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| import os | |
| from dotenv import load_dotenv | |
| import gradio as gr | |
| import requests | |
| from typing import List, Dict, Union | |
| import requests | |
| import wikipediaapi | |
| import pandas as pd | |
| import requests | |
| from bs4 import BeautifulSoup | |
| import re | |
| from urllib.parse import quote | |
| load_dotenv() | |
| # (Keep Constants as is) | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # --- Basic Agent Definition --- | |
| class BasicAgent: | |
| def __init__(self): | |
| print("BasicAgent initialized.") | |
| self.session = requests.Session() | |
| self.session.headers.update({ | |
| 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36' | |
| }) | |
| self.cache = {} | |
| def __call__(self, question: str) -> str: | |
| print(f"Agent received question (first 50 chars): {question[:50]}...") | |
| fixed_answer = agent.answer_question({question}) | |
| print(f"Agent returning fixed answer: {fixed_answer}") | |
| return fixed_answer | |
| def analyze_query(self, query): | |
| """Analyze the query using regex patterns""" | |
| return { | |
| 'entities': self._extract_entities(query), | |
| 'intent': self._determine_intent(query.lower()), | |
| 'time_constraints': self._extract_time_constraints(query), | |
| 'quantities': self._extract_quantities(query) | |
| } | |
| def _extract_entities(self, text): | |
| """Simple entity extraction using capitalization patterns""" | |
| # Find proper nouns (capitalized phrases) | |
| entities = re.findall(r'([A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*)', text) | |
| # Filter out small words and standalone letters | |
| return [(ent, 'UNKNOWN') for ent in entities if len(ent) > 2 and ' ' in ent] | |
| def _determine_intent(self, query): | |
| """Determine intent using keyword patterns""" | |
| if 'how many' in query: | |
| return 'count' | |
| elif 'when' in query or 'date' in query: | |
| return 'date' | |
| elif 'who' in query: | |
| return 'person' | |
| elif 'what is' in query or 'define' in query: | |
| return 'definition' | |
| elif 'list' in query or 'name all' in query: | |
| return 'list' | |
| return 'general' | |
| def _extract_time_constraints(self, text): | |
| """Extract year ranges from text""" | |
| constraints = [] | |
| # Match patterns like "between 2000 and 2009" | |
| range_match = re.search(r'between (\d{4}) and (\d{4})', text) | |
| if range_match: | |
| constraints.append(('range', int(range_match.group(1)), int(range_match.group(2)))) | |
| # Match patterns like "in 2005" | |
| year_match = re.search(r'in (\d{4})', text) | |
| if year_match: | |
| constraints.append(('point', int(year_match.group(1)))) | |
| return constraints | |
| def _extract_quantities(self, text): | |
| """Extract numbers from text""" | |
| return [int(match) for match in re.findall(r'\b(\d+)\b', text)] | |
| def search_wikipedia(self, query, num_results=3): | |
| """Search Wikipedia's API""" | |
| url = "https://en.wikipedia.org/w/api.php" | |
| params = { | |
| 'action': 'query', | |
| 'list': 'search', | |
| 'srsearch': query, | |
| 'format': 'json', | |
| 'srlimit': num_results | |
| } | |
| try: | |
| response = self.session.get(url, params=params).json() | |
| return [{ | |
| 'url': f"https://en.wikipedia.org/wiki/{item['title'].replace(' ', '_')}", | |
| 'title': item['title'], | |
| 'snippet': item['snippet'], | |
| 'source': 'wikipedia' | |
| } for item in response['query']['search']] | |
| except Exception as e: | |
| print(f"Wikipedia search error: {e}") | |
| return [] | |
| def fetch_page(self, url): | |
| """Fetch and parse a Wikipedia page""" | |
| if url in self.cache: | |
| return self.cache[url] | |
| try: | |
| response = self.session.get(url, timeout=10) | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| # Clean the page content | |
| for element in soup(['script', 'style', 'nav', 'footer', 'table']): | |
| element.decompose() | |
| page_data = { | |
| 'url': url, | |
| 'title': soup.title.string if soup.title else '', | |
| 'text': ' '.join(soup.stripped_strings), | |
| 'soup': soup | |
| } | |
| self.cache[url] = page_data | |
| return page_data | |
| except Exception as e: | |
| print(f"Error fetching {url}: {e}") | |
| return None | |
| def answer_question(self, question): | |
| """Answer a question using Wikipedia""" | |
| print(f"\nQuestion: {question}") | |
| # Step 1: Analyze the question | |
| analysis = self.analyze_query(question) | |
| print(f"Analysis: {analysis}") | |
| # Step 2: Search Wikipedia | |
| search_results = self.search_wikipedia(question) | |
