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import os |
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import logging |
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import requests |
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import subprocess |
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import time |
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import re |
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from functools import lru_cache |
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from typing import List, Dict, Optional, Tuple |
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import spacy |
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from openai import OpenAI |
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import gradio as gr |
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from tool import Browser, SearchInformationTool |
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try: |
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nlp = spacy.load("en_core_web_sm") |
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except OSError: |
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subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"]) |
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nlp = spacy.load("en_core_web_sm") |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
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def initialize_openai_client(): |
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try: |
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return OpenAI(api_key=os.getenv("OPENAI_API_KEY")) |
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except Exception as e: |
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logger.error(f"Failed to initialize OpenAI client: {str(e)}") |
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raise |
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class EnAgent: |
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def __init__(self, api_url: str = DEFAULT_API_URL): |
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self.api_url = api_url |
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self.openai_client = initialize_openai_client() |
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self.browser = Browser() |
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self.search_tool = SearchInformationTool(browser=self.browser) |
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logger.info("EnAgent initialized.") |
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def fetch_questions(self) -> Optional[List[Dict]]: |
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try: |
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response = requests.get(f"{self.api_url}/questions", timeout=15) |
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response.raise_for_status() |
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return response.json() |
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except Exception as e: |
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logger.error(f"Error fetching questions: {e}") |
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return None |
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def submit_answers(self, answers_payload: List[Dict], username: str, agent_code: str) -> Optional[Dict]: |
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try: |
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response = requests.post( |
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f"{self.api_url}/submit", |
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json={"username": username, "agent_code": agent_code, "answers": answers_payload}, |
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timeout=60 |
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) |
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response.raise_for_status() |
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return response.json() |
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except Exception as e: |
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logger.error(f"Error submitting answers: {e}") |
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return None |
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def answer_question_with_context(self, context: str) -> str: |
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full_prompt = f"""{context} |
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When answering, provide only the exact answer requested. |
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Do not include explanations, steps, justifications, or additional text. |
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""" |
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try: |
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answer = self.agent.run(full_prompt) |
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answer = self._clean_answer(answer) |
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if self.verbose: |
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print(f"Generated answer: {answer}") |
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return answer |
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except Exception as e: |
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error_msg = f"Error answering question: {e}" |
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if self.verbose: |
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print(error_msg) |
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return error_msg |
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def _clean_answer(self, answer: any) -> str: |
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""" |
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Clean up your response by removing common prefixes and formatting. |
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Args: |
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answer: The raw answer from the model |
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Returns: |
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The cleaned answer as a string |
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""" |
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if not isinstance(answer, str): |
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if isinstance(answer, float): |
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if answer.is_integer(): |
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formatted_answer = str(int(answer)) |
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return formatted_answer |
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elif isinstance(answer, int): |
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return str(answer) |
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else: |
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return str(answer) |
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answer = answer.strip() |
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prefixes_to_remove = [ |
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"The answer is ", |
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"Answer: ", |
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"Final answer: ", |
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"The result is ", |
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"To answer this question: ", |
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"Based on the information provided, ", |
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"According to the information: ", |
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] |
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for prefix in prefixes_to_remove: |
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if answer.startswith(prefix): |
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answer = answer[len(prefix):].strip() |
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if (answer.startswith('"') and answer.endswith('"')) or (answer.startswith("'") and answer.endswith("'")): |
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answer = answer[1:-1].strip() |
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return answer |
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def analyze_question_intent(self, question: str) -> str: |
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doc = nlp(question.lower()) |
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for token in doc: |
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if token.text in ["how", "many", "much", "number", "count"]: |
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return "count" |
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elif token.text in ["who", "name", "person"]: |
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return "name" |
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elif token.text in ["when", "date", "year"]: |
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return "date" |
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elif token.text in ["where", "place", "location"]: |
