initial commit

agent.py

@@ -1,213 +0,0 @@
-"""LangGraph Agent"""
-import os
-from dotenv import load_dotenv
-from langgraph.graph import START, StateGraph, MessagesState
-from langgraph.prebuilt import tools_condition
-from langgraph.prebuilt import ToolNode
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_groq import ChatGroq
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-from langchain_community.vectorstores import SupabaseVectorStore
-from langchain_core.messages import SystemMessage, HumanMessage
-from langchain_core.tools import tool
-from langchain.tools.retriever import create_retriever_tool
-from supabase.client import Client, create_client
-
-load_dotenv()
-
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
-    Args:
-        a: first int
-        b: second int
-    """
-    return a * b
-
-@tool
-def add(a: int, b: int) -> int:
-    """Add two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a + b
-
-@tool
-def subtract(a: int, b: int) -> int:
-    """Subtract two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a - b
-
-@tool
-def divide(a: int, b: int) -> int:
-    """Divide two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    if b == 0:
-        raise ValueError("Cannot divide by zero.")
-    return a / b
-
-@tool
-def modulus(a: int, b: int) -> int:
-    """Get the modulus of two numbers.
-    
-    Args:
-        a: first int
-        b: second int
-    """
-    return a % b
-
-@tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia for a query and return maximum 2 results.
-    
-    Args:
-        query: The search query."""
-    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"wiki_results": formatted_search_docs}
-
-@tool
-def web_search(query: str) -> str:
-    """Search Tavily for a query and return maximum 3 results.
-    
-    Args:
-        query: The search query."""
-    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"web_results": formatted_search_docs}
-
-@tool
-def arvix_search(query: str) -> str:
-    """Search Arxiv for a query and return maximum 3 result.
-    
-    Args:
-        query: The search query."""
-    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"arvix_results": formatted_search_docs}
-
-
-
-# load the system prompt from the file
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
-
-# System message
-sys_msg = SystemMessage(content=system_prompt)
-
-# build a retriever
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
-supabase: Client = create_client(
-    os.environ.get("SUPABASE_URL"), 
-    os.environ.get("SUPABASE_SERVICE_KEY"))
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding= embeddings,
-    table_name="documents",
-    query_name="match_documents_langchain",
-)
-create_retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
-)
-
-
-
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    web_search,
-    arvix_search,
-]
-
-# Build graph function
-def build_graph(provider: str = "groq"):
-    """Build the graph"""
-    # Load environment variables from .env file
-    if provider == "google":
-        # Google Gemini
-        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-    elif provider == "groq":
-        # Groq https://console.groq.com/docs/models
-        llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
-    elif provider == "huggingface":
-        # TODO: Add huggingface endpoint
-        llm = ChatHuggingFace(
-            llm=HuggingFaceEndpoint(
-                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
-                temperature=0,
-            ),
-        )
-    else:
-        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
-    # Bind tools to LLM
-    llm_with_tools = llm.bind_tools(tools)
-
-    # Node
-    def assistant(state: MessagesState):
-        """Assistant node"""
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
-    
-    def retriever(state: MessagesState):
-        """Retriever node"""
-        similar_question = vector_store.similarity_search(state["messages"][0].content)
-        example_msg = HumanMessage(
-            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
-        )
-        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
-
-    builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
-    builder.add_edge(START, "retriever")
-    builder.add_edge("retriever", "assistant")
-    builder.add_conditional_edges(
-        "assistant",
-        tools_condition,
-    )
-    builder.add_edge("tools", "assistant")
-
-    # Compile graph
-    return builder.compile()
-
-# test
-if __name__ == "__main__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    # Build the graph
-    graph = build_graph(provider="groq")
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()

