commit

.gitignore

@@ -3,3 +3,4 @@
 /questions.json
 /venv/
 /.idea/
+/questions/

app.py

@@ -1,13 +1,9 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
-from smolagents import CodeAgent, tool, InferenceClientModel, WebSearchTool, load_tool, PromptTemplates, Tool, FinalAnswerTool
-from dotenv import load_dotenv
-from my_prompt_config import PromptConfig
-from my_reverse_string import ReverseStringTool, ImageLoadTool
-from PIL import Image
+from my_agent import MyAgent
+
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -16,98 +12,29 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # testing --------------------------------------------
 testing_mode = True
 questions_to_run = [
-    #"8e867cd7-cff9-4e6c-867a-ff5ddc2550be",     # OK
-    #"a1e91b78-d3d8-4675-bb8d-62741b4b68a6"
-    #"2d83110e-a098-4ebb-9987-066c06fa42d0"      # almost OK
-    "cca530fc-4052-43b2-b130-b30968d8aa44"
-    #"4fc2f1ae-8625-45b5-ab34-ad4433bc21f8"
-    #"6f37996b-2ac7-44b0-8e68-6d28256631b4"
-    #"9d191bce-651d-4746-be2d-7ef8ecadb9c2"
-    #"cabe07ed-9eca-40ea-8ead-410ef5e83f91"
-    #"3cef3a44-215e-4aed-8e3b-b1e3f08063b7"
-    #"99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3"
-    #"305ac316-eef6-4446-960a-92d80d542f82"
-    #"f918266a-b3e0-4914-865d-4faa564f1aef"
-    #"3f57289b-8c60-48be-bd80-01f8099ca449"
-    #"1f975693-876d-457b-a649-393859e79bf3"
-    #"840bfca7-4f7b-481a-8794-c560c340185d"
-    #"bda648d7-d618-4883-88f4-3466eabd860e"
-    #"cf106601-ab4f-4af9-b045-5295fe67b37d"
-    #"a0c07678-e491-4bbc-8f0b-07405144218f"
-    #"7bd855d8-463d-4ed5-93ca-5fe35145f733"
-    #"5a0c1adf-205e-4841-a666-7c3ef95def9d"
+    #"8e867cd7-cff9-4e6c-867a-ff5ddc2550be",     # good answer, wrong albums
+    #"a1e91b78-d3d8-4675-bb8d-62741b4b68a6",
+    #"2d83110e-a098-4ebb-9987-066c06fa42d0",     # OK
+    #"cca530fc-4052-43b2-b130-b30968d8aa44",     # OK
+    #"4fc2f1ae-8625-45b5-ab34-ad4433bc21f8",
+    #"6f37996b-2ac7-44b0-8e68-6d28256631b4",
+    #"9d191bce-651d-4746-be2d-7ef8ecadb9c2",
+    #"cabe07ed-9eca-40ea-8ead-410ef5e83f91",
+    #"3cef3a44-215e-4aed-8e3b-b1e3f08063b7",
+    #"99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3",
+    #"305ac316-eef6-4446-960a-92d80d542f82",
+    #"f918266a-b3e0-4914-865d-4faa564f1aef",
+    #"3f57289b-8c60-48be-bd80-01f8099ca449",
+    #"1f975693-876d-457b-a649-393859e79bf3",
+    #"840bfca7-4f7b-481a-8794-c560c340185d",
+    #"bda648d7-d618-4883-88f4-3466eabd860e",
+    #"cf106601-ab4f-4af9-b045-5295fe67b37d",
+    #"a0c07678-e491-4bbc-8f0b-07405144218f",
+    #"7bd855d8-463d-4ed5-93ca-5fe35145f733",
+    #"5a0c1adf-205e-4841-a666-7c3ef95def9d",
 ]
 # testing --------------------------------------------
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WHERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    #MODEL_CODER = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    MODEL_CODER = "Qwen/Qwen2.5-72B-Instruct"
-    #MODEL_REASONING = "deepseek-ai/DeepSeek-R1"
-    MODEL_REASONING = "Qwen/Qwen2.5-72B-Instruct"
-
-    def __init__(self):
-        load_dotenv()
-        print("Agent initialized.")
-        self.__create_agents__()
-
-    def __create_agents__(self):
-        web_search_agent = CodeAgent(
-            tools=[WebSearchTool()],
-            model=InferenceClientModel(model_id=self.MODEL_CODER),
-            name="agent_websearch",
-            description="Agent to browse and search and extract web content"
-        )
-
-        #reverse_agent = CodeAgent(
-        #    tools=[ReverseStringTool()],
-        #    model=InferenceClientModel(model_id=self.MODEL_CODER),
-        #    name="agent_reverse",
-        #    description="Agent to reverse strings"
-        #)
-
-        # self.image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True)
-        image_generation_tool = Tool.from_space(
-            "black-forest-labs/FLUX.1-schnell",
-            name="image_generator",
-            description="Generate an image from a prompt"
-        )
-
-        image_captioning_tool = Tool.from_space(
-            "ovi054/image-to-prompt",
-            name="image_captioning",
-            description="Generate description of an image"
-        )
-
-        image_loading_tool = ImageLoadTool()
-        print(f"Image load tool: {image_loading_tool}")
-
-        #image_generation_agent = CodeAgent(
-        #    tools=[image_generation_tool],
-        #    model=InferenceClientModel(model_id=self.MODEL_CODER)
-        #)
-
-        # ImageLoadTool()
-        self.reasoning_agent = CodeAgent(
-            #tools=[image_generation_tool, image_captioning_tool, ReverseStringTool(), image_loading_tool],
-            tools=[image_loading_tool, FinalAnswerTool()],
-            model=InferenceClientModel(model_id=self.MODEL_REASONING),
-            planning_interval=3, # This is where you activate planning!,
-            prompt_templates=PromptConfig().PROMPT_TEMPLATES,
-            managed_agents=[web_search_agent],
-            additional_authorized_imports=["PIL","chess","my_tools","my_tools."],
-        )
-        print(f"Main agent initialized: {self.reasoning_agent}")
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-
-        answer = self.reasoning_agent.run(question)
-
-        print(f"Agent returning answer: {answer}")
-        return answer
-
 
