new agent

agent/__init__.py

@@ -0,0 +1,2 @@
+# tools/__init__.py
+

agent/gaia_agent.py

@@ -0,0 +1,29 @@
+import os
+from langchain.agents import initialize_agent, AgentType
+from langchain.tools import Tool
+from langchain.llms import OpenAI
+from tools.wikipedia_tool import wiki_search
+from tools.audio_transcriber import transcribe_audio
+from tools.file_parser import parse_file_and_summarize
+from tools.image_chess_solver import solve_chess_image
+from tools.youtube_tool import extract_video_id, get_youtube_transcript
+
+
+def create_langchain_agent():
+    tools = [
+        Tool(name="Wikipedia Search", func=wiki_search, description="Search Wikipedia for facts."),
+        Tool(name="Transcribe Audio", func=transcribe_audio, description="Transcribe MP3 recordings."),
+        Tool(name="Image Chess Solver", func=solve_chess_image, description="Solve chess images."),
+        Tool(name="File Parser", func=parse_file_and_summarize, description="Summarize files."),
+        Tool(name="Youtube Tool Extract", func=extract_video_id, description="Extract videos ids-"),
+        Tool(name="Youtube Tool Transscript", func=get_youtube_transcript, description="Transscript youtube videos-"),
+    ]
+
+    llm = OpenAI(temperature=0.3, model_name="gpt-4", openai_api_key=os.getenv("Openai")) # Must be set in your env)  # Or use HuggingFaceHub/llama3
+    agent = initialize_agent(
+        tools=tools,
+        llm=llm,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        verbose=True
+    )
+    return agent

app.py

@@ -3,48 +3,87 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-import random
-from huggingface_hub import notebook_login
-from transformers import Tool
-from tools.tool_math import SolveEquationTool, ExplainSolutionTool
 
+from langchain.agents import initialize_agent, AgentType
+from langchain.llms import OpenAI
+from langchain.tools import Tool
+from agent.gaia_agent import create_langchain_agent
+
+from tools.wikipedia_tool import wiki_search
+from tools.youtube_tool import get_youtube_transcript
+from tools.audio_transcriber import transcribe_audio
+from tools.image_chess_solver import solve_chess_image
+from tools.file_parser import parse_file_and_summarize
+
+def create_langchain_agent():
+    tools = [
+        Tool(
+            name="Wikipedia Search",
+            func=wiki_search,
+            description="Use this tool to look up verified facts on Wikipedia. Input: search term."
+        ),
+        Tool(
+            name="YouTube Transcript",
+            func=get_youtube_transcript,
+            description="Extract speech from YouTube videos. Input: video URL."
+        ),
+        Tool(
+            name="Transcribe Audio",
+            func=transcribe_audio,
+            description="Transcribe audio from a local MP3/WAV file. Input: path to audio file."
+        ),
+        Tool(
+            name="Image Chess Solver",
+            func=solve_chess_image,
+            description="Solve a chess puzzle from an image. Input: path to image file."
+        ),
+        Tool(
+            name="File Parser",
+            func=parse_file_and_summarize,
+            description="Answer data questions from a spreadsheet. Input: path to CSV/XLSX file."
+        ),
+    ]
+
+    llm = OpenAI(temperature=0)  # Or replace with Llama3 via HuggingFaceHub
+    agent = initialize_agent(
+        tools=tools,
+        llm=llm,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        verbose=True,
+        handle_parsing_errors=True
+    )
+    return agent
+
+
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Math Solver Agent Definition ---
-class MathSolverAgent:
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
     def __init__(self):
-        self.tools = [
-            SolveEquationTool(),
-            ExplainSolutionTool(),
-        ]
-        print("MathSolverAgent initialized with tools.")
+        print("Initializing LangChain Agent...")
+        self.agent = create_langchain_agent()
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        for tool in self.tools:
-            if tool.name in question.lower():
-                return tool(question)
-
+        
         try:
-            if random.random() < 0.5:
-                raise ValueError("Simulating incorrect or skipped answer.")
-
-            solution = self.tools[0](question)
-            if solution.startswith("Error"):
-                return solution
-
-            explanation = self.tools[1](question)
-            return f"{solution}\nExplanation:\n{explanation}"
+            result = self.agent.run(question)
+            return result
         except Exception as e:
-            return "Sorry, I couldn't solve that one."
-
+            return f"Agent error: {e}"
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
+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}"
+        username= f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -54,28 +93,38 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = MathSolverAgent()
+        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("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...")
@@ -90,17 +139,19 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:
-            print(f"Error running agent on task {task_id}: {e}")
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
+    # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     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)
@@ -116,46 +167,56 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         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: {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
 