app.py

@@ -1,8 +1,217 @@
+# import os
+# import gradio as gr
+# import requests
+# import inspect
+# import pandas as pd
+
+# # (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):
+#     """
+#     Fetches all questions, runs the BasicAgent on them, submits all answers,
+#     and displays the results.
+#     """
+#     # --- Determine HF Space Runtime URL and Repo URL ---
+#     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+#     if profile:
+#         username= f"{profile.username}"
+#         print(f"User logged in: {username}")
+#     else:
+#         print("User not logged in.")
+#         return "Please Login to Hugging Face with the button.", None
+
+#     api_url = DEFAULT_API_URL
+#     questions_url = f"{api_url}/questions"
+#     submit_url = f"{api_url}/submit"
+
+#     # 1. Instantiate Agent ( modify this part to create your agent)
+#     try:
+#         agent = BasicAgent()
+#     except Exception as e:
+#         print(f"Error instantiating agent: {e}")
+#         return f"Error initializing agent: {e}", None
+#     # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+#     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+#     print(agent_code)
+
+#     # 2. Fetch Questions
+#     print(f"Fetching questions from: {questions_url}")
+#     try:
+#         response = requests.get(questions_url, timeout=15)
+#         response.raise_for_status()
+#         questions_data = response.json()
+#         if not questions_data:
+#              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)
+
+
 import os
 import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from smolagents import CodeAgent, HfApiModel, DuckDuckGoSearchTool, FinalAnswerTool
+import os
+import yaml
+from tools_agent import ReverseTextTool, TableCommutativityTool, VegetableListTool, ExcelSumFoodTool
+from smolagents import (
+    CodeAgent,
+    OpenAIServerModel,
+    DuckDuckGoSearchTool,
+    FinalAnswerTool,
+    PythonInterpreterTool
+)
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -10,14 +219,56 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+from smolagents import CodeAgent, HfApiModel
+import os
+from smolagents import HfApiModel
+
+
+# --- Agent Definition ---
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Initializing BasicAgent with tools...")
+
+        # Load OpenAI token from environment
+        openai_token = os.getenv("OPENAI_API_KEY")
+        if not openai_token:
+            raise ValueError("Missing OpenAI API token!")
+
+        # Initialize model and tools
+        model = OpenAIServerModel(
+            #api_base="openai",
+            api_key=openai_token,
+            model_id="gpt-4.1"
+        )
+        search_tool = DuckDuckGoSearchTool()
+        final_answer_tool = FinalAnswerTool()
+        reverse_tool = ReverseTextTool()
+        table_tool = TableCommutativityTool()
+        veg_tool = VegetableListTool()
+        python_tool = PythonInterpreterTool()
+        exfood_tool = ExcelSumFoodTool()
+
+        # Load system prompt templates
+        with open("prompts.yaml", "r") as stream:
+            prompt_templates = yaml.safe_load(stream)
+
+        # Build the agent
+        self.agent = CodeAgent(
+            model=model,
+            prompt_templates=prompt_templates,
+            tools=[search_tool, reverse_tool, table_tool, veg_tool, python_tool, exfood_tool], #final_answer_tool
+            add_base_tools=True,
+            planning_interval=None,
+            name = "GoodAgent",
+            max_steps=10,
+            verbosity_level=1,
+        )
+
     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
+        print(f"Agent received question (first 50 chars): {question}...")
+        answer = self.agent(question)
+        print(f"Agent returning answer: {answer}")
+        return answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -146,11 +397,9 @@ with gr.Blocks() as demo:
     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).

