start over

agent-old.py

@@ -0,0 +1,70 @@
+"""
+from typing import Annotated, TypedDict
+
+from langchain_community.chat_models import ChatHuggingFace
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
+from langgraph.graph import START, StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode, tools_condition
+
+from retriever import guest_info_tool
+from tools import (absolute, add, divide, exponential, floor_divide,
+                   get_current_time_in_timezone, logarithm, modulus, multiply,
+                   power, roman_calculator_converter, square_root, subtract,
+                   web_search)
+
+# Generate the chat interface, including the tools
+llm = HuggingFaceEndpoint(
+    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+)
+
+chat = ChatHuggingFace(llm=llm, verbose=True)
+tools = [
+    multiply,
+    add,
+    subtract,
+    power,
+    divide,
+    modulus,
+    square_root,
+    floor_divide,
+    absolute,
+    logarithm,
+    exponential,
+    web_search,
+    roman_calculator_converter,
+    get_current_time_in_timezone,
+]
+
+chat_with_tools = chat.bind_tools(tools)
+
+# Generate the AgentState and Agent graph
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+def assistant(state: AgentState):
+    return {
+        "messages": [chat_with_tools.invoke(state["messages"])],
+    }
+
+## The graph
+builder = StateGraph(AgentState)
+
+# Define nodes: these do the work
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+
+# Define edges: these determine how the control flow moves
+builder.add_edge(START, "assistant")
+builder.add_conditional_edges(
+    "assistant",
+    # If the latest message requires a tool, route to tools
+    # Otherwise, provide a direct response
+    tools_condition,
+)
+builder.add_edge("tools", "assistant")
+alfred = builder.compile()
+
+"""

agent.py

@@ -1,70 +0,0 @@
-"""
-from typing import Annotated, TypedDict
-
-from langchain_community.chat_models import ChatHuggingFace
-from langchain_community.llms import HuggingFaceEndpoint
-from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
-from langgraph.graph import START, StateGraph
-from langgraph.graph.message import add_messages
-from langgraph.prebuilt import ToolNode, tools_condition
-
-from retriever import guest_info_tool
-from tools import (absolute, add, divide, exponential, floor_divide,
-                   get_current_time_in_timezone, logarithm, modulus, multiply,
-                   power, roman_calculator_converter, square_root, subtract,
-                   web_search)
-
-# Generate the chat interface, including the tools
-llm = HuggingFaceEndpoint(
-    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
-)
-
-chat = ChatHuggingFace(llm=llm, verbose=True)
-tools = [
-    multiply,
-    add,
-    subtract,
-    power,
-    divide,
-    modulus,
-    square_root,
-    floor_divide,
-    absolute,
-    logarithm,
-    exponential,
-    web_search,
-    roman_calculator_converter,
-    get_current_time_in_timezone,
-]
-
-chat_with_tools = chat.bind_tools(tools)
-
-# Generate the AgentState and Agent graph
-class AgentState(TypedDict):
-    messages: Annotated[list[AnyMessage], add_messages]
-
-def assistant(state: AgentState):
-    return {
-        "messages": [chat_with_tools.invoke(state["messages"])],
-    }
-
-## The graph
-builder = StateGraph(AgentState)
-
-# Define nodes: these do the work
-builder.add_node("assistant", assistant)
-builder.add_node("tools", ToolNode(tools))
-
-# Define edges: these determine how the control flow moves
-builder.add_edge(START, "assistant")
-builder.add_conditional_edges(
-    "assistant",
-    # If the latest message requires a tool, route to tools
-    # Otherwise, provide a direct response
-    tools_condition,
-)
-builder.add_edge("tools", "assistant")
-alfred = builder.compile()
-
-"""

