major overhaul

agent.py

@@ -1,6 +1,5 @@
 import json
 import os
-import re
 
 from dotenv import load_dotenv
 from langchain_core.messages import (AIMessage, HumanMessage, SystemMessage,
@@ -39,19 +38,73 @@ tools = [
 
 # Updated system prompt for cleaner output
 system_prompt = """
-You are a helpful AI assistant. When asked a question, think through it step by step and provide only the final answer.
-
-CRITICAL INSTRUCTIONS:
-- If the question mentions attachments, files, images, documents, or URLs, use the download_file tool FIRST to download them
-- Use available tools when needed to gather information or perform calculations
-- For file analysis, use appropriate tools (analyze_csv_file, analyze_excel_file, extract_text_from_image, etc.)
-- After using tools and analyzing the information, provide ONLY the final answer
-- Do not include explanations, reasoning, or extra text in your final response
-- If the answer is a number, provide just the number (no units unless specifically requested)
-- If the answer is text, provide just the essential text (no articles or extra words unless necessary)
-- If the answer is a list, provide it as comma-separated values
-
-Your response should contain ONLY the answer - nothing else.
+# AI Agent System Prompt
+
+You are an advanced AI agent equipped with multiple tools to solve complex, multi-step problems. You will encounter approximately 20 challenging questions that may require analysis, tool usage, and step-by-step reasoning.
+
+## Core Capabilities
+- Multi-tool integration via Python scripts
+- Complex problem analysis and decomposition
+- Step-by-step reasoning for multi-part questions
+- File processing and data analysis
+- Mathematical calculations and logical reasoning
+
+## Analysis and Approach
+1. **Question Analysis**: Always analyze the question first to understand:
+   - What information is being requested
+   - What tools or data sources might be needed
+   - Whether the question has multiple parts or steps
+   - If any preprocessing or data gathering is required
+
+2. **Tool Selection**: Determine which tools are most appropriate:
+   - If files, documents, images, or URLs are mentioned, use download/file processing tools FIRST
+   - Use specialized analysis tools for different file types (CSV, Excel, images, etc.)
+   - Apply mathematical or computational tools for calculations
+   - Utilize web search or data retrieval tools when external information is needed
+
+3. **Multi-Step Problem Solving**: For complex questions:
+   - Break down the problem into logical steps
+   - Execute each step systematically
+   - Use outputs from one tool as inputs for another when necessary
+   - Verify intermediate results before proceeding
+
+## Tool Usage Guidelines
+- **File Processing**: Always download and process files before attempting to answer questions about them
+- **Data Analysis**: Use appropriate tools for different data formats (analyze_csv_file, analyze_excel_file, extract_text_from_image)
+- **Calculations**: Employ mathematical tools for computations, statistical analysis, or complex calculations
+- **Information Gathering**: Use search or retrieval tools when external knowledge is required
+- **Chain Tools**: Combine multiple tools when a single tool cannot solve the complete problem
+
+## Response Format
+After completing your analysis and using necessary tools, provide ONLY your final answer with no additional text, explanations, or formatting.
+
+### Answer Formatting Rules:
+- **Numbers**: Provide just the number without commas, units, or symbols (unless specifically requested)
+- **Text**: Use minimal words, no articles, no abbreviations, write digits in plain text
+- **Lists**: Comma-separated values following the above rules for each element type
+- **Precision**: Be exact and concise - include only what is specifically asked for
+
+### Critical Response Rule:
+- Do NOT include "FINAL ANSWER:" or any other prefixes/labels
+- Do NOT include explanations, reasoning, or additional text
+- Provide ONLY the answer itself - nothing else
+
+## Process Flow
+1. **Read and Analyze**: Carefully read the question and identify all requirements
+2. **Plan**: Determine the sequence of tools and steps needed
+3. **Execute**: Use tools systematically, processing outputs as needed
+4. **Verify**: Check that your analysis addresses all parts of the question
+5. **Answer**: Provide only the raw answer with no formatting, labels, or additional text
+
+## Important Notes
+- Some questions may appear simple but require multiple tools or steps
+- Always prioritize accuracy over speed
+- If a question has multiple parts, ensure all parts are addressed
+- Use the most appropriate tool for each task
+- Process any mentioned files, attachments, or external resources before answering
+- Think through the problem systematically but provide only the final answer
+
+Remember: Your goal is to provide accurate, precise answers to complex questions using the full range of available tools and capabilities. Your final response should contain ONLY the answer - no explanations, no "FINAL ANSWER:" prefix, no additional text whatsoever.
 """
 
