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.gitignore

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+.venv
+.env

README.md

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----
-title: Template Final Assignment
-emoji: 🕵🏻‍♂️
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.25.2
-app_file: app.py
-pinned: false
-hf_oauth: true
-# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
-hf_oauth_expiration_minutes: 480
----
-
+---
+title: Template Final Assignment
+emoji: 🕵🏻‍♂️
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.25.2
+app_file: app.py
+pinned: false
+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

agent.py

@@ -0,0 +1,102 @@
+# Libs
+import os
+import yaml
+import importlib
+
+from smolagents import (
+    CodeAgent,
+    DuckDuckGoSearchTool,
+    VisitWebpageTool,
+    WikipediaSearchTool,
+    OpenAIServerModel,
+    SpeechToTextTool,
+    FinalAnswerTool,
+)
+
+# Local
+from tools import GetTaskFileTool, LoadXlsxFileTool, LoadTextFileTool
+
+sys_instruction = (
+    "You are a general AI assistant. Answer each question by reporting your thoughts, "
+    "then submit ONLY a concise text using the 'final_answer' tool. "
+    "Final answer MUST be a number, a few words, or a comma-separated list of numbers and/or strings. "
+    "For numbers, avoid commas and units unless specified. For strings, avoid articles and abbreviations, "
+    "and write digits in full unless stated otherwise. Apply these rules for list elements as well."
+)
+
+prompts = yaml.safe_load(
+    importlib.resources.files("smolagents.prompts").joinpath("code_agent.yaml").read_text()
+)
+prompts["system_prompt"] = sys_instruction + prompts["system_prompt"]
+
+# req_instruction = (
+req_instruction = (
+    "You are an expert and helpful agent named {{name}}.\n"
+    "A valued client has assigned you the following task:\n"
+    "---\n"
+    "Task:\n"
+    "{{task}}\n"
+    "---\n"
+    "Use your tools as needed. Before completing the task, plan your actions carefully.\n"
+    "While completing the task, think step by step. And after completing the task, carefully double check your solution.\n\n"
+    "If you respond correctly, you will be rewarded with a very high bonus.\n\n"
+    "Your final_answer MUST be:\n"
+    "- a number,\n"
+    "- a short phrase,\n"
+    "- or a comma-separated list of numbers or strings (no articles or abbreviations).\n\n"
+    "Only the content passed to the final_answer tool will be preserved—everything else will be discarded."
+)
+prompts['managed_agent']['task'] = req_instruction
+prompts['managed_agent']['report'] = "{{final_answer}}"
+
+# print(prompts["system_prompt"])
+# print(prompts['planning'])
+# print(prompts['managed_agent'])
+# print(prompts['final_answer'])
+
+def get_model(
+        model_id: str = "gpt-4.1-mini",
+        model_temperature: float = 0.7,
+    ):
+    """
+    Create and return an OpenAIServerModel instance with the specified model ID and temperature.
+    """
+    # Load the model
+    model = OpenAIServerModel(
+            model_id=model_id,
+            api_key=os.getenv("OPENAI_API_KEY"),
+            temperature=model_temperature
+    )
+    return model
+
+def get_agent(
+        model_id: str = "gpt-4.1-mini",
+        model_temperature: float = 0.7,
+        agent_max_steps: int = 15,
+    ):
+    """
+    Create and return a CodeAgent instance with the specified model and tools.
+    """
+
+    # Defne the agent with the tools
+    agent = CodeAgent(
+        tools=[
+            DuckDuckGoSearchTool(),
+            VisitWebpageTool(),
+            WikipediaSearchTool(),
+            GetTaskFileTool(),
+            SpeechToTextTool(),
+            LoadXlsxFileTool(),
+            LoadTextFileTool(),
+            FinalAnswerTool(),
+        ],
+        model=get_model(
+            model_id=model_id,
+            model_temperature=model_temperature,
+        ),
+        prompt_templates=prompts,
+        max_steps=agent_max_steps,
+        additional_authorized_imports = ["pandas"],
+        name="GAIAAgent",
+    )
+    return agent