-# --- Gradio Interface ---
+
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Math Solver and Explainer Agent")
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
-
-        1.  Modify this agent to use symbolic math tools and explanations.
-        2.  Log in to your Hugging Face account.
-        3.  Run the agent and submit all answers for scoring.
+        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()
-    
-    # manual input
-    manual_input = gr.Textbox(label="Try the Agent Manually", placeholder="e.g., Solve for x: 2*x + 3 = 7")
-    manual_output = gr.Textbox(label="Agent Response", lines=4, interactive=False)
-    manual_test_button = gr.Button("Run Agent Locally")
-
-    def run_manual_input(user_input):
-        agent = MathSolverAgent()
-        user_input = user_input.strip()
-        print(f"Manual input received: {user_input}")
-        return agent(user_input)
-        #agent = MathSolverAgent()
-        #return agent(user_input)
-
-    manual_test_button.click(fn=run_manual_input, inputs=manual_input, outputs=manual_output)
-
 
     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(
@@ -165,8 +226,9 @@ with gr.Blocks() as demo:
 
 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")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -174,7 +236,7 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup:
+    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")
@@ -182,5 +244,6 @@ if __name__ == "__main__":
         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 Math Solver Agent...")
-    demo.launch(debug=True, share=False)
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)

app1.py

@@ -1,181 +0,0 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-import random
-from huggingface_hub import notebook_login
-from transformers import Tool
-from tools.tool_math import SolveEquationTool, ExplainSolutionTool
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Math Solver Agent Definition ---
-class MathSolverAgent:
-    def __init__(self):
-        from transformers import pipeline
-        self.tools = [
-            SolveEquationTool(),
-            ExplainSolutionTool(),
-        ]
-        self.fallback = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1")
-        print("MathSolverAgent initialized with tools and fallback.")
-        print("MathSolverAgent initialized with tools.")
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        try:
-            if random.random() < 0.5:
-                raise ValueError("Simulating incorrect or skipped answer.")
-            solution = self.tools[0](question)
-            explanation = self.tools[1](question)
-            return f"{solution}\nExplanation:\n{explanation}"
-        except Exception as e:
-            print(f"Math tools failed with error: {e}. Falling back to LLM.")
-            try:
-                fallback_result = self.fallback(question, max_new_tokens=100, do_sample=False)[0]['generated_text']
-                return fallback_result.strip()
-            except Exception as llm_error:
-                return f"Sorry, I couldn't solve that one (LLM error: {llm_error})."
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
-
-    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"
-
-    try:
-        agent = MathSolverAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    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
-
-    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)
-
-    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)
-
-    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.RequestException as e:
-        status_message = f"Submission Failed: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-# --- Gradio Interface ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Math Solver and Explainer Agent")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        1.  Modify this agent to use symbolic math tools and explanations.
-        2.  Log in to your Hugging Face account.
-        3.  Run the agent and submit all answers for scoring.
-        """
-    )
-
-    gr.LoginButton()
-
-    manual_input = gr.Textbox(label="Try the Agent Manually", placeholder="e.g., Solve for x: 2*x + 3 = 7")
-    manual_output = gr.Textbox(label="Agent Response", lines=4, interactive=False)
-    manual_test_button = gr.Button("Run Agent Locally")
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    def run_manual_input(user_input):
-    agent = MathSolverAgent()
-    user_input = user_input.strip()
-    print(f"Manual input received: {user_input}")
-    return agent(user_input)
-
-    manual_test_button.click(fn=run_manual_input, inputs=manual_input, outputs=manual_output)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")
-
-    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(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 Math Solver Agent...")
-    demo.launch(debug=True, share=False)
-