app-old.py

@@ -0,0 +1,523 @@
+import asyncio
+import inspect
+import json
+import os
+import time
+from typing import Any, Dict, List, Optional
+
+import gradio as gr
+import pandas as pd
+import requests
+from dotenv import load_dotenv
+from langchain_community.chat_models import ChatHuggingFace
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_core.tools import StructuredTool
+
+from tools import (absolute, add, divide, exponential, floor_divide,
+                   get_current_time_in_timezone, logarithm, modulus, multiply,
+                   power, roman_calculator_converter, square_root, subtract,
+                   web_search)
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+MAX_AGENT_ITERATIONS = 15
+MAX_CONCURRENT_REQUESTS = 5  # Limit concurrent requests to avoid overwhelming the API
+
+load_dotenv()
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
+
+# Quick test to see if tokens are available.
+print(f"Available env vars: {[k for k in os.environ.keys() if 'TOKEN' in k or 'HF' in k]}")
+
+# Global cache for answers
+answer_cache = {}
+
+class ImprovedAgent:
+    def __init__(self):
+        if not HUGGINGFACEHUB_API_TOKEN:
+            raise ValueError("Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN.")
+
+        print("ImprovedAgent initialized.")
+        
+        # Initialize LLM with better parameters
+        self.llm = HuggingFaceEndpoint(
+            repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+            huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+            temperature=0.1,  # Lower temperature for more consistent responses
+            max_new_tokens=1024,
+            timeout=30,
+        )
+        
+        self.chat = ChatHuggingFace(llm=self.llm, verbose=False)
+        
+        # Initialize tools
+        self.tools = [
+            multiply, add, subtract, power, divide, modulus,
+            square_root, floor_divide, absolute, logarithm,
+            exponential, web_search, roman_calculator_converter,
+            get_current_time_in_timezone
+        ]
+        
+        self.chat_with_tools = self.chat.bind_tools(self.tools)
+        print(f"Total tools available: {len(self.tools)}")
+        
+        # Create tool mapping for easier access
+        self.tool_map = {tool.name: tool for tool in self.tools}
+
+    def _extract_tool_calls(self, response) -> List[Dict]:
+        """Extract tool calls from the response"""
+        tool_calls = []
+        if hasattr(response, 'tool_calls') and response.tool_calls:
+            for tool_call in response.tool_calls:
+                tool_calls.append({
+                    'name': tool_call['name'],
+                    'args': tool_call['args']
+                })
+        return tool_calls
+
+    def _execute_tool_calls(self, tool_calls: List[Dict]) -> List[str]:
+        """Execute tool calls and return results"""
+        results = []
+        for tool_call in tool_calls:
+            tool_name = tool_call['name']
+            tool_args = tool_call['args']
+            
+            if tool_name in self.tool_map:
+                try:
+                    tool = self.tool_map[tool_name]
+                    result = tool.invoke(tool_args)
+                    results.append(f"Tool {tool_name} result: {result}")
+                except Exception as e:
+                    results.append(f"Tool {tool_name} error: {str(e)}")
+            else:
+                results.append(f"Unknown tool: {tool_name}")
+        
+        return results
+
+    async def answer(self, question: str) -> str:
+        """Improved answer method with better error handling and tool usage"""
+        print(f"Processing question: {question[:100]}...")
+        
+        try:
+            # Create system prompt for better instruction following
+            system_prompt = """You are a helpful AI assistant with access to various tools. 
+            When answering questions, use the appropriate tools when needed and provide clear, concise answers.
+            If you need to perform calculations, use the math tools available.
+            If you need current information, use the web search tool.
+            Always provide a final answer after using tools."""
+            
+            messages = [
+                HumanMessage(content=f"{system_prompt}\n\nQuestion: {question}")
+            ]
+            
+            # Initial response
+            response = await asyncio.to_thread(self.chat_with_tools.invoke, messages)
+            
+            # Handle tool calls if present
+            max_iterations = 3
+            iteration = 0
+            
+            while iteration < max_iterations:
+                tool_calls = self._extract_tool_calls(response)
+                
+                if not tool_calls:
+                    break
+                
+                # Execute tool calls
+                tool_results = self._execute_tool_calls(tool_calls)
+                
+                # Add tool results to conversation
+                messages.append(AIMessage(content=response.content))
+                messages.append(HumanMessage(content=f"Tool results: {'; '.join(tool_results)}. Please provide a final answer based on these results."))
+                
+                # Get next response
+                response = await asyncio.to_thread(self.chat_with_tools.invoke, messages)
+                iteration += 1
+            
+            # Extract final answer
+            final_answer = response.content.strip()
+            
+            # Clean up the response - remove any tool call artifacts
+            if "Tool " in final_answer and "result:" in final_answer:
+                # Try to extract just the final answer part
+                lines = final_answer.split('\n')
+                for line in reversed(lines):
+                    if line.strip() and not line.startswith('Tool ') and not 'result:' in line:
+                        final_answer = line.strip()
+                        break
+            
+            return final_answer
+            
+        except Exception as e:
+            print(f"Error in answer method: {e}")
+            return f"Error processing question: {str(e)}"
+
+    def answer_sync(self, question: str) -> str:
+        """Synchronous version of answer method"""
+        try:
+            return asyncio.run(self.answer(question))
+        except Exception as e:
+            print(f"Error in sync answer: {e}")
+            return f"Error: {str(e)}"
+
+async def process_questions_batch(agent, questions_batch, semaphore):
