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| import os | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| import asyncio | |
| from langchain_google_genai.chat_models import ChatGoogleGenerativeAI | |
| from typing import IO, Dict | |
| from io import BytesIO | |
| from langchain_core.messages import HumanMessage, SystemMessage | |
| from langgraph.graph import MessagesState | |
| from langgraph.graph import START, StateGraph | |
| from langgraph.prebuilt import tools_condition | |
| from langgraph.prebuilt import ToolNode | |
| import base64 | |
| from google.ai.generativelanguage_v1beta.types import Tool as GenAITool | |
| from google import genai | |
| from google.genai import types | |
| import os | |
| GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") | |
| # (Keep Constants as is) | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| GEMINI_API_KEY = os.getenv("Gemini_API_key") | |
| SERPER_API_KEY = os.getenv("SERPER_API_KEY") | |
| # --- Basic Agent Definition --- | |
| # Agent capabilities required: Search the web, listen to audio recordings, watch YouTube videos (process the footage, not the transcript), work with Excel spreadsheets | |
| # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ | |
| def get_file(task_id: str) -> IO: | |
| ''' | |
| Downloads the file associated with the given task_id, if one exists and is mapped. | |
| If the question mentions an attachment, use this function. | |
| Args: | |
| task_id: Id of the question. | |
| Returns: | |
| The file associated with the question. | |
| ''' | |
| file_request = requests.get(url=f'https://agents-course-unit4-scoring.hf.space/files/{task_id}') | |
| file_request.raise_for_status() | |
| return BytesIO(file_request.content) | |
| def analyse_excel(task_id: str) -> Dict[str, float]: | |
| ''' | |
| Analyzes the Excel file associated with the given task_id and returns the sum of each numeric column. | |
| Args: | |
| task_id: Id of the question. | |
| Returns: | |
| A dictionary with the sum of each numeric column. | |
| ''' | |
| excel_file = get_file(task_id) | |
| df = pd.read_excel(excel_file, sheet_name=0) | |
| return df.select_dtypes(include='number').sum().to_dict() | |
| def add_numbers(a: float, b: float) -> float: | |
| ''' | |
| Adds two numbers together. | |
| Args: | |
| a: First number. | |
| b: Second number. | |
| Returns: | |
| The sum of the two numbers. | |
| ''' | |
| return a + b | |
| def transcribe_audio(task_id: str) -> HumanMessage: | |
| ''' | |
| Opens an audio file and returns its content as a string. | |
| Args: | |
| file: The audio file to be opened. | |
| Returns: | |
| The content of the audio file as a string. | |
| ''' | |
| audio_file = get_file(task_id) | |
| if audio_file is None: | |
| raise ValueError("No audio file found for the given task_id.") | |
| # Encode the audio file to base64 | |
| audio_file.seek(0) # Ensure the file pointer is at the beginning | |
| encoded_audio = base64.b64encode(audio_file.read()).decode("utf-8") | |
| return HumanMessage( | |
| content=[ | |
| {"type": "text", "text": "Transcribe the audio."}, | |
| { | |
| "type": "media", | |
| "data": encoded_audio, # Use base64 string directly | |
| "mime_type": "audio/mpeg", | |
| }, | |
| ] | |
| ) | |
| def python_code(task_id: str) -> str: | |
| ''' | |
| Returns the Python code associated with the given task_id. | |
| Args: | |
| task_id: Id of the question. | |
| Returns: | |
| The Python code associated with the question. | |
| ''' | |
| code_request = requests.get(url=f'https://agents-course-unit4-scoring.hf.space/files/{task_id}') | |
| code_request.raise_for_status() | |
| return code_request.text | |
| def open_image(task_id: str) -> str: | |
| ''' | |
| Opens an image file associated with the given task_id. | |
| Args: | |
| task_id: Id of the question. | |
| Returns: | |
| The base64 encoded string of the image file. | |
| ''' | |
| image_file = get_file(task_id) | |
| if image_file is None: | |
| raise ValueError("No image file found for the given task_id.") | |
