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README.md

@@ -1,14 +1,15 @@
 ---
-title: Hf Agents Final Assignment
-emoji: 🚀
-colorFrom: purple
-colorTo: green
+title: Template Final Assignment
+emoji: 🕵🏻‍♂️
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 5.29.0
+sdk_version: 5.25.2
 app_file: app.py
 pinned: false
-license: mit
 hf_oauth: true
+# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
+hf_oauth_expiration_minutes: 480
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

agents/agent.py

@@ -0,0 +1,70 @@
+import os
+import time
+import logging
+from google import genai
+from google.genai.types import GenerateContentConfig
+from ratelimit import limits, sleep_and_retry
+
+RPM = 15
+TPM = 1_000_000
+PER_MINUTE = 60
+SYSTEM_PROMPT_GAIA = "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."
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S"
+)
+logger = logging.getLogger(__name__)
+
+class BasicAgent:
+    def __init__(self):
+        logger.info("BasicAgent initialized.")
+
+    def __call__(self, question: str) -> str:
+        logger.info(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a default answer."
+        logger.info(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
+
+class SimpleGeminiAgent(BasicAgent):
+    def __init__(self, model="gemini-2.0-flash"):
+        super().__init__()
+        gemini_key = os.getenv("GEMINI_API_KEY")
+        self.client = genai.Client(api_key=gemini_key)
+        self.model = model
+        logger.info("AdvancedAgent initialized.")
+        self.minute_start = time.time()
+        self.tokens_this_minute = 0
+        self.token_count = 0
+
+    @sleep_and_retry
+    @limits(calls=RPM, period=PER_MINUTE)
+    def __call__(self, question: str) -> str:
+        now = time.time()
+        if now - self.minute_start >= 60:
+            self.tokens_this_minute = 0
+            self.minute_start = now
+        
+        # Enforce tokens per minute
+        if self.tokens_this_minute + self.token_count > TPM:
+            sleep_time = max(0, 60 - (now - self.minute_start))
+            time.sleep(sleep_time)
+            self.tokens_this_minute = 0
+            self.minute_start = time.time()
+
+        response = self.client.models.generate_content(model=self.model, 
+                                                       contents=question,
+                                                       config=GenerateContentConfig(system_instruction=SYSTEM_PROMPT_GAIA))
+        self.tokens_this_minute += response.total_token_count
+        self.token_count += response.total_token_count
+        logger.info(f"AdvancedAgent received question (first 50 chars): {question[:50]}...")
+        logger.info(f"AdvancedAgent returning answer: {response.text}")
+        return response.text
+
+if __name__ == "__main__":
+    # Example usage
+    agent = SimpleGeminiAgent()
+    question = "What is the capital of France?"
+    answer = agent(question)
+    print(f"Question: {question}\nAnswer: {answer}")

api.py

@@ -0,0 +1,185 @@
+import os
+import requests
+import logging
+import pandas as pd
+import gradio as gr
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S"
+)
+logger = logging.getLogger(__name__)
+
+class GAIAHFAPIClient:
+    """
+    A client for interacting with the GAIA HF API for the final assignment.
+    This client handles dealing with the API requests.
+    """
+
+    def __init__(self, profile: gr.OAuthProfile | None, api_base_url=DEFAULT_API_URL):
+        # --- 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:
+        #    self.username = f"{profile.username}"
+        #    logger.info(f"User logged in: {self.username}")
+        #else:
+        #    logger.warning("User not logged in.")
+        #    return "Please Login to Hugging Face with the button.", None
+        self.agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+        self.questions_url = f"{api_base_url}/questions"
+        self.random_question_url = f"{api_base_url}/random-question"
+        self.file_task_url = f"{api_base_url}/files/" + "{}"
+        self.submit_url = f"{api_base_url}/submit"
+        self.api_base_url = api_base_url
+
+    def submit_answers(self, submission_data, results_log):
+        try:
+            response = requests.post(self.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
+        
+    def get_questions(self):
+        """
+        Fetches the list of questions from the API.
+        Returns:
+            List of questions or an error message.
+        """
+        logger.info(f"Fetching questions from: {self.questions_url}")
+        try:
+            response = requests.get(self.questions_url, timeout=15)
+            response.raise_for_status()
+            questions_data = response.json()
+            if not questions_data:
+                logger.warning("Fetched questions list is empty.")
+                return "Fetched questions list is empty or invalid format.", None
+            logger.info(f"Fetched {len(questions_data)} questions.")
+            return questions_data, 'success'
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Error fetching questions: {e}")
+            return f"Error fetching questions: {e}", None
+        except requests.exceptions.JSONDecodeError as e:
+            logger.error(f"Error decoding JSON response from questions endpoint: {e}")
+            logger.error(f"Response text: {response.text[:500]}")
+            return f"Error decoding server response for questions: {e}", None
+        except Exception as e:
+            logger.error(f"An unexpected error occurred fetching questions: {e}")
+            return f"An unexpected error occurred fetching questions: {e}", None
+
+
+    def get_random_question(self):
+        """
+        Fetches a random question from the API.
+        Returns:
+            A random question or an error message.
+        """
+        logger.info(f"Fetching a random question from: {self.random_question_url}")
+        try:
+            response = requests.get(self.random_question_url, timeout=15)
+            response.raise_for_status()
+            question_data = response.json()
+            if not question_data:
+                logger.warning("No random question data received.")
+                return "No random question data received.", None
+            logger.info(f"Received random question: {question_data.get('question', 'No question text')}")
+            return question_data, None
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Error fetching random question: {e}")
+            return f"Error fetching random question: {e}", None
+        
+    def get_file_task(self, task_id):
+        """
+        Fetches a file task by its ID from the API.
+        Args:
+            task_id (str): The ID of the task to fetch.
+        Returns:
+            The task data or an error message.
+        """
+        if not task_id:
+            return "Task ID cannot be empty.", None
+        _url = self.file_task_url.format(task_id)
+        logger.info(f"Fetching file task from: {_url}")
+        try:
+            response = requests.get(_url, timeout=15)
+            response.raise_for_status()
+
+            # Handle the case where the response is not JSON / text or bytes. Inspect the response object
+            content_type = response.headers.get("Content-Type", "")
+            if "application/json" in content_type:
+                task_data = response.json()
+            elif "text/" in content_type:
+                task_data = response.text
+            else:
+                # Assume it's a binary file (e.g., CSV, image, etc.)
+                task_data = response.content
+            if not task_data:
+                logger.warning(f"No data found for task ID: {task_id}")
+                return f"No data found for task ID: {task_id}", None
+            logger.info(f"Received file task for ID {task_id}: {task_data}")
+            return task_data, None
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Error fetching file task {task_id}: {e}")
+            return f"Error fetching file task {task_id}: {e}", None
+
+if __name__ == "__main__":
+    # Example usage
+    demo = gr.Blocks(title="GAIA HF API Client Demo")
+    api_client = GAIAHFAPIClient(profile=demo)
+    random_question, error = api_client.get_random_question()
+    if error:
+        logger.error(f"Error fetching random question: {error}")
+    else:
+        logger.info(f"Random question: {random_question}")
+
+    questions, error = api_client.get_questions()
+    if not error:
+        logger.error(f"Error fetching questions: {error}")
+    else:
+        logger.info(f"Fetched questions: {questions}")
+
+    task_id = 'f918266a-b3e0-4914-865d-4faa564f1aef'
+    task_data, error = api_client.get_file_task(task_id)
+    if error:
+        logger.error(f"Error fetching file task {task_id}: {error}")
+    else:
+        logger.info(f"Fetched file task: {task_data}")