 # -----------------------------------------------------------------------------
 #
@@ -122,7 +49,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -134,7 +61,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = MyAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -184,8 +111,8 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             if question_file_name is not None:
                 ext = question_file_name[-4:]
                 if ext == ".png":
-                    question_text = question_text + (f" . Use available tool to load an image associated with task id: "
-                                                     f"{task_id}")
+                    question_text = question_text + (f" Use one of available tools to load an image associated with "
+                                                     f"task_id: '{task_id}'")
 
             submitted_answer = agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})

my_agent.py

@@ -0,0 +1,127 @@
+from smolagents import CodeAgent, InferenceClientModel, WebSearchTool, FinalAnswerTool
+#from smolagents import ApiModel
+#from mistralai import Mistral
+import os
+# --------- my tools
+from my_tool_reverse_string import ReverseStringTool
+from my_tool_image_load import ImageLoadTool
+from my_tool_chess_board import ChessBoard
+from my_tool_fen import FENTool
+from my_tool_chess_analysis import ChessAnalysisTool
+from my_prompt_config import MyPromptConfig
+from dotenv import load_dotenv
+
+# https://huggingface.co/docs/transformers/model_doc/mistral?usage=Pipeline
+
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WHERE YOU CAN BUILD WHAT YOU WANT ------
+class MyAgent:
+    #MODEL_REASONING = "Qwen/Qwen2.5-Coder-32B-Instruct"
+    #MODEL_REASONING = "deepseek-ai/DeepSeek-R1"
+    MODEL_REASONING = "Qwen/Qwen3-235B-A22B"        # test more
+    #MODEL_REASONING = "mistralai/Mixtral-8x22B-v0.1"    # not available via HuggingFace
+    chess_board_model_name = "my_chess_pieces_recognition.pth"
+    chess_board_model_dir = "/mnt/c/Users/krzsa/IdeaProjects/Agents-Course-Assignment/saved_models"
+
+    def __init__(self):
+        print("Agent initialized.")
+        load_dotenv()
+        self.mistral_api_key = os.environ["MISTRAL_API_KEY"]
+        self.__create_agents__()
+
+    def __create_agents__(self):
+        # --- Mistral -----------------------------------------------------------------------------------
+        # will need class implementation based on ApiModel
+        # https://github.com/huggingface/smolagents/blob/main/src/smolagents/models.py
+        # https://github.com/mistralai/client-python/tree/main/examples
+        #self.mistral_client = Mistral(api_key=self.mistral_api_key)
+        #self.model = ApiModel(
+        #    model_id="mistral-large-2411",
+        #    client=self.mistral_client
+        #)
+        # --- Mistral -----------------------------------------------------------------------------------
+
+        # --- HF Inference ------------------------------------------------------------------------------
+        self.model = InferenceClientModel(model_id=self.MODEL_REASONING)
+        # --- HF Inference ------------------------------------------------------------------------------
+
+        self.reasoning_agent = CodeAgent(
+            name="CourseAssistant",
+            description="General AI Assistant",
+            tools=[
+                ImageLoadTool(),
+                ReverseStringTool(),
+                ChessBoard(self.chess_board_model_name, self.chess_board_model_dir),
+                FENTool(),
+                ChessAnalysisTool(),
+                WebSearchTool(),