+    """Process a batch of questions with rate limiting"""
+    results = []
+    
+    async def process_single_question(task_id, question):
+        async with semaphore:
+            try:
+                # Check cache first
+                cache_key = f"{task_id}_{hash(question)}"
+                if cache_key in answer_cache:
+                    print(f"Using cached answer for task {task_id}")
+                    return task_id, question, answer_cache[cache_key], None
+                
+                answer = await agent.answer(question)
+                
+                # Cache the result
+                answer_cache[cache_key] = answer
+                
+                return task_id, question, answer, None
+            except Exception as e:
+                print(f"Error processing task {task_id}: {e}")
+                return task_id, question, None, str(e)
+    
+    # Create semaphore for rate limiting
+    tasks = []
+    for item in questions_batch:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if task_id and question_text is not None:
+            tasks.append(process_single_question(task_id, question_text))
+    
+    if tasks:
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+    
+    return results
+
+async def run_agent_async_improved(agent, questions_data):
+    """Improved async processing with batching and caching"""
+    results_log, answers_payload = [], []
+    
+    # Create semaphore for rate limiting
+    semaphore = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
+    
+    # Process questions in batches
+    batch_size = 10
+    batches = [questions_data[i:i + batch_size] for i in range(0, len(questions_data), batch_size)]
+    
+    print(f"Processing {len(questions_data)} questions in {len(batches)} batches...")
+    
+    for i, batch in enumerate(batches):
+        print(f"Processing batch {i+1}/{len(batches)} ({len(batch)} questions)...")
+        
+        try:
+            batch_results = await process_questions_batch(agent, batch, semaphore)
+            
+            for result in batch_results:
+                if isinstance(result, Exception):
+                    print(f"Batch processing error: {result}")
+                    continue
+                
+                task_id, question, answer, error = result
+                
+                if error:
+                    print(f"Error in task {task_id}: {error}")
+                    results_log.append({
+                        "Task ID": task_id, 
+                        "Question": question[:100] + "..." if len(question) > 100 else question,
+                        "Submitted Answer": f"ERROR: {error}"
+                    })
+                else:
+                    answers_payload.append({"task_id": task_id, "submitted_answer": answer})
+                    results_log.append({
+                        "Task ID": task_id, 
+                        "Question": question[:100] + "..." if len(question) > 100 else question,
+                        "Submitted Answer": answer[:200] + "..." if len(answer) > 200 else answer
+                    })
+            
+            # Small delay between batches to be respectful
+            if i < len(batches) - 1:
+                await asyncio.sleep(1)
+                
+        except Exception as e:
+            print(f"Error processing batch {i+1}: {e}")
+            # Continue with next batch
+            continue
+    
+    return results_log, answers_payload
+
+def cache_answers(profile: gr.OAuthProfile | None):
+    """Cache answers without submitting"""
+    if not profile:
+        return "Please log in to Hugging Face first.", None
+    
+    username = profile.username
+    print(f"Caching answers for user: {username}")
+    
+    # Fetch questions
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        
+        if not questions_data:
+            return "No questions found.", None
+        
+        print(f"Fetched {len(questions_data)} questions for caching.")
+        
+        # Initialize agent
+        try:
+            agent = ImprovedAgent()
+        except Exception as e:
+            print(f"Full error details: {e}")
+            return f"Error initializing agent: {e}", None
+        
+        # Process questions
+        results_log, answers_payload = asyncio.run(run_agent_async_improved(agent, questions_data))
+        
+        # Store in global cache with username
+        answer_cache[f"user_{username}"] = answers_payload
+        
+        status = f"Cached {len(answers_payload)} answers for user {username}. Ready to submit!"
+        results_df = pd.DataFrame(results_log)
+        
+        return status, results_df
+        
+    except Exception as e:
+        print(f"Error caching answers: {e}")
+        return f"Error caching answers: {e}", None
+
+def submit_cached_answers(profile: gr.OAuthProfile | None):
+    """Submit previously cached answers"""
+    if not profile:
+        return "Please log in to Hugging Face first.", None
+    
+    username = profile.username
+    cache_key = f"user_{username}"
+    
+    if cache_key not in answer_cache:
+        return "No cached answers found. Please run 'Cache Answers' first.", None
+    
+    answers_payload = answer_cache[cache_key]
+    
+    if not answers_payload:
+        return "No answers to submit.", None
+    
+    # Get space info
+    space_id = os.getenv("SPACE_ID")
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown"
+    
+    # Submit
+    api_url = DEFAULT_API_URL
+    submit_url = f"{api_url}/submit"
+    
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+    
+    try:
+        print(f"Submitting {len(answers_payload)} cached answers...")
+        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.')}"
+        )
+        
+        # Clear cache after successful submission
+        if cache_key in answer_cache:
+            del answer_cache[cache_key]
+        
+        return final_status, None
+        
+    except Exception as e:
+        print(f"Submission error: {e}")
+        return f"Submission failed: {e}", None
+
+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:
+        # Using the retry function instead of direct request
+        response = make_request_with_retry(questions_url)
+        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:
+        # Using the retry function for submission as well
+        response = make_request_with_retry(submit_url, method="post", json_data=submission_data, timeout=60)
+        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)
+    