app.py

@@ -1,193 +1,143 @@
 import os
+import logging
 import gradio as gr
-import requests
-import inspect
 import pandas as pd
-from agents import BasicAgent
+from api import GAIAHFAPIClient
+from agents.agent import BasicAgent, SimpleGeminiAgent
 
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-GAIA_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."
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S"
+)
+logger = logging.getLogger(__name__)
 
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 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)
-
+    api_client = GAIAHFAPIClient(profile=profile)  # Initialize the API client
+    agent = SimpleGeminiAgent()
     # 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
+    questions_data, error = api_client.get_questions()
+    if error is None or questions_data is None:
+        return questions_data, error
 
     # 3. Run your Agent
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
+    logger.info(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}")
+            logger.warning(f"Skipping item with missing task_id or question: {item}")
             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}",
-                }
-            )
-        break
+            logger.error(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.")
+        logger.warning("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)
+    # 4. Prepare Submission 
+    submission_data = {"username": api_client.username.strip(), "agent_code": api_client.agent_code, "answers": answers_payload}
+    logger.info(f"Agent finished. Submitting {len(answers_payload)} answers for user '{api_client.username}'...")
 
     # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    logger.info(f"Submitting {len(answers_payload)} answers to: {api_client.submit_url}")
+    api_client.submit(submission_data, results_log)
+
+def run_and_submit_one(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    api_client = GAIAHFAPIClient(profile=profile)  # Initialize the API client
+    agent = SimpleGeminiAgent()
+    # 2. Fetch Question
+    questions_data, error = api_client.get_questions()
+    if error is None or questions_data is None:
+        return questions_data, error
+    question_data = questions_data[0]
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    
+    task_id = question_data.get("task_id")
+    question_text = question_data.get("question")
+    if not task_id or question_text is None:
+        logger.warning(f"Skipping item with missing task_id or question: {question_data}")
     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
+        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:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        logger.error(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:
+        logger.warning("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-# --- 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.
+    # 4. Prepare Submission 
+    submission_data = {"username": api_client.username.strip(), "agent_code": api_client.agent_code, "answers": answers_payload}
+    logger.info(f"Agent finished. Submitting {len(answers_payload)} answers for user '{api_client.username}'...")
 
-        ---
-        **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.
-        """
-    )
+    # 5. Submit
+    logger.info(f"Submitting {len(answers_payload)} answers to: {api_client.submit_url}")
+    api_client.submit(submission_data, results_log)
+
+def build_gradio_interface():
+    # --- 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()
+        gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+        run_all_button = gr.Button("Run Evaluation & Submit All Answers")
+        run_one_button = gr.Button("Run a Single Evaluation")
 
-    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)
+        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_all_button.click(
+            fn=run_and_submit_all,
+            outputs=[status_output, results_table]
+        )
+        run_one_button.click(
+            fn=run_and_submit_one,
+            outputs=[status_output, results_table]
+        )
+    return demo
 
 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}")
@@ -195,18 +145,15 @@ 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 = build_gradio_interface()
+    demo.launch(debug=True, share=False)

requirements.txt

@@ -1,4 +1,4 @@
 gradio
 requests
-langchain-google-genai
-langgraph
+google-genai
+ratelimit