Update app.py

app.py

@@ -1,217 +1,57 @@
 import os
 import gradio as gr
 import requests
+import inspect
 import pandas as pd
-import re # For CalculatorTool validation
-
-# --- Smol Agents and Hugging Face Hub Imports ---
-from smol_agents import ReactAgent # The core agent from smol_agents (module name uses underscore)
-from huggingface_hub import load_tool as hub_load_tool # To load tools like search
-from huggingface_hub import login
 
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-# LLM for the Agent (Mixtral is a strong choice for reasoning tasks)
-LLM_ENDPOINT = "mistralai/Mixtral-8x7B-Instruct-v0.1" 
-
-# --- Hugging Face Authentication ---
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-# --- Tool Definitions for Smol Agent ---
-
-# 1. Calculator Tool (as a simple class with a 'run' method for smol-agents)
-class SmolCalculatorTool:
-    name = "calculator"
-    description = (
-        "This tool is a calculator. Use it to compute mathematical expressions. "
-        "Input should be a valid mathematical expression string (e.g., '2+2', '100/5*2', '(3.14+2.71)*4'). "
-        "Only use standard arithmetic operators (+, -, *, /) and parentheses."
-    )
-    # smol-agents typically infers arguments from the run method's signature
-    
-    def run(self, expression: str) -> str:
-        """
-        Computes a mathematical expression.
-        Args:
-            expression (str): The mathematical expression to evaluate.
-        Returns:
-            str: The result of the calculation or an error message.
-        """
-        print(f"CalculatorTool received expression: {expression}")
-        try:
-            if not isinstance(expression, str):
-                return "Error: Input expression must be a string."
-            
-            if not re.match(r"^[0-9\.\+\-\*\/\(\)\s_]+$", expression): # Allow underscore for e.g. large numbers
-                return "Error: Expression contains invalid characters. Only use numbers, operators (+, -, *, /), parentheses, and underscores."
-
-            # Replace underscores if they are used as separators (e.g., 1_000_000)
-            expression_to_eval = expression.replace("_", "")
-            
-            # Safely evaluate the expression
-            result = eval(expression_to_eval)
-            return str(result)
-        except Exception as e:
-            return f"Error during calculation: {str(e)}. Please ensure the expression is valid."
-
-# --- GAIA Smol Agent Definition ---
-# This system prompt guides the ReactAgent's behavior.
-# smol-agents' ReactAgent will automatically include tool descriptions.
-SMOL_AGENT_SYSTEM_PROMPT = """
-You are a highly capable and meticulous AI assistant. Your task is to answer user questions accurately and comprehensively.
-You must operate in a loop of Thought, Action, Observation.
 