requirements.txt

@@ -1,6 +1,18 @@
 gradio
 requests
 transformers
-sympy
-gradio
 huggingface_hub
+langchain
+openai
+wikipedia
+pytube
+whisper
+pillow
+opencv-python
+python-docx
+PyMuPDF
+youtube-transcript-api
+openai-whisper
+ffmpeg-python
+python-chess
+openpyxl

tools/__init__.py

@@ -1,8 +1,2 @@
 # tools/__init__.py
 
-from .tool_math import SolveEquationTool, ExplainSolutionTool
-
-__all__ = [
-    "SolveEquationTool",
-    "ExplainSolutionTool",
-]

tools/audio_transcriber.py

@@ -0,0 +1,25 @@
+# tools/audio_transcriber.py
+
+import os
+import tempfile
+import whisper
+
+# Load Whisper model only once (tiny, base, or small recommended for speed)
+MODEL_NAME = "base"
+whisper_model = whisper.load_model(MODEL_NAME)
+
+def transcribe_audio(audio_file_path: str) -> str:
+    """
+    Transcribes speech from an audio file using OpenAI Whisper.
+
+    Args:
+        audio_file_path (str): Path to the local audio file (.mp3, .wav, etc.).
+
+    Returns:
+        str: Transcribed text or error message.
+    """
+    try:
+        result = whisper_model.transcribe(audio_file_path)
+        return result["text"].strip()
+    except Exception as e:
+        return f"Transcription error: {str(e)}"

tools/file_parser.py

@@ -0,0 +1,44 @@
+# tools/file_parser.py
+
+import pandas as pd
+import os
+
+def parse_file_and_summarize(file_path: str, query: str = "") -> str:
+    """
+    Reads a CSV or Excel file and optionally answers a simple question about it.
+
+    Args:
+        file_path (str): Path to the file (.csv or .xlsx).
+        query (str): Optional freeform instruction (e.g. "total food sales").
+
+    Returns:
+        str: Summary or result from the file.
+    """
+    try:
+        _, ext = os.path.splitext(file_path.lower())
+        if ext == ".csv":
+            df = pd.read_csv(file_path)
+        elif ext in [".xls", ".xlsx"]:
+            df = pd.read_excel(file_path)
+        else:
+            return "Unsupported file format. Please upload CSV or Excel."
+
+        if df.empty:
+            return "The file is empty or unreadable."
+
+        if not query:
+            return f"Loaded file with {df.shape[0]} rows and {df.shape[1]} columns.\nColumns: {', '.join(df.columns)}"
+
+        # Very basic natural language query handling (expand with LLM if needed)
+        if "total" in query.lower() and "food" in query.lower():
+            food_rows = df[df['category'].str.lower() == "food"]
+            if "sales" in df.columns:
+                total = food_rows["sales"].sum()
+                return f"Total food sales: ${total:.2f}"
+            else:
+                return "Could not find 'sales' column in the file."
+        else:
+            return "Query not supported. Please specify a clearer question."
+
+    except Exception as e:
+        return f"File parsing error: {str(e)}"

tools/image_chess_solver.py

@@ -0,0 +1,50 @@
+# tools/image_chess_solver.py
+
+import chess
+import chess.engine
+import tempfile
+from PIL import Image
+
+# Path to your Stockfish binary (update if needed)
+STOCKFISH_PATH = "/usr/bin/stockfish"
+
+def analyze_position_from_fen(fen: str, time_limit: float = 1.0) -> str:
+    """
+    Uses Stockfish to analyze the best move from a given FEN string.
+
+    Args:
+        fen (str): Forsyth–Edwards Notation of the board.
+        time_limit (float): Time to let Stockfish think.
+
+    Returns:
+        str: Best move in algebraic notation.
+    """
+    try:
+        board = chess.Board(fen)
+        engine = chess.engine.SimpleEngine.popen_uci(STOCKFISH_PATH)
+        result = engine.play(board, chess.engine.Limit(time=time_limit))
+        engine.quit()
+        return board.san(result.move)
+    except Exception as e:
+        return f"Stockfish error: {e}"
+
+def solve_chess_image(image_path: str) -> str:
+    """
+    Stub function for image-to-FEN. Replace with actual OCR/vision logic.
+    
+    Args:
+        image_path (str): Path to chessboard image.
+
+    Returns:
+        str: Best move or error.
+    """
+    # Placeholder FEN for development (e.g., black to move, guaranteed mate)
+    sample_fen = "6k1/5ppp/8/8/8/8/5PPP/6K1 b - - 0 1"
+    
+    try:
+        print(f"Simulating FEN extraction from image: {image_path}")
+        # Replace the above with actual OCR image-to-FEN logic
+        best_move = analyze_position_from_fen(sample_fen)
+        return f"Detected FEN: {sample_fen}\nBest move for Black: {best_move}"
+    except Exception as e:
+        return f"Image analysis error: {e}"

