app.py

import getpass
import os
import gradio as gr
import requests
import inspect
import pandas as pd
from langgraph.graph import START, StateGraph, END
from langgraph.prebuilt import ToolNode
from typing_extensions import TypedDict
from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage
from langchain.schema import AIMessage
from typing import Annotated, List, Any, Optional
from langgraph.graph.message import add_messages
from tools import extract_text, describe_image, transcribe_audio, web_search, read_file
from langchain_openai import AzureChatOpenAI
from azure.identity import EnvironmentCredential
from langchain_google_genai import ChatGoogleGenerativeAI

#DEFINE AGENT STATE
class AgentState(TypedDict):
    # The input document
    messages: Annotated[List[AnyMessage], add_messages]
    task_id: Optional[str]  # The task ID for the agent
    file_name:  Optional[str]  # Contains file name if the task is file-based
    local_file_name:  Optional[str]  # Contains local file name if file has been downloaded
    final_response: Optional[str]  # The final response from the agent

#CREATE STRUCTURED OUTPUT
class FinalAnswer(TypedDict):
    """Always use this tool to structure your response to the user."""
    final_answer: Annotated[str,...,"The final answer provided by the agent, formatted as per the instructions."]

# (Keep Constants as is)
# --- 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:
    def __init__(self):
        print("BasicAgent initialized.")
        self.tools=[
            extract_text,
            web_search,
            describe_image,
            read_file,
            transcribe_audio
        ]        
        self.google_llm = ChatGoogleGenerativeAI(model='gemini-2.0-flash-lite')
        self.llm_with_tools = self.google_llm.bind_tools(self.tools)
        self.graph = self.create_graph()        

    def __call__(self, task_id: str, question: str, file_name: str = None, local_file_name: str = None) -> 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
        state = {
            "messages": [
                HumanMessage(content=question)
            ],
            "task_id": task_id,
            "file_name": file_name,
            "local_file_name": local_file_name
        }
        print(f"Initial state: {state}")
        response = self.graph.invoke(state)
        return response['final_response']['final_answer']
        
    def get_access_token(self):
        credential = EnvironmentCredential()
        access_token = credential.get_token("https://cognitiveservices.azure.com/.default")
        return access_token.token
    
    #CREATE ASSISTANT FUNCTION
    def assistant(self, state: AgentState):
        local_file_name=state.get("local_file_name","NOT AVAILABLE")
        
        sys_msg = SystemMessage(content=f"""
        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 may be asked to review an image or file, if available, the file location is: {local_file_name}.
        """)
        new_state = state.copy()
        new_state["messages"] = [self.llm_with_tools.invoke([sys_msg] + state["messages"])]

        return new_state

    #CREATE FILE DOWNLOADER
    def download_file(self, state: AgentState) -> None:
        """
        Downloads a file from the given URL and saves it to the specified path.

        Args:
            state: The AgentState.
        """
        print(f'download_file called with state:{state}')

        new_state = state.copy()

        if "file_name" in state and state.get("file_name") is not None and state.get("file_name", '') != '':
            file_name = state["file_name"]
            task_id = state.get("task_id", "unknown_task")
            url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
            save_path = f"./attachments/{file_name}"
            response = requests.get(url, stream=True)
            response.raise_for_status()
            with open(save_path, 'wb') as f:
                for chunk in response.iter_content(chunk_size=8192):
                    if chunk:
                        f.write(chunk)
            print(f"File downloaded and saved to {save_path}")
            new_state["local_file_name"] = save_path
        else:
            print("No file name provided in state, skipping download.")
        return new_state

    # STRUCTURED OUTPUT AGENT
    def structured_output(self, state: AgentState):
        """
        Create a structured output from the agent's response.

        Args:
            state: The AgentState containing the agent's messages.

        Returns:
            A string representing the structured output.
        """
        response = self.google_llm.with_structured_output(FinalAnswer).invoke(state['messages'][-1].content)
        return {'final_response': response}
    
    # ROUTING FUNCTION
    def routing_function(self, state: AgentState) -> str:
        """
        Routing function to determine the next step based on the assistant's last message.
        
        Args:
            state: The AgentState containing the agent's messages.
        
        Returns:
            The next node to route to.
        """
        if not state["messages"]:
            return "END"
        
        last_message = state["messages"][-1]
        
        if isinstance(last_message, AIMessage) and last_message.tool_calls:
            return "tools"
        
        return "structured_output"
    
    #BUILD GRAPH
    def create_graph(self):
        builder = StateGraph(AgentState)

        # Define nodes: these do the work
        builder.add_node("downloader", self.download_file)
        builder.add_node("assistant", self.assistant)
        builder.add_node("tools", ToolNode(self.tools))
        builder.add_node("structured_output", self.structured_output)

        # Define edges: these determine how the control flow moves
        builder.add_edge(START, "downloader")
        builder.add_edge("downloader", "assistant")
        builder.add_conditional_edges(
            "assistant",
            # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools
            # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END
            self.routing_function,
            ["tools", "structured_output"]
        )
        builder.add_edge("structured_output", END)
        builder.add_edge("tools", "assistant")
        react_graph = builder.compile()
        return react_graph

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")
        file_name = item.get("file_name")
        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=question_text,
                task_id=task_id,
                file_name=file_name)
            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()
    
    os.environ["AZURE_TENANT_ID"] = gr.Textbox(label="AZURE_TENANT_ID")
    os.environ["AZURE_CLIENT_ID"] = gr.Textbox(label="AZURE_CLIENT_ID")
    os.environ["AZURE_CLIENT_SECRET"] = gr.Textbox(label="AZURE_CLIENT_SECRET")
    os.environ["TAVILY_API_KEY"] = gr.Textbox(label="TAVILY_API_KEY")
    os.environ["TAVILY_API_URL"] = gr.Textbox(label="TAVILY_API_URL")
    os.environ["GOOGLE_API_KEY"] = gr.Textbox(label="GOOGLE_API_KEY")
    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)