app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import json
from duckduckgo_search import DDGS
import ast # For safely evaluating literal structures if needed, though JSON is preferred

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
HF_API_URL = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-V3"
TEST_AGENT_KEY = os.getenv("TEST_AGENT_KEY")  # Make sure you add this secret in your Hugging Face Space
MODEL_NAME = "deepseek-ai/Deepseek-V3"

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        self.api_key = os.getenv("TEST_AGENT_KEY")
        if not self.api_key:
            raise ValueError("OpenRouter API Key not found.")
        self.scorer_api_url = DEFAULT_API_URL # For fetching files/submitting
        self.llm_api_url = HF_API_URL
        self.model_name = MODEL_NAME

        # Load system prompt
        try:
            with open("prompt.txt", "r") as file:
                self.system_prompt = file.read().strip()
        except FileNotFoundError:
            print("Error: prompt.txt not found. Using a default system prompt.")
            self.system_prompt = "You are a helpful AI assistant. Please answer the user's questions. Use tools if necessary by outputting TOOL: {\"name\": \"tool_name\", \"args\": {\"arg_name\": \"value\"}}. When you have the final answer, output ANSWER: your_final_answer."
        except Exception as e:
            print(f"Error loading prompt.txt: {e}. Using a default system prompt.")
            self.system_prompt = "You are a helpful AI assistant. Please answer the user's questions. Use tools if necessary by outputting TOOL: {\"name\": \"tool_name\", \"args\": {\"arg_name\": \"value\"}}. When you have the final answer, output ANSWER: your_final_answer."


        # Define tools
        self.tools = {
            "web_search_tool": web_search_tool,
            "decimal_approximation_tool": decimal_approximation_tool,
            "get_files_task_id_tool": get_files_task_id_tool
            # image_processing_tool removed as requested
        }
        print(f"Agent tools initialized: {list(self.tools.keys())}")

    def _call_llm(self, conversation_history: list) -> str:
        print(f"Calling LLM. Conversation history length: {len(conversation_history)}")
    
        # Hugging Face headers (simpler)
        headers = {
            "Authorization": f"Bearer {self.api_key}",  # should be your HF token
            "Content-Type": "application/json"
        }
    
        # Format conversation into a single prompt string
        # You can improve this formatting later if needed
        prompt = ""
        for turn in conversation_history:
            role = turn.get("role", "user").capitalize()
            content = turn.get("content", "")
            prompt += f"{role}: {content}\n"
        prompt += "Assistant:"
    
        # Payload for Hugging Face
        payload = {
            "inputs": prompt,
            "parameters": {
                "temperature": 0.7,
                "max_new_tokens": 512
            }
        }
    
        url = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-V3"
        response = requests.post(url, headers=headers, json=payload)
    
        try:
            return response.json()[0]["generated_text"].split("Assistant:")[-1].strip()
        except Exception:
            return f"Error: {response.text}"
    
    def __call__(self, question_data: dict) -> str:
        task_id = question_data.get("task_id")
        question_text = question_data.get("question")
        print(f"Agent received task_id: {task_id}, question (first 50 chars): {str(question_text)[:50]}...")

        if not task_id or question_text is None:
            print("Error: Missing task_id or question in agent input.")
            return "Error: Invalid input to agent."

        conversation = [
            {"role": "system", "content": self.system_prompt},
            {"role": "user", "content": question_text}
        ]

        max_loops = 1000 # Prevent infinite loops
        for loop_count in range(max_loops):
            print(f"\nAgent Loop: {loop_count + 1}")
            llm_response = self._call_llm(conversation)

            if llm_response.startswith("ANSWER:"):
                answer = llm_response[len("ANSWER:"):].strip()
                print(f"Agent returning final answer: {answer}")
                return answer
            elif llm_response.startswith("TOOL:"):
                tool_call_str = llm_response[len("TOOL:"):].strip()
                print(f"Attempting tool call: {tool_call_str}")
                try:
                    tool_data = json.loads(tool_call_str) # Parse the JSON string
                    tool_name = tool_data.get("name")
                    tool_args_dict = tool_data.get("args", {})

                    if tool_name in self.tools:
                        print(f"Executing tool: {tool_name} with args: {tool_args_dict}")
                        # Special handling for get_files_task_id_tool if it doesn't take generic args
                        if tool_name == "get_files_task_id_tool":
                            # Ensure task_id is passed correctly, not from LLM args unless intended
                            observation = self.tools[tool_name](task_id)
                        else:
                            observation = self.tools[tool_name](**tool_args_dict)