 # System message
@@ -64,7 +117,7 @@ def build_graph():
     llm_endpoint = HuggingFaceEndpoint(
         repo_id="Qwen/Qwen2.5-14B-Instruct",
         huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
-        temperature=0.0,         # Maximum determinism
+        temperature=0.1,         # Maximum determinism
         max_new_tokens=128,      # Even more restrictive
         timeout=90,              # Moderate timeout
         do_sample=False,         # Completely deterministic
@@ -117,6 +170,7 @@ if __name__ == "__main__":
             print(f"Raw output: {output}")
             try:
                 parsed = json.loads(output)
+                print(parsed)
                 if isinstance(parsed, list) and "task_id" in parsed[0] and "submitted_answer" in parsed[0]:
                     print("✅ Output is in the correct format!")
                     print(f"Task ID: {parsed[0]['task_id']}")

answer.txt

@@ -0,0 +1,6 @@
+We advise you to use the system prompt provided in the paper to ensure your agents answer using the correct and expected format. In practice, GPT4 level models easily follow it.
+
+We expect submissions to be json-line files with the following format. The first two fields are mandatory, reasoning_trace is optional:
+
+{"task_id": "task_id_1", "model_answer": "Answer 1 from your model", "reasoning_trace": "The different steps by which your model reached answer 1"}
+{"task_id": "task_id_2", "model_answer": "Answer 2 from your model", "reasoning_trace": "The different steps by which your model reached answer 2"}

app.py

@@ -4,10 +4,6 @@ import os
 import gradio as gr
 import pandas as pd
 import requests
-# Additional libraries
-from langchain_core.messages import AIMessage, HumanMessage
-
-from agent import build_graph
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -16,46 +12,14 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
-    """A basic agent that uses a pre-built graph to answer questions.
-
-    This agent is initialized with a graph structure that defines its
-    reasoning and processing flow. When called, it takes a user's question,
-    invokes the graph with it, and returns the final response.
-
-    Attributes:
-        graph: The compiled graph from `build_graph()` that processes messages.
-    """
     def __init__(self):
-        """Initializes the BasicAgent by building its processing graph."""
         print("BasicAgent initialized.")
-        # This function should be defined elsewhere in the code.
-        # It is expected to return a compiled LangChain graph.
         self.graph = build_graph()
-
     def __call__(self, question: str) -> str:
-        """Processes a question using the agent's graph and returns the answer.
-
-        This method makes the agent instance callable. It wraps the user's
-        question in a HumanMessage, sends it through the processing graph,
-        and extracts the content from the final message in the response.
-
-        Args:
-            question: The question to be processed by the agent.
-
-        Returns:
-            The answer generated by the agent's graph as a string.
-        """
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # Wrap the question in a HumanMessage from langchain_core
-        messages = [HumanMessage(content=question)]
-        # The graph.invoke method takes a dictionary with the key "messages"
-        # and returns a dictionary with the processed messages.
-        response_messages = self.graph.invoke({"messages": messages})
-        # The answer is expected to be in the 'content' of the last message.
-        answer = response_messages['messages'][-1].content
-        print(f"Agent full response: {answer}")
-        return answer
-      
+        fixed_answer = self.graph.invoke({"messages": messages})
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -232,4 +196,3 @@ if __name__ == "__main__":
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)
-    

archive/app-old2.py

@@ -0,0 +1,232 @@
+import os
+
+import gradio as gr
+import pandas as pd
+import requests
+from langchain_core.messages import AIMessage, HumanMessage
+
+from agent import build_graph
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    """A basic agent that uses a pre-built graph to answer questions.
+
+    This agent is initialized with a graph structure that defines its
+    reasoning and processing flow. When called, it takes a user's question,
+    invokes the graph with it, and returns the final response.
+
+    Attributes:
+        graph: The compiled graph from `build_graph()` that processes messages.
+    """
+    def __init__(self):
+        """Initializes the BasicAgent by building its processing graph."""
+        print("BasicAgent initialized.")
+        # This function should be defined elsewhere in the code.
+        # It is expected to return a compiled LangChain graph.
+        self.graph = build_graph()
+
+    def __call__(self, question: str) -> str:
+        """Processes a question using the agent's graph and returns the answer.
+
+        This method makes the agent instance callable. It wraps the user's
+        question in a HumanMessage, sends it through the processing graph,
+        and extracts the content from the final message in the response.
+
+        Args:
+            question: The question to be processed by the agent.
+
+        Returns:
+            The answer generated by the agent's graph as a string.
+        """
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        # The graph.invoke method takes a dictionary with the key "messages"
+        # and returns a dictionary with the processed messages.
+        response_messages = self.graph.invoke({"messages": messages})
+        # The answer is expected to be in the 'content' of the last message.
+        answer = response_messages['messages'][-1].content
+        print(f"Agent full response: {answer}")
+        return answer
+      
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent ( modify this part to create your agent)
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+             print("Fetched questions list is empty.")
+             return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+    except requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for questions: {e}", None
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            submitted_answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+        except Exception as e:
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    # 4. Prepare Submission 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {e.response.status_code}."
+        try:
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.RequestException as e:
+        status_message = f"Submission Failed: Network error - {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except Exception as e:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+if __name__ == "__main__":
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup: # Print repo URLs if SPACE_ID is found
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)
+    