+                FinalAnswerTool(),
+            ],
+            model=self.model,
+            planning_interval=3, # This is where you activate planning!,
+            prompt_templates=MyPromptConfig.PROMPT_TEMPLATES,
+            managed_agents=[],
+            additional_authorized_imports=[
+                "PIL",
+                "chess",
+                "matplotlib",
+                "matplotlib.pyplot",
+                "stockfish",
+                "requests",
+                "wikipediaapi",
+                "bs4",
+                "my_tool_chess_analysis",
+                "my_tool_chess_board",
+                "my_tool_fen",
+                "my_tool_image_load",
+                "my_tool_reverse_string",
+            ],
+        )
+        #web_search_agent = CodeAgent(
+        #    tools=[WebSearchTool()],
+        #    model=InferenceClientModel(model_id=self.MODEL_CODER),
+        #    name="agent_websearch",
+        #    description="Agent to browse and search and extract web content"
+        #)
+
+        # self.image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True)
+        #image_generation_tool = Tool.from_space(
+        #    "black-forest-labs/FLUX.1-schnell",
+        #    name="image_generator",
+        #    description="Generate an image from a prompt"
+        #)
+
+        #image_captioning_tool = Tool.from_space(
+        #    "ovi054/image-to-prompt",
+        #    name="image_captioning",
+        #    description="Generate description of an image"
+        #)
+
+        #image_loading_tool = ImageLoadTool()
+        #print(f"Image load tool: {image_loading_tool}")
+
+        #image_generation_agent = CodeAgent(
+        #    tools=[image_generation_tool],
+        #    model=InferenceClientModel(model_id=self.MODEL_CODER)
+        #)
+
+        # ImageLoadTool()
+        #self.reasoning_agent = CodeAgent(
+        #    #tools=[image_generation_tool, image_captioning_tool, ReverseStringTool(), image_loading_tool],
+        #    tools=[image_loading_tool, FinalAnswerTool()],
+        #    model=InferenceClientModel(model_id=self.MODEL_REASONING),
+        #    planning_interval=3, # This is where you activate planning!,
+        #    prompt_templates=PromptConfig().PROMPT_TEMPLATES,
+        #    managed_agents=[web_search_agent],
+        #    additional_authorized_imports=["PIL","chess","my_tools","my_tools."],
+        #)
+        print(f"Main agent initialized: {self.reasoning_agent}")
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+
+        answer = self.reasoning_agent.run(question)
+
+        print(f"Agent returning answer: {answer}")
+        return answer

my_prompt_config.py

@@ -1,17 +1,51 @@
 from smolagents import PromptTemplates, PlanningPromptTemplate, FinalAnswerPromptTemplate, ManagedAgentPromptTemplate
 
+#             Additional tools provided :
+#             * _my_tool_chess_analysis : analyze chess position provided in FEN notation and provide best move,
+#                 available arguments: fen, player_color
+#             * _my_tool_chess_board : process an image and extract list of chess pieces, available arguments: img ,
+#             * _my_tool_fen : convert list of chess pieces into FEN notation, available arguments: chest_pieces ,
+#             * _my_tool_image_load : load an image for given task_id, available arguments: task_id ,
+#
+#             Use <code></code> examples as here
+#             https://github.com/huggingface/smolagents/blob/main/src/smolagents/vision_web_browser.py
+#
+#             * _my_tool_ reverse_string : reverse a string, available arguments: input_str ,
 
