import os import gradio as gr import pandas as pd from huggingface_hub import InferenceClient import asyncio from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI from llama_index.core.agent.workflow import AgentWorkflow from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.readers.web import SimpleWebPageReader import requests from huggingface_hub import InferenceClient from llama_index.readers.wikipedia import WikipediaReader from llama_index.core.agent.workflow import ( AgentInput, AgentOutput, ToolCall, ToolCallResult, AgentStream, ) import requests from bs4 import BeautifulSoup from urllib.parse import urljoin # (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): self.llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct") self.vision_llm = HuggingFaceInferenceAPI(model_name="CohereLabs/aya-vision-32b") self.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5") self.search_client = DuckDuckGoSearchToolSpec() self.wiki_reader = WikipediaReader() system_prompt = """ You are a helpful tool that uses the web to find out answers to specific questions in the manner that a human would. Your answers should contain just ONE single word. You have access to the following tools: 1. search_web: This uses DuckDuckGo to search the web. It's useful when you need to find generic info or links to web pages; 2. search_wiki: Use this when you think searching Wikipedia directly is more useful; 3. webpage_reader: Use this to extract content from web pages; 4. describe_images: This tool will return descriptions of all the images on a web page. Use this to describe images and figures; 5. Use multiply_nums, divide_nums, add_nums and subtract_nums for basic math operations. """ self.agent = AgentWorkflow.from_tools_or_functions([self.search_web, self.search_wiki, self.webpage_reader, self.describe_images, self.multiply_nums, self.divide_nums, self.add_nums, self.subtract_nums], llm=self.llm, system_prompt=system_prompt) print("BasicAgent initialized.") async def __call__(self, question: str) -> str: handler = self.agent.run(user_msg=question) # async for event in handler.stream_events(): # if isinstance(event, AgentStream): # print(event.delta, end="", flush=True) # elif isinstance(event, ToolCallResult): # print(event.tool_name) # the tool name # print(event.tool_kwargs) # the tool kwargs # print(event.tool_output) # the tool output response = await handler return str(response) def extract_image_urls(self, page_url): try: # Send HTTP GET request to the page response = requests.get(page_url) response.raise_for_status() # Raise an error for bad status codes # Parse HTML content soup = BeautifulSoup(response.text, 'html.parser') # Find all tags img_tags = soup.find_all('img') # Extract and resolve image URLs img_urls = [] for img in img_tags: src = img.get('src') if src: # Make relative URLs absolute full_url = urljoin(page_url, src) img_urls.append(full_url) return img_urls except requests.RequestException as e: print(f"Request failed: {e}") return [] async def describe_images(self, webpage_url: str) -> str: """Extracts and describes images from an input webpage url based on a query.""" image_urls = self.extract_image_urls(webpage_url) print("image urls: ", image_urls) if len(image_urls) == 0: return "Looks like there are no images on this webpage" docs = [] for image_url in image_urls: messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": image_url } } ] } ] # print(messages) client = InferenceClient( provider="hyperbolic", api_key=os.getenv('INFERENCE_KEY'), ) try: completion = client.chat.completions.create( model="Qwen/Qwen2.5-VL-7B-Instruct", messages=messages, ) # print(completion.choices[0].message.content) docs.append(completion.choices[0].message.content) except: continue return str(docs) async def search_wiki(self, query: str) -> str: """Useful for browsing Wikipedia to look up specific info.""" reader = self.wiki_reader documents = reader.load_data(pages=[query]) index = VectorStoreIndex.from_documents(documents, embed_model=self.embed_model) search_res = index.as_query_engine(llm=self.llm).query(query) return str(search_res) async def search_web(self, query: str) -> str: """Useful for using the web to answer questions. Keep the query very concise in order to get good results.""" client = self.search_client search_res = client.duckduckgo_full_search(query) return str(search_res) async def webpage_reader(self, webpage_url: str) -> str: """Useful for when you want to read and extract information from a specific webpage.""" documents = SimpleWebPageReader(html_to_text=True).load_data( [webpage_url] ) return str(documents) async def multiply_nums(self, a: int, b: int) -> float: """Useful for multiplying two numbers""" return a * b async def divide_nums(self, a: int, b: int) -> float: """Useful for dividing two numbers""" return a / b async def add_nums(self, a: int, b: int) -> int: """Useful for adding two numbers""" return a + b async def subtract_nums(self, a: int, b: int) -> int: """Useful for subtracting two numbers""" return a - b 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") question_text += "One-word answer only." 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) print("Answer: ", submitted_answer) 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__": # agent = BasicAgent() # while True: # query = input("Ask a question here: ") # answ = asyncio.run(agent(query)) # print(answ) 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)