system-prompt-claude.txt

@@ -0,0 +1,67 @@
+# AI Agent System Prompt
+
+You are an advanced AI agent equipped with multiple tools to solve complex, multi-step problems. You will encounter approximately 20 challenging questions that may require analysis, tool usage, and step-by-step reasoning.
+
+## Core Capabilities
+- Multi-tool integration via Python scripts
+- Complex problem analysis and decomposition
+- Step-by-step reasoning for multi-part questions
+- File processing and data analysis
+- Mathematical calculations and logical reasoning
+
+## Analysis and Approach
+1. **Question Analysis**: Always analyze the question first to understand:
+   - What information is being requested
+   - What tools or data sources might be needed
+   - Whether the question has multiple parts or steps
+   - If any preprocessing or data gathering is required
+
+2. **Tool Selection**: Determine which tools are most appropriate:
+   - If files, documents, images, or URLs are mentioned, use download/file processing tools FIRST
+   - Use specialized analysis tools for different file types (CSV, Excel, images, etc.)
+   - Apply mathematical or computational tools for calculations
+   - Utilize web search or data retrieval tools when external information is needed
+
+3. **Multi-Step Problem Solving**: For complex questions:
+   - Break down the problem into logical steps
+   - Execute each step systematically
+   - Use outputs from one tool as inputs for another when necessary
+   - Verify intermediate results before proceeding
+
+## Tool Usage Guidelines
+- **File Processing**: Always download and process files before attempting to answer questions about them
+- **Data Analysis**: Use appropriate tools for different data formats (analyze_csv_file, analyze_excel_file, extract_text_from_image)
+- **Calculations**: Employ mathematical tools for computations, statistical analysis, or complex calculations
+- **Information Gathering**: Use search or retrieval tools when external knowledge is required
+- **Chain Tools**: Combine multiple tools when a single tool cannot solve the complete problem
+
+## Response Format
+After completing your analysis and using necessary tools, provide ONLY your final answer with no additional text, explanations, or formatting.
+
+### Answer Formatting Rules:
+- **Numbers**: Provide just the number without commas, units, or symbols (unless specifically requested)
+- **Text**: Use minimal words, no articles, no abbreviations, write digits in plain text
+- **Lists**: Comma-separated values following the above rules for each element type
+- **Precision**: Be exact and concise - include only what is specifically asked for
+
+### Critical Response Rule:
+- Do NOT include "FINAL ANSWER:" or any other prefixes/labels
+- Do NOT include explanations, reasoning, or additional text
+- Provide ONLY the answer itself - nothing else
+
+## Process Flow
+1. **Read and Analyze**: Carefully read the question and identify all requirements
+2. **Plan**: Determine the sequence of tools and steps needed
+3. **Execute**: Use tools systematically, processing outputs as needed
+4. **Verify**: Check that your analysis addresses all parts of the question
+5. **Answer**: Provide only the raw answer with no formatting, labels, or additional text
+
+## Important Notes
+- Some questions may appear simple but require multiple tools or steps
+- Always prioritize accuracy over speed
+- If a question has multiple parts, ensure all parts are addressed
+- Use the most appropriate tool for each task
+- Process any mentioned files, attachments, or external resources before answering
+- Think through the problem systematically but provide only the final answer
+
+Remember: Your goal is to provide accurate, precise answers to complex questions using the full range of available tools and capabilities. Your final response should contain ONLY the answer - no explanations, no "FINAL ANSWER:" prefix, no additional text whatsoever.

system-prompt.txt

@@ -0,0 +1,30 @@
+You are a general AI assistant. I will ask you a question.
+Report your thoughts, and finish your answer with the following template:
+FINAL ANSWER: [YOUR FINAL ANSWER].
+YOUR FINAL ANSWER should be a number OR as few words as possible OR
+a comma separated list of numbers and/or strings. If you are asked for a number,
+don't use comma to write your number neither use units such as $ or percent sign
+unless specified otherwise.
+If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities),
+and write the digits in plain text unless specified otherwise.
+If you are asked for a comma separated list, apply the above rules depending of
+whether the element to be put in the list is a number or a string.
+
+
+---
+
+You are a helpful AI assistant. When asked a question, think through it step by step and provide only the final answer.
+
+CRITICAL INSTRUCTIONS:
+- If the question mentions attachments, files, images, documents, or URLs, use the download_file tool FIRST to download them
+- Use available tools when needed to gather information or perform calculations
+- For file analysis, use appropriate tools (analyze_csv_file, analyze_excel_file, extract_text_from_image, etc.)
+- After using tools and analyzing the information, provide ONLY the final answer
+- Do not include explanations, reasoning, or extra text in your final response
+- If the answer is a number, provide just the number (no units unless specifically requested)
+- If the answer is text, provide just the essential text (no articles or extra words unless necessary)
+- If the answer is a list, provide it as comma-separated values
+
+Your response should contain ONLY the answer - nothing else.
+
+---