| return base64.b64encode(image_file.read()).decode("utf-8") | |
| def open_youtube_video(url: str, query:str) -> str: | |
| ''' | |
| Answers a question about a video from the given URL. | |
| Args: | |
| url: The URL of the video file. | |
| query: The question to be answered about the video. | |
| Returns: | |
| Answer to the question about the video. | |
| ''' | |
| client = genai.Client(api_key=GOOGLE_API_KEY) | |
| response = client.models.generate_content( | |
| model='models/gemini-2.0-flash', | |
| contents=types.Content( | |
| parts=[ | |
| types.Part( | |
| file_data=types.FileData(file_uri=url) | |
| ), | |
| types.Part(text=f'''{query} YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated | |
| list of numbers and/or strings.''') | |
| ] | |
| ) | |
| ) | |
| return response.text | |
| def google_search(query: str) -> str: | |
| ''' | |
| Performs a Google search for the given query. | |
| Args: | |
| query: The search query. | |
| Returns: | |
| The search results as a string. | |
| ''' | |
| llm = ChatGoogleGenerativeAI( | |
| model="gemini-2.5-flash-preview-05-20", | |
| max_tokens=8192, | |
| temperature=0 | |
| ) | |
| response = llm.invoke(query, | |
| tools=[GenAITool(google_search={})] | |
| ) | |
| return response.content | |
| class BasicAgent: | |
| def __init__(self): | |
| self.llm = ChatGoogleGenerativeAI( | |
| model="gemini-2.5-flash-preview-05-20", | |
| max_tokens=8192, | |
| temperature=0 | |
| ) | |
| self.tools = [get_file, analyse_excel, add_numbers, transcribe_audio, python_code, open_image, open_youtube_video | |
| , google_search | |
| ] | |
| self.agent = self.llm.bind_tools(self.tools) | |
| self.sys_msg = SystemMessage('''You are a general AI assistant. I will ask you a question. Only provide YOUR FINAL ANSWER and nothing else. | |
| 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 have access to multiple tools and should use as many as you need to answer the question. | |
| If you are asked to analyze an Excel file, use the 'analyse_excel' tool. | |
| If you are asked to download a file, use the 'get_file' tool. | |
| If you are asked to add two numbers, use the 'add_numbers' tool. If you need to add more than two numbers, use the 'add_numbers' | |
| tool multiple times. | |
| If you are asked to transcribe an audio file, use the 'transcribe_audio' tool. | |
| If you are asked to run a Python code, use the 'python_code' tool. | |
| If you are asked to open an image, use the 'open_image' tool. | |
| If you were given a link with www.youtube.com, use the 'open_youtube_video' tool. | |
| If the question requires a web search because your internal knowledge doesn't have the information, use the 'google_search' tool. | |
| ''') | |
| # Graph | |
| self.builder = StateGraph(MessagesState) | |
| # Define nodes: these do the work | |
| self.builder.add_node("assistant", self.assistant) | |
| self.builder.add_node("tools", ToolNode(self.tools)) | |
| # Define edges: these determine how the control flow moves | |
| self.builder.add_edge(START, "assistant") | |
| self.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 | |
| tools_condition, | |
| ) | |
| self.builder.add_edge("tools", "assistant") | |
| self.react_graph = self.builder.compile() | |
| print("BasicAgent initialized.") | |
| def assistant(self, state: MessagesState): | |
| return {"messages": [self.agent.invoke([self.sys_msg] + state["messages"])]} | |
| async def __call__(self, question: str, task_id: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| fixed_answer = "This is a default answer." | |
| await asyncio.sleep(60) | |
| messages = self.react_graph.invoke({"messages": f'Task id: {task_id}\n {question}'}) | |
| return messages["messages"][-1].content if messages["messages"] else 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" | |
| # 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 = asyncio.run(agent(question_text, task_id)) | |
| 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) |