prompts.yaml

@@ -0,0 +1,312 @@
+"system_prompt": |-
+  You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
+  To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
+  To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.
+  At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.
+  Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '<end_code>' sequence.
+  During each intermediate step, you can use 'print()' to save whatever important information you will then need.
+  These print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.
+
+
+  Here are a few examples using notional tools:
+  ---
+  Task: "Generate an image of the oldest person in this document."
+
+  Thought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.
+  Code:
+  ```py
+  answer = document_qa(document=document, question="Who is the oldest person mentioned?")
+  print(answer)
+  ```<end_code>
+  Observation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."
+
+  Thought: I will now generate an image showcasing the oldest person.
+  Code:
+  ```py
+  image = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")
+  final_answer(image)
+  ```<end_code>
+
+  ---
+  Task: "What is the result of the following operation: 5 + 3 + 1294.678?"
+
+  Thought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool
+  Code:
+  ```py
+  result = 5 + 3 + 1294.678
+  final_answer(result)
+  ```<end_code>
+
+  ---
+  Task:
+  "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.
+  You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
+  {'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}"
+
+  Thought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.
+  Code:
+  ```py
+  translated_question = translator(question=question, src_lang="French", tgt_lang="English")
+  print(f"The translated question is {translated_question}.")
+  answer = image_qa(image=image, question=translated_question)
+  final_answer(f"The answer is {answer}")
+  ```<end_code>
+  ---
+  Task:
+  In a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.
+  What does he say was the consequence of Einstein learning too much math on his creativity, in one word?
+  Thought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.
+  Code:
+  ```py
+  pages = search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")
+  print(pages)
+  ```<end_code>
+  Observation:
+  No result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".
+
+  Thought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.
+  Code:
+  ```py
+  pages = search(query="1979 interview Stanislaus Ulam")
+  print(pages)
+  ```<end_code>
+  Observation:
+  Found 6 pages:
+  [Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)
+
+  [Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)
+
+  (truncated)
+
+  Thought: I will read the first 2 pages to know more.
+  Code:
+  ```py
+  for url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:
+      whole_page = visit_webpage(url)
+      print(whole_page)
+      print("\n" + "="*80 + "\n")  # Print separator between pages
+  ```<end_code>
+  Observation:
+  Manhattan Project Locations:
+  Los Alamos, NM
+  Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at
+  (truncated)
+
+  Thought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let's answer in one word.
+  Code:
+  ```py
+  final_answer("diminished")
+  ```<end_code>
+
+  ---
+  Task: "Which city has the highest population: Guangzhou or Shanghai?"
+
+  Thought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.
+  Code:
+  ```py
+  for city in ["Guangzhou", "Shanghai"]:
+      print(f"Population {city}:", search(f"{city} population")
+  ```<end_code>
+  Observation:
+  Population Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
+  Population Shanghai: '26 million (2019)'
+
+  Thought: Now I know that Shanghai has the highest population.
+  Code:
+  ```py
+  final_answer("Shanghai")
+  ```<end_code>
+
+  ---
+  Task: "What is the current age of the pope, raised to the power 0.36?"
+
+  Thought: I will use the tool `wiki` to get the age of the pope, and confirm that with a web search.
+  Code:
+  ```py
+  pope_age_wiki = wiki(query="current pope age")
+  print("Pope age as per wikipedia:", pope_age_wiki)
+  pope_age_search = web_search(query="current pope age")
+  print("Pope age as per google search:", pope_age_search)
+  ```<end_code>
+  Observation:
+  Pope age: "The pope Francis is currently 88 years old."
+
+  Thought: I know that the pope is 88 years old. Let's compute the result using python code.
+  Code:
+  ```py
+  pope_current_age = 88 ** 0.36
+  final_answer(pope_current_age)
+  ```<end_code>
+
+  Above example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools:
+  {%- for tool in tools.values() %}
+  - {{ tool.name }}: {{ tool.description }}
+      Takes inputs: {{tool.inputs}}
+      Returns an output of type: {{tool.output_type}}
+  {%- endfor %}
+
+  {%- if managed_agents and managed_agents.values() | list %}
+  You can also give tasks to team members.
+  Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'task', a long string explaining your task.
+  Given that this team member is a real human, you should be very verbose in your task.
+  Here is a list of the team members that you can call:
+  {%- for agent in managed_agents.values() %}
+  - {{ agent.name }}: {{ agent.description }}
+  {%- endfor %}
+  {%- else %}
+  {%- endif %}
+
+  Here are the rules you should always follow to solve your task:
+  1. Always provide a 'Thought:' sequence, and a 'Code:\n```py' sequence ending with '```<end_code>' sequence, else you will fail.
+  2. Use only variables that you have defined!
+  3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wiki({'query': "What is the place where James Bond lives?"})', but use the arguments directly as in 'answer = wiki(query="What is the place where James Bond lives?")'.
+  4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.
+  5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.
+  6. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.
+  7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.
+  8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}
+  9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.
+  10. Don't give up! You're in charge of solving the task, not providing directions to solve it.
+
+  Now Begin! If you solve the task correctly, you will receive a reward of $1,000,000.