-class PromptConfig:
+class MyPromptConfig:
     PROMPT_TEMPLATES = PromptTemplates(
         system_prompt="""
-            You are a general AI assistant. I will ask you a question. Report your thoughts, and finish
-            your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
-            Describe your initial plan as a set of bullet points.
+            You are a general AI assistant. 
+            
+            Answer the following questions as best you can.
+            
+            Describe your initial plan as a set of bullet points. 
             Each bullet point should describe in one sentence an action which is to be taken in this step.
-            Use the tools provided. If you are going to use a tool, describe in detail why you are going
-            to use that particular tool and explain parameters used to invoke the tool.
-            Analyze the question provided.
-            Describe each step which needs to be taken to answer it.
+            Do NOT provide hypothetical examples for the final answer.
+            
+            Use the tools provided, and only those which are necessary to answer the question. 
+            If you are going to use a tool, describe in detail how you are going
+            to invoke the tool and explain parameters used to invoke the tool.
+            
+            The list of tools available:
+            
+            * _my_tool_chess_analysis : analyze chess position provided as FEN notation
+            * _my_tool_chess_board : extract list of chess pieces from chess board image
+            * _my_tool_fen : convert list of chess pieces into FEN notation
+            * _my_tool_image_load : load image of the chess board for given task_id
+            * _my_tool_reverse_string : reverse provided string
+            
+            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.
+            
+            Report your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
+            
         """,
         planning=PlanningPromptTemplate(
             initial_plan="""
@@ -30,12 +64,12 @@ class PromptConfig:
             post_messages="""
                 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
+                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
             """
         ),
     )

my_chess_analysis_tool.py → my_tool_chess_analysis.py

@@ -1,5 +1,6 @@
 from smolagents import Tool
 from stockfish import Stockfish
+import traceback
 
 
 #AUTHORIZED_TYPES = [
@@ -19,9 +20,13 @@ from stockfish import Stockfish
 
 
 class ChessAnalysisTool(Tool):
-    name = "_my_chess_analysis"
+    name = "_my_tool_chess_analysis"
     description = """
-        Analyze chess board provided in FEN notation and provide best move for given color
+        Analyze chess position provided as FEN notation and answer with best move for player color
+        To invoke the tool use code as below
+        <code>
+        best_answer = _my_tool_chess_analysis(fen=loaded_fen, player_color="dummy")
+        </code>
     """
 
     inputs = {
@@ -37,15 +42,28 @@ class ChessAnalysisTool(Tool):
 
     output_type = "string"
 
+    is_initialized = True
+
     def __init__(self):
+        print(f"***KS*** Chess analysis tool initializing ...")
         self.engine = Stockfish()
+        print(f"***KS*** Chess analysis tool initialized with engine: {self.engine}")
 
+    def initialize(self):
+        print(f"***KS*** Chess analysis tool initialized (2) with engine: {self.engine}")
 
     def forward(self, fen: str, player_color: str) -> str:
-        self.engine.set_fen_position(fen)
-        board = self.engine.get_board_visual()
-        print(f"{board}")
-        top_3_moves = self.engine.get_top_moves(3)
-        print(f"Top 3 moves:\n{top_3_moves}")
-        best_move = self.engine.get_best_move()
-        return best_move
+        print(f"***KS*** Chess analysis tool getting best move for player: {player_color} with position: {fen}")
+        best_move = ""
+        try:
+            self.engine.set_fen_position(fen)
+            board = self.engine.get_board_visual()
+            print(f"{board}")
+            top_3_moves = self.engine.get_top_moves(3)
+            print(f"Top 3 moves:\n{top_3_moves}")
+            best_move = self.engine.get_best_move()
+        except Exception as ex:
+            print(traceback.format_exc())
+            print(f"***KS*** Exception invoking ChessAnalysisTool: {ex}")
+
+        return f"FINAL ANSWER: {best_move}"

my_chess_board_tool.py → my_tool_chess_board.py

@@ -10,9 +10,13 @@ import traceback
 
 # Based on; https://github.com/kratos606/chessboard-recogniser/tree/main
 class ChessBoard(Tool):
-    name = "_my_chess_board"
+    name = "_my_tool_chess_board"
     description = """
-        Process an image representing a chess board and return board position as a list of chess pieces
+        Process an image of a chess board and return board position as a list of chess pieces
+        To invoke the tool use code as below
+        <code>
+        chess_pieces = _my_tool_chess_board(img=loaded_image)
+        </code>
     """
 
