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| import os | |
| import gradio as gr | |
| import requests | |
| import base64 | |
| import time | |
| import pandas as pd | |
| from dotenv import load_dotenv | |
| from llama_index.readers.web import SimpleWebPageReader | |
| from llama_index.llms.gemini import Gemini | |
| from llama_index.tools.wikipedia import WikipediaToolSpec | |
| from llama_index.readers.youtube_transcript import YoutubeTranscriptReader | |
| from llama_index.core.tools import FunctionTool | |
| from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec | |
| from llama_index.tools.arxiv import ArxivToolSpec | |
| from llama_index.core.agent.workflow import AgentWorkflow | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # --- Basic Agent Definition --- | |
| class BasicAgent: | |
| def __init__(self): | |
| # Initialize LLM | |
| load_dotenv() | |
| self.llm = Gemini( | |
| model_name="models/gemini-2.0-flash", | |
| temperature=0.1, | |
| max_tokens=4096 | |
| ) | |
| # Define tools | |
| def load_video_transcript(video_url: str) -> dict: | |
| """Get the transcript of a YouTube video.""" | |
| try: | |
| loader = YoutubeTranscriptReader() | |
| documents = loader.load_data(ytlinks=[video_url]) | |
| if documents and len(documents) > 0: | |
| return {"video_transcript": documents[0].text} | |
| else: | |
| return {"video_transcript": "No transcript available for this video."} | |
| except Exception as e: | |
| return {"video_transcript": f"Error obtaining transcript: {str(e)}"} | |
| load_video_transcript_tool = FunctionTool.from_defaults( | |
| load_video_transcript, | |
| name="load_video_transcript", | |
| description="Load the transcript of the given video using the link. If some calls fail, we can still use this tool for others." | |
| ) | |
| def web_page_reader(url: str) -> dict: | |
| """Read and extract content from a web page.""" | |
| try: | |
| documents = SimpleWebPageReader(html_to_text=True).load_data([url]) | |
| return {"webpage_content": "\n".join([doc.text for doc in documents])} | |
| except Exception as e: | |
| return {"webpage_content": f"Error reading the web page: {str(e)}"} | |
| web_page_reader_tool = FunctionTool.from_defaults( | |
| web_page_reader, | |
| name="web_page_reader", | |
| description="Visit a web page at the given URL and return its textual content." | |
| ) | |
| def duck_duck_go_search_tool(query: str) -> dict: | |
| """Search the web using DuckDuckGo.""" | |
| try: | |
| raw_results = DuckDuckGoSearchToolSpec().duckduckgo_full_search(query, max_results=5) | |
| texts = [f"Title: {res['title']}\nURL: {res['link']}\nContent: {res['body']}" for res in raw_results] | |
| full_text = "\n\n".join(texts) | |
| return {"web_search_results": full_text} | |
| except Exception as e: | |
| return {"web_search_results": f"Error searching the web: {str(e)}"} | |
| duckduckgo_search_tool = FunctionTool.from_defaults( | |
| duck_duck_go_search_tool, | |
| name="duck_duck_go_search_tool", | |
| description="Search the web and refine the result into a high-quality response. Use this tool when others don't seem appropriate." | |
| ) | |
| def wikipedia_search(page_title: str, query: str) -> dict: | |
| """Search information on Wikipedia.""" | |
| try: | |
| text = WikipediaToolSpec().load_data(page=page_title) | |
| if not text: | |
| text = WikipediaToolSpec().search_data(query) | |
| return {"wiki_search_results": text} | |
| except Exception as e: | |
| return {"wiki_search_results": f"Error searching Wikipedia: {str(e)}"} | |
| wikipedia_search_tool = FunctionTool.from_defaults( | |
| wikipedia_search, | |
| name="wikipedia_search", | |
| description="Search Wikipedia and convert the results into a high-quality response." | |
| ) | |
| # Create a list of all tools | |
| tools = [ | |
| duckduckgo_search_tool, | |
| load_video_transcript_tool, | |
| wikipedia_search_tool, | |
| web_page_reader_tool | |
| ] | |
| # Create system prompt | |
| system_prompt = """ | |
| You're an AI agent designed for question answering. Keep your answers concise or even one word when possible. | |
| You have access to a bunch of tools, utilize them well to reach answers. | |
| """ | |
| # Initialize the agent workflow | |
| self.agent = AgentWorkflow.from_tools_or_functions(tools, llm=self.llm, system_prompt=system_prompt) | |
| print("BasicAgent initialized.") | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| try: | |
| # Process file data if present | |
| if "file_data:" in question: | |
| parts = question.split("file_data:", 1) | |
| question_text = parts[0].strip() | |
| question = f"{question_text}\n[This question includes attached file data]" | |
| # Run the agent | |
| response = self.agent.run(question) | |
| # Extract final answer | |
| final_answer = response.response | |
| print(f"Agent returning answer: {final_answer[:50]}...") | |
| return final_answer | |
| except Exception as e: | |
| error_message = f"Error processing question: {str(e)}" | |
| print(error_message) | |
| return error_message | |
| 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" | |
| files_url = f"{api_url}/files/" | |
| # 1. Instantiate 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 | |
| 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=30) | |
| 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: | |
| time.sleep(20) # Added delay between questions | |
| 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: | |
| encoded = None | |
| if item.get("file_name") != "": | |
| response = requests.get(files_url + task_id) | |
| response.raise_for_status() | |
| data = response.content | |
| encoded = base64.b64encode(data).decode('utf-8') | |
| if encoded is not None: | |
| submitted_answer = agent(question_text + "\nfile_data: " + encoded) | |
| else: | |
| 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. Log in to your Hugging Face account using the button below. This uses your HF username for submission. | |
| 2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score. | |
| --- | |
| **Disclaimer:** | |
| Once you click the "Submit" button, it may take some time for the agent to process all questions. | |
| """ | |
| ) | |
| 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] | |
| ) | |
| 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) |