-1.  **Thought:** Analyze the question. What information is needed? Can you answer it directly, or do you need a tool? If you need a tool, which one is most appropriate and why? Formulate a plan.
-2.  **Action:** If you decide to use a tool, output a JSON blob with 'tool_name' and 'tool_input'. For example:
-    ```json
-    {{
-      "tool_name": "your_tool_name",
-      "tool_input": "input for the tool"
-    }}
-    ```
-    If you believe you can answer the question without a tool, or after using tools you have the final answer, output your answer directly without the JSON blob.
-3.  **Observation:** (This will be filled by the system with the tool's output if an action was taken.)
-4.  Repeat the Thought, Action, Observation loop until you have the final answer.
-
-Important guidelines:
--   **Accuracy is key:** Prioritize correctness. If you cannot find the information or are unsure, state that. Do not invent facts.
--   **Tool Use:** Use tools only when necessary. For factual queries requiring up-to-date information or calculations, use your tools.
--   **Search Effectively:** When using the search tool, formulate concise and targeted search queries.
--   **Calculations:** For any numerical calculations, use the calculator tool to ensure accuracy.
--   **Multi-step Reasoning:** Break down complex questions into smaller, manageable steps.
--   **Final Answer:** Once you have all the necessary information and are confident in your answer, provide it directly.
-"""
-
-class GaiaSmolAgent:
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
     def __init__(self):
-        print("Initializing GaiaSmolAgent...")
-        self.agent = None
-
-        if not HF_TOKEN:
-            print("ERROR: HF_TOKEN environment variable not found. GaiaSmolAgent cannot be initialized.")
-            raise ValueError("HF_TOKEN is not set. Please set it as a secret in your Hugging Face Space.")
-
-        try:
-            login(token=HF_TOKEN, add_to_git_credential=False)
-            print("Successfully logged in to Hugging Face Hub for GaiaSmolAgent.")
-        except Exception as e:
-            print(f"Error during Hugging Face login for GaiaSmolAgent: {e}")
-            raise ConnectionError(f"Hugging Face login failed: {e}")
-
-        # Prepare tools
-        tools_list = []
-        try:
-            print("Loading DuckDuckGo search tool via hub_load_tool...")
-            # hub_load_tool is often used with smol-agents
-            search_tool = hub_load_tool("HuggingFaceH4/duckduckgo_search", hf_token=HF_TOKEN)
-            tools_list.append(search_tool)
-            print(f"DuckDuckGo search tool loaded: {search_tool.name if hasattr(search_tool, 'name') else 'Unknown name'}")
-        except Exception as e:
-            print(f"Error loading DuckDuckGo search tool: {e}. Search functionality will be unavailable.")
-            # Decide if this is a fatal error
-            # raise RuntimeError(f"Failed to load search tool: {e}") from e
-
-        calculator = SmolCalculatorTool()
-        tools_list.append(calculator)
-        print(f"Calculator tool prepared: {calculator.name}")
-        
-        if not tools_list:
-             print("CRITICAL WARNING: No tools were successfully loaded for GaiaSmolAgent.")
-        
-        try:
-            print(f"Initializing ReactAgent with LLM: {LLM_ENDPOINT}")
-            self.agent = ReactAgent(
-                tools=tools_list,
-                llm_engine=LLM_ENDPOINT, # smol-agents can take model ID string
-                system_prompt=SMOL_AGENT_SYSTEM_PROMPT,
-                hf_token=HF_TOKEN, # Pass token for LLM access via model ID
-                max_iterations=10, # Limit iterations to prevent infinite loops
-                # You can adjust temperature and other LLM params via llm_kwargs
-                # llm_kwargs={"temperature": 0.1, "max_new_tokens": 1024} 
-            )
-            print("GaiaSmolAgent ReactAgent component initialized successfully.")
-        except Exception as e:
-            print(f"CRITICAL Error initializing ReactAgent component: {e}")
-            raise RuntimeError(f"Failed to initialize ReactAgent: {e}") from e
-        
-        print("GaiaSmolAgent fully initialized.")
-
+        print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
-        print(f"GaiaSmolAgent received question (first 100 chars): {question[:100]}...")
-        if self.agent is None:
-            print("ERROR: GaiaSmolAgent's ReactAgent component is not initialized.")
-            return "Error: Agent not initialized. Check logs."
-        
-        try:
-            print("Running ReactAgent to get the answer...")
-            # smol-agents' run method takes a 'task' argument
-            # The output of agent.run() is typically the final answer string.
-            final_answer = self.agent.run(task=question)
-            
-            if not isinstance(final_answer, str):
-                print(f"Warning: ReactAgent returned a non-string type: {type(final_answer)}. Converting to string.")
-                final_answer = str(final_answer)
-
-            print(f"GaiaSmolAgent returning answer (first 100 chars): {final_answer[:100]}...")
-            return final_answer
-        except Exception as e:
-            print(f"Error during GaiaSmolAgent execution for question '{question[:50]}...': {e}")
-            # Include traceback for better debugging if possible, or detailed error
-            import traceback
-            traceback_str = traceback.format_exc()
-            print(traceback_str)
-            return f"AGENT EXECUTION ERROR: {str(e)}"
-
+        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):
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the GaiaSmolAgent on them, submits all answers,
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
-    space_id = os.getenv("SPACE_ID")
+    # --- 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.")
         return "Please Login to Hugging Face with the button.", None
 