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return "location" |
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elif token.text in ["what", "which"]: |
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return "fact" |
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return "unknown" |
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def extract_number_between_years(self, text: str, start: int, end: int) -> Optional[int]: |
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year_matches = re.findall(r"\b(19|20)\d{2}\b", text) |
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years = [int(y) for y in year_matches if start <= int(y) <= end] |
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return len(set(years)) if years else None |
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def format_answer(self, question: str, answer: str, intent: str) -> str: |
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answer = answer.strip() |
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logger.info(f"Intent: {intent} | Raw answer: {answer}") |
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if intent == "count": |
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year_matches = re.findall(r"\b(19|20)\d{2}\b", question) |
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years = list(map(int, year_matches)) |
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if len(years) >= 2: |
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start, end = sorted(years[:2]) |
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number = self.extract_number_between_years(answer, start, end) |
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if number is not None: |
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logger.info(f"Extracted number from years: {number}") |
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return str(number) |
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album_match = re.search(r"(one|two|three|four|five|\d+)\s+(studio\s+)?albums?", answer.lower()) |
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if album_match: |
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number_word = album_match.group(1) |
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number = self.convert_word_to_number(number_word) if number_word.isalpha() else int(number_word) |
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if number: |
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logger.info(f"Extracted number from album phrase: {number}") |
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return str (number) |
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numbers = re.findall(r"\d+", answer) |
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if numbers: |
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logger.info(f"Extracted fallback number: {numbers[0]}") |
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return numbers[0] |
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return answer |
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elif intent == "name": |
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doc = nlp(answer) |
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persons = [ent.text for ent in doc.ents if ent.label_ in ["PERSON", "ORG"]] |
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return persons[0] if persons else answer |
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elif intent == "date": |
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doc = nlp(answer) |
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for ent in doc.ents: |
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if ent.label_ == "DATE": |
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return ent.text |
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return answer |
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elif intent == "location": |
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doc = nlp(answer) |
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for ent in doc.ents: |
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if ent.label_ == "GPE": |
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return ent.text |
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return answer |
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elif intent == "fact": |
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return answer |
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return answer |
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def find_country_with_min_athletes(text: str) -> Optional[str]: |
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matches = re.findall(r"\b([A-Z][a-z]+(?: [A-Z][a-z]+)?)\s*\((\d+)\)", text) |
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if not matches: |
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return None |
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min_count = min(int(c) for _, c in matches) |
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filtered = [country for country, count in matches if int(count) == min_count] |
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return sorted(filtered)[0] if filtered else None |
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def extract_ioc_code(country_name: str, ioc_text: str) -> Optional[str]: |
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pattern = re.compile(rf"{re.escape(country_name)}\s*\((\w{{3}})\)", re.IGNORECASE) |
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match = pattern.search(ioc_text) |
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return match.group(1).upper() if match else None |
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def preprocess_question(self, question: str) -> str: |
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question = question.strip().lower() |
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question = re.sub(r"[^\w\s]", "", question) |
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question = re.sub(r"\s+", " ", question) |
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return question |
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def search_with_reference(self, query: str) -> str: |
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domains = [] |
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query_lower = query.lower() |
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wikipedia_related_keywords = ["information", "article", "search", "learn", "facts", "data", "country", "athlete"] |
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if any(keyword in query_lower for keyword in wikipedia_related_keywords) or "wikipedia" in query_lower or "wikipedia.org" in query_lower: |
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domains.append("en.wikipedia.org") |
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if "wikipedia" in query_lower or "wikipedia.org" in query_lower: |
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domains.append("en.wikipedia.org") |
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if "baseball reference" in query_lower: |
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domains.append("www.baseball-reference.com") |
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if "imdb" in query_lower: |
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domains.append("www.imdb.com") |
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if domains: |
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domain_filters = " OR ".join([f"site:{domain}" for domain in domains]) |
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query = f"{query} ({domain_filters})" |
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search_result = self.search_tool.forward(query) |
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if not search_result or "An error occurred" in search_result or "No results found" in search_result: |
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logger.warning("Search returned no usable results.") |
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return "" |
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return search_result[:1000] |
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@lru_cache(maxsize=128) |
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def answer_question(self, question: str) -> str: |
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logger.info(f"Answering question with reasoning: {question[:50]}...") |
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try: |
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source_text = self.search_with_reference(question) |
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intent = self.analyze_question_intent(question) |
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system_prompt = ( |
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"You are a concise assistant. You do it step by step.To search for information, you can use Wikipedia and the sources of information specified in the question. You are only answering the question." |