+"planning":
+  "initial_facts": |-
+    Below I will present you a task.
+    You will now build a comprehensive preparatory survey of which facts we have at our disposal and which ones we still need.
+    To do so, you will have to read the task and identify things that must be discovered in order to successfully complete it.
+    Don't make any assumptions. For each item, provide a thorough reasoning. Here is how you will structure this survey:
+
+    ---
+    ### 1. Facts given in the task
+    List here the specific facts given in the task that could help you (there might be nothing here).
+
+    ### 2. Facts to look up
+    List here any facts that we may need to look up.
+    Also list where to find each of these, for instance a website, a file... - maybe the task contains some sources that you should re-use here.
+
+    ### 3. Facts to derive
+    List here anything that we want to derive from the above by logical reasoning, for instance computation or simulation.
+
+    Keep in mind that "facts" will typically be specific names, dates, values, etc. Your answer should use the below headings:
+    ### 1. Facts given in the task
+    ### 2. Facts to look up
+    ### 3. Facts to derive
+    Do not add anything else.
+
+  "initial_plan": |-
+    You are a world expert at making efficient plans to solve any task using a set of carefully crafted tools.
+    Now for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.
+    This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.
+    Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
+    After writing the final step of the plan, write the '\n<end_plan>' tag and stop there.
+
+    Here is your task:
+
+    Task:
+    ```
+    {{task}}
+    ```
+    You can leverage these tools:
+    {%- for tool in tools.values() %}
+    - {{ tool.name }}: {{ tool.description }}
+        Takes inputs: {{tool.inputs}}
+        Returns an output of type: {{tool.output_type}}
+    {%- endfor %}
+
+    {%- if managed_agents and managed_agents.values() | list %}
+    You can also give tasks to team members.
+    Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'request', a long string explaining your request.
+    Given that this team member is a real human, you should be very verbose in your request.
+    Here is a list of the team members that you can call:
+    {%- for agent in managed_agents.values() %}
+    - {{ agent.name }}: {{ agent.description }}
+    {%- endfor %}
+    {%- else %}
+    {%- endif %}
+
+    List of facts that you know:
+    ```
+    {{answer_facts}}
+    ```
+    Now begin! Write your plan below.
+  "update_facts_pre_messages": |-
+    You are a world expert at gathering known and unknown facts based on a conversation.
+    Below you will find a task, and a history of attempts made to solve the task. You will have to produce a list of these:
+    ### 1. Facts given in the task
+    ### 2. Facts that we have learned
+    ### 3. Facts still to look up
+    ### 4. Facts still to derive
+    Find the task and history below:
+  "update_facts_post_messages": |-
+    Earlier we've built a list of facts.
+    But since in your previous steps you may have learned useful new facts or invalidated some false ones.
+    Please update your list of facts based on the previous history, and provide these headings:
+    ### 1. Facts given in the task
+    ### 2. Facts that we have learned
+    ### 3. Facts still to look up
+    ### 4. Facts still to derive
+    Now write your new list of facts below.
+  "update_plan_pre_messages": |-
+    You are a world expert at making efficient plans to solve any task using a set of carefully crafted tools.
+    You have been given a task:
+    ```
+    {{task}}
+    ```
+
+    Find below the record of what has been tried so far to solve it. Then you will be asked to make an updated plan to solve the task.
+    If the previous tries so far have met some success, you can make an updated plan based on these actions.
+    If you are stalled, you can make a completely new plan starting from scratch.
+  "update_plan_post_messages": |-
+    You're still working towards solving this task:
+    ```
+    {{task}}
+    ```
+    You can leverage these tools:
+    {%- for tool in tools.values() %}
+    - {{ tool.name }}: {{ tool.description }}
+        Takes inputs: {{tool.inputs}}
+        Returns an output of type: {{tool.output_type}}
+    {%- endfor %}
+
+    {%- if managed_agents and managed_agents.values() | list %}
+    You can also give tasks to team members.
+    Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'task'.
+    Given that this team member is a real human, you should be very verbose in your task, it should be a long string providing informations as detailed as necessary.
+    Here is a list of the team members that you can call:
+    {%- for agent in managed_agents.values() %}
+    - {{ agent.name }}: {{ agent.description }}
+    {%- endfor %}
+    {%- else %}
+    {%- endif %}
+
+    Here is the up to date list of facts that you know:
+    ```
+    {{facts_update}}
+    ```
+
+    Now for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.
+    This plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.
+    Beware that you have {remaining_steps} steps remaining.
+    Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
+    After writing the final step of the plan, write the '\n<end_plan>' tag and stop there.
+
+    Now write your new plan below.
+"managed_agent":
+  "task": |-
+    You're a helpful agent named '{{name}}'.
+    You have been submitted this task by your manager.
+    ---
+    Task:
+    {{task}}
+    ---
+    You're helping your manager solve a wider task: so make sure to not provide a one-line answer, but give as much information as possible to give them a clear understanding of the answer.
+    Your final_answer WILL HAVE to contain only the pure answer to the given task. For example if asked how many then answer with a certain number. Same with other tasks:
+    ### 1. Task outcome (only the pure answer to the task)
+
+    Put all these in your final_answer tool, everything that you do not pass as an argument to final_answer will be lost.
+    And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.
+  "report": |-
+    {{final_answer}}
+"final_answer":
+  "pre_messages": ""
+  "post_messages": ""