-    if not HF_TOKEN:
-        no_token_message = "ERROR: HF_TOKEN secret is not set in this Space. The agent cannot operate. Please ask the Space owner to set it."
-        print(no_token_message)
-        return no_token_message, None
-
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent (MODIFIED TO USE GaiaSmolAgent)
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        print("Attempting to instantiate GaiaSmolAgent...")
-        agent = GaiaSmolAgent() # <<<< MODIFIED HERE
-        print("GaiaSmolAgent instantiated successfully.")
+        agent = BasicAgent()
     except Exception as e:
-        error_msg = f"Fatal Error initializing GaiaSmolAgent: {e}. Cannot proceed with evaluation."
-        print(error_msg)
-        import traceback
-        print(traceback.format_exc()) # Print full traceback for initialization errors
-        # Provide more specific feedback
-        if "HF_TOKEN is not set" in str(e):
-            error_msg = "Fatal Error: The HF_TOKEN secret is missing or not accessible. The agent cannot start. Please ensure it's set in the Space settings."
-        elif "login failed" in str(e) or "authentication" in str(e).lower():
-            error_msg = "Fatal Error: Hugging Face login failed. Check if the HF_TOKEN is valid and has 'read' permissions. The agent cannot start."
-        elif "Failed to initialize ReactAgent" in str(e):
-             error_msg = f"Fatal Error: Core ReactAgent component failed to initialize: {e}. This could be due to issues with the LLM endpoint ({LLM_ENDPOINT}) or tool setup."
-        return error_msg, None
-
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Code link not available (SPACE_ID not set)"
-    print(f"Agent code link: {agent_code}")
+        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=20) 
+        response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
@@ -225,14 +65,14 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
          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: 
+    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 GaiaSmolAgent on {len(questions_data)} questions...")
+    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")
@@ -240,30 +80,27 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            print(f"\nProcessing Task ID: {task_id}, Question: {question_text[:100]}...")
-            submitted_answer = agent(question_text) 
+            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})
-            print(f"Task ID {task_id} processed. Answer (first 100): {submitted_answer[:100]}")
         except Exception as e:
-             print(f"Error running agent on task {task_id} ('{question_text[:50]}...'): {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT RUNTIME 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 valid answers to submit (all tasks might have resulted in errors).")
-        return "Agent did not produce any valid answers to submit. Check logs for errors.", pd.DataFrame(results_log)
+    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
+    # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"GaiaSmolAgent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    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=90) 
-        response.raise_for_status() 
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
         result_data = response.json()
         final_status = (
             f"Submission Successful!\n"
@@ -278,10 +115,10 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except requests.exceptions.HTTPError as e:
         error_detail = f"Server responded with status {e.response.status_code}."
         try:
-            error_json = e.response.json() 
+            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]}" 
+        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)
@@ -291,12 +128,12 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         print(status_message)
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
-    except requests.exceptions.RequestException as e: 
+    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: 
+    except Exception as e:
         status_message = f"An unexpected error occurred during submission: {e}"
         print(status_message)
         results_df = pd.DataFrame(results_log)
@@ -304,51 +141,54 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
 
 # --- Build Gradio Interface using Blocks ---
-with gr.Blocks(theme=gr.themes.Soft()) as demo: 
-    gr.Markdown("# GAIA Benchmark Agent Runner (using smol-agents) 🚀")
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
-        f"""
+        """
         **Instructions:**
-        1.  This Space runs a **GAIA-style Agent** using `smol-agents.ReactAgent` with the `{LLM_ENDPOINT}` model.
-        2.  It uses **DuckDuckGo Search** and a **Calculator** tool.
-        3.  **IMPORTANT:** The Space owner must set the `HF_TOKEN` in the Space secrets for the agent to work.
-        4.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        5.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run the agent, submit answers, and see the score.
+        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:**
-        -   Processing all questions can take significant time (several minutes) depending on the LLM and question complexity.
-        -   Ensure your `HF_TOKEN` has 'read' access.
-        -   The agent's performance depends on the LLM, prompt, and tool effectiveness.
-        -   This version uses `smolagents` (the PyPI package name). Ensure `requirements.txt` is updated accordingly.
+        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=7, interactive=False) 
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True, height=400) 
+
+    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,
-        inputs=None, 
-        outputs=[status_output, results_table],
+        outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " GAIA Smol Agent App Starting " + "-"*30)
-    
-    if not HF_TOKEN:
-        print("🔴 WARNING: HF_TOKEN environment variable is NOT SET at startup.")
-        print("   The agent will likely FAIL to initialize. Please set HF_TOKEN as a secret in your Space settings.")
+    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(f"✅ HF_TOKEN found. Agent will attempt to use it.")
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    space_id_startup = os.getenv("SPACE_ID")
-    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")
     else:
         print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-    
-    print("-"*(60 + len(" GAIA Smol Agent App Starting ")) + "\n")
-    print("Launching Gradio Interface for GAIA Smol Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)