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import os |
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import gradio as gr |
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import requests |
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import json |
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import pandas as pd |
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from openai import OpenAI |
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from bs4 import BeautifulSoup |
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
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class BasicAgent: |
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def __init__(self): |
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print("BasicAgent initialized.") |
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self.client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=os.getenv("OR_TOKEN")) |
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def duckduckgo_search(self, query: str, num_results: int = 3) -> list: |
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""" |
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Perform a search using DuckDuckGo and return the results. |
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Args: |
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query: The search query string |
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num_results: Maximum number of results to return (default: 5) |
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Returns: |
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List of dictionaries containing search results with title, url, and snippet |
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""" |
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print(f"Performing DuckDuckGo search for: {query}") |
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try: |
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headers = { |
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36" |
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} |
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formatted_query = query.replace(' ', '+') |
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url = f"https://html.duckduckgo.com/html/?q={formatted_query}" |
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response = requests.get(url, headers=headers, timeout=10) |
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response.raise_for_status() |
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soup = BeautifulSoup(response.text, 'html.parser') |
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results = [] |
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for result in soup.select('.result'): |
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title_elem = result.select_one('.result__title') |
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link_elem = result.select_one('.result__url') |
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snippet_elem = result.select_one('.result__snippet') |
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if title_elem and link_elem: |
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title = title_elem.get_text(strip=True) |
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url = link_elem.get('href') if link_elem.get('href') else link_elem.get_text(strip=True) |
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snippet = snippet_elem.get_text(strip=True) if snippet_elem else "" |
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results.append({ |
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"title": title, |
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"url": url, |
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"snippet": snippet |
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}) |
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if len(results) >= num_results: |
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break |
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print(f"Found {len(results)} results for query: {query}") |
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return results |
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except Exception as e: |
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print(f"Error during DuckDuckGo search: {e}") |
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return [] |
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def __call__(self, question: str) -> str: |
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print(f"Agent received question: {question}...") |
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try: |
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tools = [ |
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{ |
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"type": "function", |
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"function": { |
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"name": "duckduckgo_search", |
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"description": "Search the web using DuckDuckGo and return relevant results", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"query": { |
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"type": "string", |
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"description": "The search query string" |
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}, |
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"num_results": { |
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"type": "integer", |
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"description": "Maximum number of results to return", |
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"default": 5 |
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} |
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}, |
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"required": ["query"] |
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} |
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} |
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} |
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] |
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tools_mapping = { |
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"duckduckgo_search": self.duckduckgo_search |
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} |
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messages = [ |
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{ |
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"role": "system", |
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"content": "You are a general AI assistant. I will ask you a question. Read the question carefully. Break down the question into multiple questions and use the tools available to you to answer the question. Do not report your thoughts, only give 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.", |
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}, |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "text", |
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"text": question |
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}, |
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] |
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} |
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] |
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for _ in range(3): |
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print("Using Inference API for generation...") |
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completion = self.client.chat.completions.create( |
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extra_headers={ |
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"HTTP-Referer": "<YOUR_SITE_URL>", |
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"X-Title": "<YOUR_SITE_NAME>", |
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}, |
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extra_body={}, |
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model="meta-llama/llama-4-maverick:free", |
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messages=messages |
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) |
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messages.append(completion.choices[0].message) |
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print(f"Message after completion: {completion.choices[0].message}") |
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if completion.choices[0].message.tool_calls is None: |
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answer = completion.choices[0].message.content |
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print(f"Agent generated response: {answer}") |
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return answer |
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for tool_call in completion.choices[0].message.tool_calls: |
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""" |
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In this case we only provided one tool, so we know what function to call. |
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When providing multiple tools, you can inspect `tool_call.function.name` |
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to figure out what function you need to call locally. |
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""" |
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tool_name = tool_call.function.name |
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tool_args = json.loads(tool_call.function.arguments) |
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tool_response = tools_mapping[tool_name](**tool_args) |
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message = { |
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"role": "tool", |
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"tool_call_id": tool_call.id, |
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"name": tool_name, |
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"content": json.dumps(tool_response), |
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} |
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messages.append(message) |
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print(f"Message after tools call: {message}") |
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except Exception as e: |
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print(f"Error generating response: {e}") |
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fallback_answer = "I apologize, but I encountered an error when trying to answer your question." |
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print(f"Agent returning fallback answer: {fallback_answer}") |
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return fallback_answer |
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def run_and_submit_all(profile: gr.OAuthProfile | None): |
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""" |
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Fetches all questions, runs the BasicAgent on them, submits all answers, |
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and displays the results. |
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""" |
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space_id = os.getenv("SPACE_ID") |
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if profile: |
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username = f"{profile.username}" |