app.py

@@ -1,413 +1,108 @@
-import asyncio
 import inspect
-import json
 import os
-import time
-from typing import Any, Dict, List, Optional
 
 import gradio as gr
 import pandas as pd
 import requests
-from dotenv import load_dotenv
-from langchain_community.chat_models import ChatHuggingFace
-from langchain_community.llms import HuggingFaceEndpoint
-from langchain_core.messages import AIMessage, HumanMessage
-from langchain_core.tools import StructuredTool
-
-from tools import (absolute, add, divide, exponential, floor_divide,
-                   get_current_time_in_timezone, logarithm, modulus, multiply,
-                   power, roman_calculator_converter, square_root, subtract,
-                   web_search)
 
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-MAX_AGENT_ITERATIONS = 15
-MAX_CONCURRENT_REQUESTS = 5  # Limit concurrent requests to avoid overwhelming the API
-
-load_dotenv()
-HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
-
-# Quick test to see if tokens are available.
-print(f"Available env vars: {[k for k in os.environ.keys() if 'TOKEN' in k or 'HF' in k]}")
 
-# Global cache for answers
-answer_cache = {}
-
-class ImprovedAgent:
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
     def __init__(self):
-        if not HUGGINGFACEHUB_API_TOKEN:
-            raise ValueError("Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN.")
-
-        print("ImprovedAgent initialized.")
-        
-        # Initialize LLM with better parameters
-        self.llm = HuggingFaceEndpoint(
-            repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
-            huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
-            temperature=0.1,  # Lower temperature for more consistent responses
-            max_new_tokens=1024,
-            timeout=30,
-        )
-        
-        self.chat = ChatHuggingFace(llm=self.llm, verbose=False)
-        
-        # Initialize tools
-        self.tools = [
-            multiply, add, subtract, power, divide, modulus,
-            square_root, floor_divide, absolute, logarithm,
-            exponential, web_search, roman_calculator_converter,
-            get_current_time_in_timezone
-        ]
-        
-        self.chat_with_tools = self.chat.bind_tools(self.tools)
-        print(f"Total tools available: {len(self.tools)}")
-        
-        # Create tool mapping for easier access
-        self.tool_map = {tool.name: tool for tool in self.tools}
-
-    def _extract_tool_calls(self, response) -> List[Dict]:
-        """Extract tool calls from the response"""
-        tool_calls = []
-        if hasattr(response, 'tool_calls') and response.tool_calls:
-            for tool_call in response.tool_calls:
-                tool_calls.append({
-                    'name': tool_call['name'],
-                    'args': tool_call['args']
-                })
-        return tool_calls
-
-    def _execute_tool_calls(self, tool_calls: List[Dict]) -> List[str]:
-        """Execute tool calls and return results"""
-        results = []
-        for tool_call in tool_calls:
-            tool_name = tool_call['name']
-            tool_args = tool_call['args']
-            
-            if tool_name in self.tool_map:
-                try:
-                    tool = self.tool_map[tool_name]
-                    result = tool.invoke(tool_args)
-                    results.append(f"Tool {tool_name} result: {result}")
-                except Exception as e:
-                    results.append(f"Tool {tool_name} error: {str(e)}")
-            else:
-                results.append(f"Unknown tool: {tool_name}")
-        
-        return results
-
-    async def answer(self, question: str) -> str:
-        """Improved answer method with better error handling and tool usage"""
-        print(f"Processing question: {question[:100]}...")
-        
-        try:
-            # Create system prompt for better instruction following
-            system_prompt = """You are a helpful AI assistant with access to various tools. 
-            When answering questions, use the appropriate tools when needed and provide clear, concise answers.
-            If you need to perform calculations, use the math tools available.
-            If you need current information, use the web search tool.
-            Always provide a final answer after using tools."""
-            