requirements.txt

@@ -1,18 +1,5 @@
 gradio
 requests
-langchain
-langchain-community
-langchain-core
-langchain-google-genai
-langchain-huggingface
-langchain-groq
-langchain-tavily
-langchain-chroma
-langgraph
-huggingface_hub
-supabase
-arxiv
-pymupdf
-wikipedia
-pgvector
-python-dotenv
+smolagents==1.13.0
+pandas
+smolagents[openai]

system_prompt.txt

@@ -1,5 +0,0 @@
-You are a helpful assistant tasked with answering questions using a set of tools. 
-Now, 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.
-Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 

tools_agent.py

@@ -0,0 +1,88 @@
+import pandas as pd
+from smolagents import Tool
+from typing import Any, Dict, Optional
+
+class ReverseTextTool(Tool):
+    name = "reverse_text"
+    description = "Reverses the input text."
+    # tell the validator: I’m expecting a dict with key "text"
+    inputs = {"input": {"type": "any", "description": "The text to be reversed"}}
+    output_type = "string"
+
+    def forward(self, input: Any) -> Any:
+        return input[::-1]
+
+
+class TableCommutativityTool(Tool):
+    name = "find_non_commutative_elements"
+    description = (
+        "Given a multiplication table (2D list) and its header elements, "
+        "returns the elements involved in any a*b != b*a."
+    )
+    inputs = {
+        "input": {
+            "type": "any",
+            "description": "Dict with keys 'table' (list of lists) and 'elements' (list of strings)."
+        }
+    }
+    output_type = "string"
+
+    def forward(self, input: dict) -> list[str]:
+        table   = input["table"]
+        elements = input["elements"]
+        non_comm = set()
+        for i, a in enumerate(elements):
+            for j, b in enumerate(elements):
+                if table[i][j] != table[j][i]:
+                    non_comm.update({a, b})
+        return str(sorted(non_comm))
+
+
+
+class VegetableListTool(Tool):
+    name = "list_vegetables"
+    description = (
+        "From a list of grocery items, returns those that are true vegetables "
+        "(botanical definition), sorted alphabetically."
+    )
+    inputs = {
+        "input": {
+            "type": "any",
+            "description": "Dict with key 'items' containing a list of item strings."
+        }
+    }
+    output_type = "string"
+
+    _VEG_SET = {
+        "broccoli", "bell pepper", "celery", "corn",
+        "green beans", "lettuce", "sweet potatoes", "zucchini"
+    }
+
+    def forward(self, input: Any) -> Any:
+        items = input["items"]
+        return str(sorted(item for item in items if item in self._VEG_SET))
+
+
+class ExcelSumFoodTool(Tool):
+    name = "sum_food_sales"
+    description = (
+        "Reads an Excel file with columns 'Category' and 'Sales', "
+        "and returns total sales where Category != 'Drink', rounded to two decimals."
+    )
+    inputs = {
+        "input": {
+            "type": "any",
+            "description": "Dict with key 'excel_path' pointing to the .xlsx file to read."
+        }
+    }
+    output_type = "string"
+
+    def forward(self, input: Any) -> Any:
+        excel_path = input["excel_path"]
+        df = pd.read_excel(excel_path)
+        total = df.loc[df["Category"] != "Drink", "Sales"].sum()
+        return str(round(float(total), 2))
+
+
+
+