     inputs = {

my_fen_tool.py → my_tool_fen.py

@@ -21,13 +21,17 @@ import textwrap
 
 
 class FENTool(Tool):
-    name = "_my_fen_tool"
+    name = "_my_tool_fen"
     description = """
-        Convert a bord provided a s alist of chest pieces into FEN notation
+        Convert a list of chess pieces into FEN notation
+        To invoke the tool use code as below
+        <code>
+        loaded_fen = _my_tool_fen(chest_pieces="dummy")
+        </code>
     """
 
     inputs = {
-        "_inp": {
+        "chest_pieces": {
             "type": "string",
             "description": "string with chest pieces",
         }
@@ -41,8 +45,8 @@ class FENTool(Tool):
             .replace('111111','6').replace('11111','5') \
             .replace('1111','4').replace('111','3').replace('11','2')
 
-    def forward(self, _inp: str) -> str:
-        arr = textwrap.wrap(_inp, 8)
+    def forward(self, chest_pieces: str) -> str:
+        arr = textwrap.wrap(chest_pieces, 8)
         #print(f"Arr: \n{arr}")
 
         arr = arr[::-1]

my_image_load.py → my_tool_image_load.py

@@ -18,9 +18,13 @@ from io import BytesIO
 
 
 class ImageLoadTool(Tool):
-    name = "_my_image_load"
+    name = "_my_tool_image_load"
     description = """
         Load image for the provided task id
+        To invoke the tool use code as below
+        <code>
+        loaded_image = _my_tool_image_load(task_id="dummy")
+        </code>
     """
 
     inputs = {

my_reverse_string.py → my_tool_reverse_string.py

@@ -16,13 +16,17 @@ from smolagents import Tool
 
 
 class ReverseStringTool(Tool):
-    name = "_my_reverse_string"
+    name = "_my_tool_reverse_string"
     description = """
-        Decode a string which is provided in a reversed form.
+        Decode a string which is provided in a reversed form
+        To invoke the tool use code as below
+        <code>
+        reversed_string = _my_tool_reverse_string(input_str="dummy")
+        </code>
     """
 
     inputs = {
-        "_inp": {
+        "input_str": {
             "type": "string",
             "description": "encoded input string",
         }
@@ -30,8 +34,8 @@ class ReverseStringTool(Tool):
 
     output_type = "string"
 
-    def forward(self, _inp: str) -> str:
+    def forward(self, input_str: str) -> str:
         _out = ""
-        for a in _inp:
+        for a in input_str:
             _out = a + _out
         return _out

requirements.txt

@@ -7,4 +7,8 @@ matplotlib
 PyQt6
 opencv-python
 torch
-stockfish
+stockfish
+wikipedia-api
+bs4
+mistral-inference
+mistralai

simple.py

@@ -1,25 +1,20 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
 from smolagents import CodeAgent, tool, InferenceClientModel, WebSearchTool, load_tool, PromptTemplates, Tool, FinalAnswerTool
 from smolagents import PromptTemplates, PlanningPromptTemplate, FinalAnswerPromptTemplate, ManagedAgentPromptTemplate
 
-from dotenv import load_dotenv
-from my_reverse_string import ReverseStringTool
-from my_image_load import ImageLoadTool
-from my_chess_board_tool import ChessBoard
-from my_fen_tool import FENTool
-from my_chess_analysis_tool import ChessAnalysisTool
-from PIL import Image
+from my_tool_reverse_string import ReverseStringTool
+from my_tool_image_load import ImageLoadTool
+from my_tool_chess_board import ChessBoard
+from my_tool_fen import FENTool
+from my_tool_chess_analysis import ChessAnalysisTool
 
 
 task_id = "cca530fc-4052-43b2-b130-b30968d8aa44"
 
 MODEL_REASONING = "Qwen/Qwen2.5-Coder-32B-Instruct"
-MODEL_CHESS     = "jayasuryajsk/chess-reasoner-qwen"
-#MODEL_REASONING = "Qwen/Qwen2.5-72B-Instruct"  not good
+#MODEL_REASONING = "deepseek-ai/deepseek-coder-33b-instruct"     # not available
+#MODEL_REASONING = "deepseek-ai/DeepSeek-Coder-V2-Instruct"      # not available
+#MODEL_CHESS     = "jayasuryajsk/chess-reasoner-qwen"
+#MODEL_REASONING = "Qwen/Qwen2.5-72B-Instruct"                  # not good
 #"meta-llama/Meta-Llama-3-70B-Instruct"
 # jayasuryajsk/chess-reasoner-qwen
 # https://huggingface.co/jayasuryajsk/chess-reasoner-qwen
@@ -34,16 +29,21 @@ PROMPT_TEMPLATES = PromptTemplates(
             Each bullet point should describe in one sentence an action which is to be taken in this step.
             Do NOT provide hypothetical examples for the final answer.
             