-            messages = [
-                HumanMessage(content=f"{system_prompt}\n\nQuestion: {question}")
-            ]
-            
-            # Initial response
-            response = await asyncio.to_thread(self.chat_with_tools.invoke, messages)
-            
-            # Handle tool calls if present
-            max_iterations = 3
-            iteration = 0
-            
-            while iteration < max_iterations:
-                tool_calls = self._extract_tool_calls(response)
-                
-                if not tool_calls:
-                    break
-                
-                # Execute tool calls
-                tool_results = self._execute_tool_calls(tool_calls)
-                
-                # Add tool results to conversation
-                messages.append(AIMessage(content=response.content))
-                messages.append(HumanMessage(content=f"Tool results: {'; '.join(tool_results)}. Please provide a final answer based on these results."))
-                
-                # Get next response
-                response = await asyncio.to_thread(self.chat_with_tools.invoke, messages)
-                iteration += 1
-            
-            # Extract final answer
-            final_answer = response.content.strip()
-            
-            # Clean up the response - remove any tool call artifacts
-            if "Tool " in final_answer and "result:" in final_answer:
-                # Try to extract just the final answer part
-                lines = final_answer.split('\n')
-                for line in reversed(lines):
-                    if line.strip() and not line.startswith('Tool ') and not 'result:' in line:
-                        final_answer = line.strip()
-                        break
-            
-            return final_answer
-            
-        except Exception as e:
-            print(f"Error in answer method: {e}")
-            return f"Error processing question: {str(e)}"
-
-    def answer_sync(self, question: str) -> str:
-        """Synchronous version of answer method"""
-        try:
-            return asyncio.run(self.answer(question))
-        except Exception as e:
-            print(f"Error in sync answer: {e}")
-            return f"Error: {str(e)}"
-
-async def process_questions_batch(agent, questions_batch, semaphore):
-    """Process a batch of questions with rate limiting"""
-    results = []
-    
-    async def process_single_question(task_id, question):
-        async with semaphore:
-            try:
-                # Check cache first
-                cache_key = f"{task_id}_{hash(question)}"
-                if cache_key in answer_cache:
-                    print(f"Using cached answer for task {task_id}")
-                    return task_id, question, answer_cache[cache_key], None
-                
-                answer = await agent.answer(question)
-                
-                # Cache the result
-                answer_cache[cache_key] = answer
-                
-                return task_id, question, answer, None
-            except Exception as e:
-                print(f"Error processing task {task_id}: {e}")
-                return task_id, question, None, str(e)
-    
-    # Create semaphore for rate limiting
-    tasks = []
-    for item in questions_batch:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if task_id and question_text is not None:
-            tasks.append(process_single_question(task_id, question_text))
-    
-    if tasks:
-        results = await asyncio.gather(*tasks, return_exceptions=True)
-    
-    return results
-
-async def run_agent_async_improved(agent, questions_data):
-    """Improved async processing with batching and caching"""
-    results_log, answers_payload = [], []
-    
-    # Create semaphore for rate limiting
-    semaphore = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
-    
-    # Process questions in batches
-    batch_size = 10
-    batches = [questions_data[i:i + batch_size] for i in range(0, len(questions_data), batch_size)]
-    
-    print(f"Processing {len(questions_data)} questions in {len(batches)} batches...")
-    
-    for i, batch in enumerate(batches):
-        print(f"Processing batch {i+1}/{len(batches)} ({len(batch)} questions)...")
-        
-        try:
-            batch_results = await process_questions_batch(agent, batch, semaphore)
-            