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print(f"User logged in: {username}") |
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else: |
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print("User not logged in.") |
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return "Please Login to Hugging Face with the button.", None |
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api_url = DEFAULT_API_URL |
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questions_url = f"{api_url}/questions" |
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submit_url = f"{api_url}/submit" |
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try: |
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agent = BasicAgent() |
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except Exception as e: |
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print(f"Error instantiating agent: {e}") |
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return f"Error initializing agent: {e}", None |
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agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" |
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print(agent_code) |
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print(f"Fetching questions from: {questions_url}") |
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questions_data = [ |
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{ |
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'task_id': '5a0c1adf-205e-4841-a666-7c3ef95def9d', |
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'question': 'What is the first name of the only Malko Competition recipient from the 20th Century (after 1977) whose nationality on record is a country that no longer exists?', |
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'Level': '1', |
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'file_name': '' |
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}, |
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] |
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results_log = [] |
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answers_payload = [] |
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print(f"Running agent on {len(questions_data)} questions...") |
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for item in questions_data: |
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task_id = item.get("task_id") |
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question_text = item.get("question") |
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if not task_id or question_text is None: |
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print(f"Skipping item with missing task_id or question: {item}") |
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continue |
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try: |
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submitted_answer = agent(question_text) |
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answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) |
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}) |
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except Exception as e: |
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print(f"Error running agent on task {task_id}: {e}") |
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"}) |
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if not answers_payload: |
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print("Agent did not produce any answers to submit.") |
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return "Agent did not produce any answers to submit.", pd.DataFrame(results_log) |
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submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload} |
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status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..." |
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print(status_update) |
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print(f"Submitting {len(answers_payload)} answers to: {submit_url}") |
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try: |
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response = requests.post(submit_url, json=submission_data, timeout=60) |
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response.raise_for_status() |
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result_data = response.json() |
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final_status = ( |
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f"Submission Successful!\n" |
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f"User: {result_data.get('username')}\n" |
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f"Overall Score: {result_data.get('score', 'N/A')}% " |
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f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n" |
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f"Message: {result_data.get('message', 'No message received.')}" |
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) |
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print("Submission successful.") |
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results_df = pd.DataFrame(results_log) |
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return final_status, results_df |
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except requests.exceptions.HTTPError as e: |
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error_detail = f"Server responded with status {e.response.status_code}." |
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try: |
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error_json = e.response.json() |
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error_detail += f" Detail: {error_json.get('detail', e.response.text)}" |
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except requests.exceptions.JSONDecodeError: |
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error_detail += f" Response: {e.response.text[:500]}" |
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status_message = f"Submission Failed: {error_detail}" |
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print(status_message) |
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results_df = pd.DataFrame(results_log) |
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return status_message, results_df |
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except requests.exceptions.Timeout: |
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status_message = "Submission Failed: The request timed out." |
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print(status_message) |
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results_df = pd.DataFrame(results_log) |
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return status_message, results_df |
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except requests.exceptions.RequestException as e: |
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status_message = f"Submission Failed: Network error - {e}" |
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print(status_message) |
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results_df = pd.DataFrame(results_log) |
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return status_message, results_df |
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except Exception as e: |
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status_message = f"An unexpected error occurred during submission: {e}" |
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print(status_message) |
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results_df = pd.DataFrame(results_log) |
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return status_message, results_df |
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with gr.Blocks() as demo: |
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gr.Markdown("# Basic Agent Evaluation Runner") |
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gr.Markdown( |
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""" |
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**Instructions:** |
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1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ... |
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2. Log in to your Hugging Face account using the button below. This uses your HF username for submission. |
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3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score. |
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--- |
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**Disclaimers:** |
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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). |
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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. |
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""" |
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) |
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gr.LoginButton() |
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run_button = gr.Button("Run Evaluation & Submit All Answers") |
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status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False) |
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results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True) |
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run_button.click( |
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fn=run_and_submit_all, |
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outputs=[status_output, results_table] |
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) |
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if __name__ == "__main__": |
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print("\n" + "-" * 30 + " App Starting " + "-" * 30) |
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space_host_startup = os.getenv("SPACE_HOST") |
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space_id_startup = os.getenv("SPACE_ID") |
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if space_host_startup: |
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print(f"✅ SPACE_HOST found: {space_host_startup}") |
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print(f" Runtime URL should be: https://{space_host_startup}.hf.space") |
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else: |
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print("ℹ️ SPACE_HOST environment variable not found (running locally?).") |
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if space_id_startup: |
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print(f"✅ SPACE_ID found: {space_id_startup}") |
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print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}") |
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print(f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main") |
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else: |
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print("ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.") |
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print("-" * (60 + len(" App Starting ")) + "\n") |
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print("Launching Gradio Interface for Basic Agent Evaluation...") |
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demo.launch(debug=True, share=False) |
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