| if not search_results: | |
| return {"answer": "No Wikipedia results found", "source": None} | |
| # Step 3: Fetch and analyze pages | |
| answers = [] | |
| for result in search_results: | |
| page = self.fetch_page(result['url']) | |
| if page: | |
| answer = self._extract_answer(page, analysis) | |
| if answer: | |
| answers.append({ | |
| 'answer': answer, | |
| 'source': result['url'], | |
| 'confidence': self._calculate_confidence(answer, analysis) | |
| }) | |
| # Step 4: Return the best answer | |
| if not answers: | |
| return {"answer": "No answers found in Wikipedia", "source": None} | |
| answers.sort(key=lambda x: x['confidence'], reverse=True) | |
| best_answer = answers[0] | |
| # Format the output | |
| result = { | |
| "question": question, | |
| "answer": best_answer['answer'], | |
| "source": best_answer['source'], | |
| "confidence": f"{best_answer['confidence']:.0%}" | |
| } | |
| if isinstance(best_answer['answer'], list): | |
| result['answer'] = "\n- " + "\n- ".join(best_answer['answer']) | |
| return result | |
| def _extract_answer(self, page, analysis): | |
| """Extract answer based on intent""" | |
| if analysis['intent'] == 'count': | |
| return self._extract_count(page['text'], analysis) | |
| elif analysis['intent'] == 'date': | |
| return self._extract_date(page['text'], analysis) | |
| elif analysis['intent'] == 'list': | |
| return self._extract_list(page['soup'], analysis) | |
| else: | |
| return self._extract_general(page['text'], analysis) | |
| def _extract_count(self, text, analysis): | |
| """Extract a count/number from text""" | |
| entities = [e[0] for e in analysis['entities']] | |
| pattern = r'(\b\d+\b)[^\.]*\b(' + '|'.join(re.escape(e) for e in entities) + r')\b' | |
| matches = re.finditer(pattern, text, re.IGNORECASE) | |
| counts = [int(match.group(1)) for match in matches] | |
| return max(counts) if counts else None | |
| def _extract_date(self, text, analysis): | |
| """Extract dates from text""" | |
| date_pattern = r'\b(\d{1,2}(?:st|nd|rd|th)?\s+(?:\w+)\s+\d{4}|\d{4})\b' | |
| dates = [match.group(0) for match in re.finditer(date_pattern, text)] | |
| entities = [e[0] for e in analysis['entities']] | |
| return next((d for d in dates if any(e.lower() in text.lower() for e in entities)), None) | |
| def _extract_list(self, soup, analysis): | |
| """Extract list items from page""" | |
| entities = [e[0] for e in analysis['entities']] | |
| items = [] | |
| for list_tag in soup.find_all(['ul', 'ol']): | |
| list_items = [li.get_text().strip() for li in list_tag.find_all('li')] | |
| if any(e.lower() in ' '.join(list_items).lower() for e in entities): | |
| items.extend(list_items) | |
| return items if items else None | |
| def _extract_general(self, text, analysis): | |
| """Extract general information from text""" | |
| entities = [e[0] for e in analysis['entities']] | |
| sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text) | |
| relevant = [s for s in sentences if any(e.lower() in s.lower() for e in entities)] | |
| return ' '.join(relevant) if relevant else None | |
| def _calculate_confidence(self, answer, analysis): | |
| """Calculate confidence score for an answer""" | |
| confidence = 0.5 # Base confidence | |
| if analysis['intent'] == 'count' and isinstance(answer, int): | |
| confidence += 0.3 | |
| elif analysis['intent'] == 'date' and re.match(r'.*\d{4}.*', str(answer)): | |
| confidence += 0.3 | |
| elif analysis['intent'] == 'list' and isinstance(answer, list): | |
| confidence += 0.3 | |
| if analysis['time_constraints'] and str(answer): | |
| for constraint in analysis['time_constraints']: | |
| if constraint[0] == 'range': | |
| years = re.findall(r'\b(19|20)\d{2}\b', str(answer)) | |
| if any(constraint[1] <= int(y) <= constraint[2] for y in years): | |
| confidence += 0.2 | |
| return min(0.99, max(0.1, confidence)) | |
| # Example usage | |
| if __name__ == "__main__": | |
| agent = BasicAgent() | |
| questions = [ | |
| "How many studio albums did Taylor Swift release between 2010 and 2015?", | |
| "When was the first iPhone released?", | |
| "What is the capital of Canada?", | |
| "List the planets in our solar system" | |
| ] | |
| for question in questions: | |
| result = agent.answer_question(question) | |
| print(f"\nAnswer: {result['answer']}") | |
| #print(f"Source: {result['source']}") | |
| #print(f"Confidence: {result['confidence']}") | |
| #print("="*50) | |
| 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 = 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. 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) |