-            for result in batch_results:
-                if isinstance(result, Exception):
-                    print(f"Batch processing error: {result}")
-                    continue
-                
-                task_id, question, answer, error = result
-                
-                if error:
-                    print(f"Error in task {task_id}: {error}")
-                    results_log.append({
-                        "Task ID": task_id, 
-                        "Question": question[:100] + "..." if len(question) > 100 else question,
-                        "Submitted Answer": f"ERROR: {error}"
-                    })
-                else:
-                    answers_payload.append({"task_id": task_id, "submitted_answer": answer})
-                    results_log.append({
-                        "Task ID": task_id, 
-                        "Question": question[:100] + "..." if len(question) > 100 else question,
-                        "Submitted Answer": answer[:200] + "..." if len(answer) > 200 else answer
-                    })
-            
-            # Small delay between batches to be respectful
-            if i < len(batches) - 1:
-                await asyncio.sleep(1)
-                
-        except Exception as e:
-            print(f"Error processing batch {i+1}: {e}")
-            # Continue with next batch
-            continue
-    
-    return results_log, answers_payload
-
-def cache_answers(profile: gr.OAuthProfile | None):
-    """Cache answers without submitting"""
-    if not profile:
-        return "Please log in to Hugging Face first.", None
-    
-    username = profile.username
-    print(f"Caching answers for user: {username}")
-    
-    # Fetch questions
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        
-        if not questions_data:
-            return "No questions found.", None
-        
-        print(f"Fetched {len(questions_data)} questions for caching.")
-        
-        # Initialize agent
-        try:
-            agent = ImprovedAgent()
-        except Exception as e:
-            print(f"Full error details: {e}")
-            return f"Error initializing agent: {e}", None
-        
-        # Process questions
-        results_log, answers_payload = asyncio.run(run_agent_async_improved(agent, questions_data))
-        
-        # Store in global cache with username
-        answer_cache[f"user_{username}"] = answers_payload
-        
-        status = f"Cached {len(answers_payload)} answers for user {username}. Ready to submit!"
-        results_df = pd.DataFrame(results_log)
-        
-        return status, results_df
-        
-    except Exception as e:
-        print(f"Error caching answers: {e}")
-        return f"Error caching answers: {e}", None
-
-def submit_cached_answers(profile: gr.OAuthProfile | None):
-    """Submit previously cached answers"""
-    if not profile:
-        return "Please log in to Hugging Face first.", None
-    
-    username = profile.username
-    cache_key = f"user_{username}"
-    
-    if cache_key not in answer_cache:
-        return "No cached answers found. Please run 'Cache Answers' first.", None
-    
-    answers_payload = answer_cache[cache_key]
-    
-    if not answers_payload:
-        return "No answers to submit.", None
-    
-    # Get space info
-    space_id = os.getenv("SPACE_ID")
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown"
-    
-    # Submit
-    api_url = DEFAULT_API_URL
-    submit_url = f"{api_url}/submit"
-    
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
-    
-    try:
-        print(f"Submitting {len(answers_payload)} cached answers...")
-        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.')}"
-        )
-        
-        # Clear cache after successful submission
-        if cache_key in answer_cache:
-            del answer_cache[cache_key]
-        
-        return final_status, None
-        
-    except Exception as e:
-        print(f"Submission error: {e}")
-        return f"Submission failed: {e}", None
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """Original function - now improved with better error handling"""
-    if not profile:
-        return "Please log in to Hugging Face first.", None
-
-    username = profile.username
-    print(f"User logged in: {username}")
+        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"
 