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"When answering, provide only the exact answer requested." |
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"Do not include explanations, steps, justifications, or additional text." |
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"For example, if you are asked: What is the capital of France?, simply answer: Paris." |
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"For example, to answer four chairs, simply answer: 4" |
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) |
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content_block = f"Question: {question}" |
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if source_text: |
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content_block += f"\n\nSource:\n{source_text}" |
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messages = [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": content_block} |
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] |
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response = self.openai_client.chat.completions.create( |
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model="gpt-4o", |
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messages=messages, |
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temperature=0.3, |
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max_tokens=50 |
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) |
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if response.choices: |
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raw = response.choices[0].message.content.strip() |
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match = re.search(r"(?i)answer\s*[:\\-]?\s*(.*)", raw) |
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final = match.group(1).strip() if match else raw |
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return self.format_answer(question, final, intent) |
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return "No answer." |
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except Exception as e: |
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logger.error(f"Error answering question: {e}") |
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return f"Error: {e}" |
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def process_questions(self, questions: List[str]) -> List[Dict]: |
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results = [] |
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for question in questions: |
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time.sleep(1) |
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answer = self.answer_question(question) |
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results.append({"Question": question, "Answer": answer}) |
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return results |
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def process_uploaded_file(self, file_path: str) -> List[str]: |
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try: |
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ext = os.path.splitext(file_path)[1].lower() |
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if ext == ".pdf": |
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return self.extract_questions_from_pdf(file_path) |
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elif ext == ".txt": |
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return self.extract_questions_from_txt(file_path) |
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elif ext == ".md": |
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return self.extract_questions_from_markdown(file_path) |
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elif ext in [".xls", ".xlsx"]: |
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return self.extract_questions_from_excel(file_path) |
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elif ext == ".csv": |
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return self.extract_questions_from_csv(file_path) |
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elif ext in [".mp4", ".avi", ".mov"]: |
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return self.extract_images_from_video(file_path) |
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else: |
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logger.error("Unsupported file format.") |
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return [] |
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except Exception as e: |
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logger.error(f"Error processing file: {e}") |
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return [] |
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def extract_questions_from_pdf(self, file_path: str) -> List[str]: |
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return ["Question from PDF"] |
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def extract_questions_from_txt(self, file_path: str) -> List[str]: |
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return ["Question from TXT"] |
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def extract_questions_from_markdown(self, file_path: str) -> List[str]: |
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return ["Question from Markdown"] |
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def extract_questions_from_excel(self, file_path: str) -> List[str]: |
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try: |
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import pandas as pd |
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df = pd.read_excel(file_path) |
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for col in df.columns: |
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if df[col].dtype == object: |
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return df[col].dropna().astype(str).tolist() |
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return [] |
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except Exception as e: |
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logger.error(f"Error extracting from Excel: {e}") |
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return [] |
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def extract_questions_from_csv(self, file_path: str) -> List[str]: |
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return ["Question from CSV"] |
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def extract_images_from_video(self, file_path: str) -> List[str]: |
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return ["Frame 1", "Frame 2"] |
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def run_and_submit_all(profile: Optional[gr.OAuthProfile]) -> Tuple[str, Optional[List[Dict]]]: |
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try: |
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if profile is None or not hasattr(profile, "username"): |
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return "❌ Please log in to Hugging Face.", None |
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username = profile.username |
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space_id = os.getenv("SPACE_ID") |
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if not space_id: |
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return "❌ SPACE_ID environment variable not set.", None |
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agent = EnAgent() |
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questions = agent.fetch_questions() |
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if not questions: |
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return "❌ Failed to fetch questions.", None |
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results = agent.process_questions([q["question"] for q in questions]) |
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answers_payload = [ |
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{ |
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"task_id": q["id"] if "id" in q else q["task_id"], |
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"final_answer": next((r["Answer"] for r in results if r["Question"] == q["question"]), "") |
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} |
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for q in questions |
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] |
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submission_result = agent.submit_answers(answers_payload, username, space_id) |
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if not submission_result: |
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return "❌ Failed to submit answers.", None |
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return "✅ Answers submitted successfully.", results |
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except Exception as e: |
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logger.error(f"Unexpected error in run_and_submit_all: {e}") |
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return f"❌ Unexpected error: {e}", None |
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