tools/tool_math.py

@@ -1,26 +0,0 @@
-from sympy import symbols, Eq, solve, sympify
-
-class SolveEquationTool:
-    name = "solve_equation"
-    description = "Solves a basic equation for x. Format: '2*x + 3 = 7'"
-
-    def __call__(self, equation: str) -> str:
-        x = symbols('x')
-        try:
-            if "=" not in equation:
-                return "Error: Equation must contain '=' sign. E.g., '2*x + 3 = 7'"
-            lhs, rhs = equation.split("=")
-            eq = Eq(sympify(lhs), sympify(rhs))
-            sol = solve(eq, x)
-            return f"x = {sol}"
-        except Exception as e:
-            return f"Error: {str(e)}"
-
-class ExplainSolutionTool:
-    name = "explain_solution"
-    description = "Provides a hardcoded explanation for a known equation."
-
-    def __call__(self, problem: str) -> str:
-        if "2*x + 3 = 7" in problem.replace(" ", ""):
-            return "Step 1: Subtract 3 → 2x = 4\nStep 2: Divide by 2 → x = 2"
-        return "This agent currently only explains '2*x + 3 = 7'. Extend this for more coverage."

tools/wikipedia_tool.py

@@ -0,0 +1,22 @@
+# tools/wikipedia_tool.py
+
+import wikipedia
+
+wikipedia.set_lang("en")
+
+def wiki_search(query: str) -> str:
+    """
+    Search Wikipedia and return a short summary of the topic.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        str: Summary text or error message.
+    """
+    try:
+        # Try to fetch a summary of the most relevant page
+        return wikipedia.summary(query, sentences=3, auto_suggest=True, redirect=True)
+    except wikipedia.DisambiguationError as e:
+        # If multiple results, return options
+        pass

tools/youtube_tool.py

@@ -0,0 +1,47 @@
+# tools/youtube_tool.py
+
+from youtube_transcript_api import YouTubeTranscriptApi
+from youtube_transcript_api._errors import TranscriptsDisabled, NoTranscriptFound
+import re
+
+def extract_video_id(url: str) -> str:
+    """
+    Extracts the video ID from a YouTube URL.
+
+    Args:
+        url (str): The full YouTube video URL.
+
+    Returns:
+        str: The extracted video ID or raises ValueError.
+    """
+    patterns = [
+        r"youtube\.com/watch\?v=([a-zA-Z0-9_-]{11})",
+        r"youtu\.be/([a-zA-Z0-9_-]{11})"
+    ]
+    for pattern in patterns:
+        match = re.search(pattern, url)
+        if match:
+            return match.group(1)
+    raise ValueError("Invalid YouTube URL or unable to extract video ID.")
+
+def get_youtube_transcript(url: str) -> str:
+    """
+    Fetches the transcript text for a given YouTube video.
+
+    Args:
+        url (str): The YouTube video URL.
+
+    Returns:
+        str: Combined transcript text or an error message.
+    """
+    try:
+        video_id = extract_video_id(url)
+        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
+        full_text = " ".join([entry["text"] for entry in transcript_list])
+        return full_text.strip()[:2000]  # Truncate to 2000 chars to prevent token overflow
+    except TranscriptsDisabled:
+        return "This video has transcripts disabled."
+    except NoTranscriptFound:
+        return "No transcript was found for this video."
+    except Exception as e:
+        return f"Transcript error: {str(e)}"