-    # Initialize agent
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = ImprovedAgent()
+        agent = BasicAgent()
     except Exception as e:
-        print(f"Error initializing agent: {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)
 
-    # Get space info
-    space_id = os.getenv("SPACE_ID")
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown"
-
-    # Fetch questions
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
     try:
-        print(f"Fetching questions from: {questions_url}")
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
-        
         if not questions_data:
-            return "No questions found.", 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 Exception as e:
+    except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
-
-    # Process questions
-    try:
-        results_log, answers_payload = asyncio.run(run_agent_async_improved(agent, questions_data))
+    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"Error processing questions: {e}")
-        return f"Error processing questions: {e}", None
+        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:
-        return "No answers generated.", pd.DataFrame(results_log) if results_log else None
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # Submit answers
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
+    # 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:
-        print(f"Submitting {len(answers_payload)} answers...")
         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"
@@ -415,81 +110,89 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             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:
-        print(f"Submission error: {e}")
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
         results_df = pd.DataFrame(results_log)
-        return f"Submission failed: {e}", results_df
+        return status_message, results_df
+
 
-# --- Build Gradio Interface ---
-with gr.Blocks(title="Improved Agent Evaluation") as demo:
-    gr.Markdown("# Improved Agent Evaluation Runner")
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
 
-        1. Log in to your Hugging Face account using the button below.
-        2. **Recommended**: Use "Cache Answers" to process all questions first, then "Submit Cached Answers" to submit them.
-        3. **Alternative**: Use "Run & Submit All" for the original one-step process.
-
-        **Improvements:**
-        - ✅ Async processing with rate limiting
-        - ✅ Answer caching for faster resubmissions
-        - ✅ Better error handling and recovery
-        - ✅ Batch processing to avoid timeouts
-        - ✅ Improved tool usage and response parsing
+        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()
 
-    with gr.Row():
-        cache_button = gr.Button("🔄 Cache Answers", variant="secondary")
-        submit_button = gr.Button("📤 Submit Cached Answers", variant="primary")
-        run_all_button = gr.Button("🚀 Run & Submit All", variant="secondary")
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    status_output = gr.Textbox(label="Status", lines=6, interactive=False)
+    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)
 
-    # Wire up the buttons
-    cache_button.click(
-        fn=cache_answers,
-        outputs=[status_output, results_table]
-    )
-    
-    submit_button.click(
-        fn=submit_cached_answers,
-        outputs=[status_output, results_table]
-    )
-    
-    run_all_button.click(
+    run_button.click(
         fn=run_and_submit_all,
         outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " Improved App Starting " + "-"*30)
-    
-    space_host = os.getenv("SPACE_HOST")
-    space_id = os.getenv("SPACE_ID")
-
-    if space_host:
-        print(f"✅ SPACE_HOST: {space_host}")
-        print(f"   Runtime URL: https://{space_host}.hf.space")
+    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  Running locally - SPACE_HOST not found.")
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id:
-        print(f"✅ SPACE_ID: {space_id}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id}")
+    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 not found.")
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
 
-    print("-" * 76 + "\n")
-    print("Launching Improved Gradio Interface...")
-    demo.launch(debug=True, share=False)
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)
+