-            Use the tools provided. If you are going to use a tool, describe in detail how you are going
+            Use the tools provided, and only those which are necessary to answer the question. 
+            If you are going to use a tool, describe in detail how you are going
             to use that particular tool and explain parameters used to invoke the tool.
             
             Tools provided :
-            _my_image_load : load an image for given task_id ,
-            _my_chess_board : process an image and extract list of chess pieces ,
-            _my_fen_tool : convert list of chess pieces into FEN notation , 
-            _my_chess_analysis : analyze chess position provided in FEN notation and provide best move ,
-            _my_reverse_string : reverse a string ,
-            final_answer : provide final answer
+            * _my_image_load : load an image for given task_id, available arguments: task_id ,
+            
+            Use <code></code> examples as here
+            https://github.com/huggingface/smolagents/blob/main/src/smolagents/vision_web_browser.py
+            
+            * _my_chess_board : process an image and extract list of chess pieces, available arguments: img ,
+            * _my_fen_tool : convert list of chess pieces into FEN notation, available arguments: chest_pieces , 
+            * _my_chess_analysis : analyze chess position provided in FEN notation and provide best move, 
+                available arguments: fen, player_color
+            * _my_reverse_string : reverse a string, available arguments: input_str ,
             
             YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of
             numbers and/or strings.
@@ -80,23 +80,15 @@ PROMPT_TEMPLATES = PromptTemplates(
 
 #question = f"Load an image for task id {task_id} and describe the chess position shown on the image. "
 #question = f"Load an image for task id {task_id} and display it using matplotlib "
-question = (f"Load an image for task id {task_id}, extyract checc pieces position into FEN notation, and provide "
-            f"best move for black ")
+#question = (f"Load an image for task id {task_id}, extyract checc pieces position into FEN notation, and provide "
+#            f"best move for black ")
+question = ("How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the "
+       "latest "
+ "2022 version of english wikipedia.")
 
 chess_board_model_name = "my_chess_pieces_recognition.pth"
 chess_board_model_dir = "/mnt/c/Users/krzsa/IdeaProjects/Agents-Course-Assignment/saved_models"
 
-#chess_agent = CodeAgent(
-#    name="ChessAgent",
-#    description="Chess Board Analysis Agent",
-#    tools=[],
-#    model=InferenceClientModel(model_id=MODEL_CHESS),
-#    #planning_interval=3, # This is where you activate planning!,
-#    #prompt_templates=PROMPT_TEMPLATES,
-#    #managed_agents=[web_search_agent],
-#    additional_authorized_imports=[],
-#)
-
 reasoning_agent = CodeAgent(
     name="CourseAssistant",
     description="General AI Assistant",
@@ -106,7 +98,8 @@ reasoning_agent = CodeAgent(
         ReverseStringTool(),
         ChessBoard(chess_board_model_name, chess_board_model_dir),
         FENTool(),
-        ChessAnalysisTool()
+        ChessAnalysisTool(),
+        WebSearchTool()
     ],
     model=InferenceClientModel(model_id=MODEL_REASONING),
     planning_interval=3, # This is where you activate planning!,
@@ -117,6 +110,7 @@ reasoning_agent = CodeAgent(
         "chess",
         "matplotlib",
         "matplotlib.pyplot",
+        "stockfish",
         "my_chess_board_tool",
         "my_fen_tool",
         "my_image_load",

test_tools.py

@@ -1,8 +1,8 @@
-from my_reverse_string import ReverseStringTool
-from my_image_load import ImageLoadTool
-from my_chess_board_tool import ChessBoard
-from my_fen_tool import FENTool
-from my_chess_analysis_tool import ChessAnalysisTool
+from my_tool_reverse_string import ReverseStringTool
+from my_tool_image_load import ImageLoadTool
+from my_tool_chess_board import ChessBoard
+from my_tool_fen import FENTool
+from my_tool_chess_analysis import ChessAnalysisTool
 import pytest
 import matplotlib.pyplot as plt
 import matplotlib as mp