                        print(f"Tool observation: {str(observation)[:200]}...")
                        conversation.append({"role": "assistant", "content": llm_response}) # LLM's tool request
                        conversation.append({"role": "user", "content": f"Observation: {observation}"}) # Tool result
                    else:
                        print(f"Error: Unknown tool name: {tool_name}")
                        conversation.append({"role": "user", "content": f"Error: Unknown tool '{tool_name}'. Available tools are: {', '.join(self.tools.keys())}."})
                except json.JSONDecodeError as e:
                    print(f"Error decoding JSON for tool call: {e} - String was: {tool_call_str}")
                    conversation.append({"role": "user", "content": f"Error: Invalid tool call format. Expected JSON. {e}"})
                except Exception as e:
                    print(f"Error executing tool or processing its call: {e}")
                    conversation.append({"role": "assistant", "content": llm_response}) # LLM's tool request
                    conversation.append({"role": "user", "content": f"Error executing tool {tool_name}: {e}"})
            else:
                # If the LLM doesn't use the specified prefixes, treat its response as a potential direct answer or a misstep.
                # Could also be a clarification question from the LLM.
                print(f"LLM response did not start with ANSWER: or TOOL:. Treating as intermediate thought or error. Response: {llm_response[:100]}")
                # Adding it as an assistant message and prompting for a structured response
                conversation.append({"role": "assistant", "content": llm_response})
                conversation.append({"role": "user", "content": "Please respond with either 'TOOL: {\"name\": \"tool_name\", \"args\": {}}' or 'ANSWER: your_final_answer'."})

            if loop_count == max_loops - 1:
                print("Agent reached max loops. Returning last LLM response or error.")
                return f"Error: Agent reached maximum iteration limit. Last response: {llm_response}"

        return "Error: Agent loop completed without returning an answer."


# --- Tool Definitions ---
def web_search_tool(search_terms: str) -> str:
    """
    Retrieves information from the internet using DuckDuckGo Search and returns results in JSON format.
    Args: search_terms (str): The search query to look up.
    Returns: str: JSON string containing search results.
    """
    print(f"Web search tool called with terms: {search_terms}")
    try:
        with DDGS() as ddgs:
            results = [r for r in ddgs.text(search_terms, max_results=3)]
        return json.dumps({"results": results}) # Ensure it's a JSON string
    except Exception as e:
        print(f"Web search failed: {e}")
        return json.dumps({"error": f"Search failed: {str(e)}"})

def decimal_approximation_tool(number: float, decimals: int = 1) -> float:
    """
    Adjusts a numerical answer to the specified number of decimal places.
    Args:
        number (float): The number to round.
        decimals (int): Number of decimal places to round to (default is 1).
    Returns: float: The rounded number.
    """
    print(f"Decimal approximation tool called with number: {number}, decimals: {decimals}")
    try:
        return round(float(number), int(decimals))
    except Exception as e:
        print(f"Decimal approximation failed: {e}")
        return f"Error in decimal_approximation_tool: {str(e)}" # Return error as string

def get_files_task_id_tool(task_id: str) -> str:
    """
    Downloads the file associated with the given task_id by making an API call.
    Args: task_id (str): The ID of the task to fetch the file for.
    Returns: str: The file content as a string or an error message.
    """
    print(f"Get files tool called with task_id: {task_id}")
    try:
        # Assuming DEFAULT_API_URL is the base for the /files endpoint
        file_url = f"{DEFAULT_API_URL}/files/{task_id}"
        response = requests.get(file_url, timeout=30)
        if response.status_code == 200:
            return response.text
        else:
            return f"Error fetching file for task_id {task_id}: Status {response.status_code}, Response: {response.text}"
    except Exception as e:
        print(f"Error in get_files_task_id_tool: {e}")
        return f"Error fetching file for task_id {task_id}: {str(e)}"

# --- Gradio App ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    scorer_api_url = DEFAULT_API_URL
    questions_url = f"{scorer_api_url}/questions"
    submit_url = f"{scorer_api_url}/submit"

    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "local_run_code_link_not_available"
    print(f"Agent code link: {agent_code}")

    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            print("Fetched questions list is empty.")
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}. 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

    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:
            # Pass the whole item dictionary to the agent
            submitted_answer = agent(item)
            answers_payload.append({"task_id": task_id, "submitted_answer": str(submitted_answer)}) # Ensure answer is string
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": str(submitted_answer)})
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            import traceback
            traceback.print_exc() # Print full traceback for agent errors
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
    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]}"
        final_status = f"Submission Failed: {error_detail}"
        print(final_status)
    except requests.exceptions.Timeout:
        final_status = "Submission Failed: The request timed out."
        print(final_status)
    except requests.exceptions.RequestException as e:
        final_status = f"Submission Failed: Network error - {e}"
        print(final_status)
    except Exception as e:
        final_status = f"An unexpected error occurred during submission: {e}"
        print(final_status)

    results_df = pd.DataFrame(results_log)
    return final_status, results_df


with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1.  Ensure your `TEST_AGENT_KEY` (OpenRouter API Key) is set in your Hugging Face Space secrets or `.env` file.
        2.  Modify `prompt.txt` to guide the agent, especially for tool use and answer formatting. Remove references to the old `image_processing_tool`.
        3.  Log in to your Hugging Face account using the button below.
        4.  Click 'Run Evaluation & Submit All Answers'.

        ---
        **Disclaimers:**
        Agent execution can take time. This setup is a starting point.
        """
    )

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table],
        api_name="run_evaluation" # Added api_name for programmatic access if needed
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)