app.py

@@ -1,265 +1,185 @@
-import os
-import gradio as gr
-import requests
-import pandas as pd
-import yaml
-import importlib
-
-from smolagents import (
-    CodeAgent,
-    DuckDuckGoSearchTool,
-    VisitWebpageTool,
-    WikipediaSearchTool,
-    Tool,
-    OpenAIServerModel,
-    SpeechToTextTool,
-    FinalAnswerTool,
-)
-
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-class GetTaskFileTool(Tool):
-    name = "get_task_file_tool"
-    description = """This tool downloads the file content associated with the given task_id if exists. Returns absolute file path"""
-    inputs = {
-        "task_id": {"type": "string", "description": "Task id"},
-        "file_name": {"type": "string", "description": "File name"},
-    }
-    output_type = "string"
-
-    def forward(self, task_id: str, file_name: str) -> str:
-        response = requests.get(f"{DEFAULT_API_URL}/files/{task_id}", timeout=15)
-        response.raise_for_status()
-        with open(file_name, 'wb') as file:
-            file.write(response.content)
-        return os.path.abspath(file_name)
-
-class LoadXlsxFileTool(Tool):
-    name = "load_xlsx_file_tool"
-    description = """This tool loads xlsx file into pandas and returns it"""
-    inputs = {
-        "file_path": {"type": "string", "description": "File path"}
-    }
-    output_type = "object"
-
-    def forward(self, file_path: str) -> object:
-        return pd.read_excel(file_path)
-
-class LoadTextFileTool(Tool):
-    name = "load_text_file_tool"
-    description = """This tool loads any text file"""
-    inputs = {
-        "file_path": {"type": "string", "description": "File path"}
-    }
-    output_type = "string"
-
-    def forward(self, file_path: str) -> object:
-        with open(file_path, 'r', encoding='utf-8') as file:
-            return file.read()
-
-prompts = yaml.safe_load(
-    importlib.resources.files("smolagents.prompts").joinpath("code_agent.yaml").read_text()
-)
-prompts["system_prompt"] = ("You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer using the 'final_answer' tool. Your final answer MUST 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. "
-                            + prompts["system_prompt"])
-print(prompts["system_prompt"])
-
-def init_agent():
-    model = OpenAIServerModel(
-            model_id="gpt-4.1",
-            api_key=os.getenv("OPENAI_API_KEY"),
-            temperature=0.7
-    )
-    agent = CodeAgent(
-        tools=[
-            DuckDuckGoSearchTool(),
-            VisitWebpageTool(),
-            WikipediaSearchTool(),
-            GetTaskFileTool(),
-            SpeechToTextTool(),
-            LoadXlsxFileTool(),
-            LoadTextFileTool(),
-            FinalAnswerTool(),
-        ],
-        model=model,
-        prompt_templates=prompts,
-        max_steps=15,
-        additional_authorized_imports = ["pandas"]
-    )
-    return agent
-
-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 = init_agent()
-    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:
-             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
-
-    # 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}")
-            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}")
-    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
-
-    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...")
+# Libs
+import os
+import gradio as gr
+import requests
+import pandas as pd
+
+# Local
+from consts import DEFAULT_API_URL, MODEL_ID
+from agent import get_agent
+
+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 = get_agent(model_id=MODEL_ID, model_temperature=0.7, agent_max_steps=15)
+    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:
+             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
+
+    # 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}")
+            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}")
+    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
+
+    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)

consts.py

@@ -0,0 +1,2 @@
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+MODEL_ID = "gpt-4.1"

requirements.txt

@@ -1,10 +1,10 @@
-gradio
-requests
-smolagents
-wikipedia-api
-smolagents[openai]
-pyyaml[resolver]
-transformers
-smolagents[transformers]
-smolagents[audio]
+gradio
+requests
+smolagents
+wikipedia-api
+smolagents[openai]
+pyyaml[resolver]
+transformers
+smolagents[transformers]
+smolagents[audio]
 openpyxl

tools.py

@@ -0,0 +1,47 @@
+# Libs
+import os
+import requests
+import pandas as pd
+from smolagents import Tool
+
+# Local
+from consts import DEFAULT_API_URL
+
+class GetTaskFileTool(Tool):
+    name = "get_task_file_tool"
+    description = """This tool downloads the file content associated with the given task_id if exists. Returns absolute file path"""
+    inputs = {
+        "task_id": {"type": "string", "description": "Task id"},
+        "file_name": {"type": "string", "description": "File name"},
+    }
+    output_type = "string"
+
+    def forward(self, task_id: str, file_name: str) -> str:
+        response = requests.get(f"{DEFAULT_API_URL}/files/{task_id}", timeout=15)
+        response.raise_for_status()
+        with open(file_name, 'wb') as file:
+            file.write(response.content)
+        return os.path.abspath(file_name)
+
+class LoadXlsxFileTool(Tool):
+    name = "load_xlsx_file_tool"
+    description = """This tool loads xlsx file into pandas and returns it"""
+    inputs = {
+        "file_path": {"type": "string", "description": "File path"}
+    }
+    output_type = "object"
+
+    def forward(self, file_path: str) -> object:
+        return pd.read_excel(file_path)
+
+class LoadTextFileTool(Tool):
+    name = "load_text_file_tool"
+    description = """This tool loads any text file"""
+    inputs = {
+        "file_path": {"type": "string", "description": "File path"}
+    }
+    output_type = "string"
+
+    def forward(self, file_path: str) -> object:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            return file.read()