                response.raise_for_status()
+                if response.status_code == 200:
+                    # extensions: xlsx, png, py, else ignore
+                    match extension:
+                        case "xlsx":
+                            if (
+                                response.headers.get("Content-Type")
+                                == "application/octet-stream"
+                            ):
+                                logging.info(f"Processing Excel file: {file_name}")
+                                return pd.read_excel(response.content).to_json(), "xlsx"
+                        case "png":
+                            if response.headers.get("Content-Type") == "image/png":
+                                logging.info(f"Processing image file: {file_name}")
+                                return Image.open(BytesIO(response.content)).convert("RGB"), "png"
+                        case "py":
+                            if response.headers.get("Content-Type", "").startswith(
+                                "text/x-python"
+                            ):
+                                logging.info(f"Processing Python file: {file_name}")
+                                return response.content.decode(
+                                    "utf-8"
+                                ), "py"  # Load Python file if needed
+            except requests.exceptions.RequestException as e:
+                logging.error(f"Error fetching file for task {task_id}: {e}")
+                raise e
+            except Exception as e:
+                logging.error(
+                    f"An unexpected error occurred fetching file for task {task_id}: {e}"
+                )
+                raise e
+            return None, None
+    return None, None  # Always return a tuple
+
+
+def run_agent(agent, questions_data):
+    results_log = []
+    answers_payload = []
+    logging.info(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        payload, log_item = run_agent_on_question(agent, item)
+        if payload is not None:
+            answers_payload.append(payload)
+        if log_item is not None:
+            results_log.append(log_item)
+    if not answers_payload:
+        logging.info("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+    return answers_payload, pd.DataFrame(results_log)
+
+
+def run_agent_on_question(agent: BasicAgent, question):
+    """
+    Runs the agent on a single question and returns the answer.
+    """
+    task_id = question.get("task_id")
+    question_text = question.get("question")
+    content = question.get("file_content")
+    content_type = question.get("file_type")
+    if not task_id or question_text is None:
+        logging.warning(f"Skipping item with missing task_id or question: {question}")
+        return None, None
+    try:
+        submitted_answer = agent(question_text, content=content, content_type=content_type)
+        return (
+            {"task_id": task_id, "submitted_answer": submitted_answer},
+            {
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": submitted_answer,
+            },
+        )
+    except Exception as e:
+        logging.error(f"Error running agent on task {task_id}: {e}")
+        return (
+            {
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": f"AGENT ERROR: {e}",
+            },
+            None,
+        )

pyproject.toml

@@ -0,0 +1,20 @@
+[project]
+name = "final-assignment-template"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "chess>=1.11.2",
+    "google-genai>=1.23.0",
+    "gradio[oauth]>=5.34.1",
+    "langfuse>=3.0.7",
+    "openpyxl>=3.1.5",
+    "pandas>=2.3.0",
+    "requests>=2.32.4",
+    "ruff>=0.12.0",
+    "smolagents[audio,litellm,openai,telemetry,toolkit,transformers]>=1.18.0",
+    "transformers>=4.53.0",
+    "wikipedia>=1.4.0",
+    "wikipedia-api>=0.8.1",
+]