| import gradio as gr |
| import re |
| import emoji |
| import logging |
| from typing import Tuple, Optional |
| from functools import lru_cache |
| from collections import Counter |
|
|
| logging.basicConfig(level=logging.INFO) |
| logger = logging.getLogger(__name__) |
|
|
| def count_emojis(text: str) -> int: |
| """Подсчет количества эмодзи в тексте""" |
| return len([c for c in text if c in emoji.EMOJI_DATA]) |
|
|
| def extract_mentions(text: str) -> list: |
| """Извлечение упоминаний пользователей""" |
| return re.findall(r'@(\w+)', text) |
|
|
| def is_spam(text: str) -> bool: |
| """Определение спам-комментариев""" |
| spam_indicators = ['🔥' * 3, '❤️' * 3, 'follow me', 'check my'] |
| return any(indicator in text.lower() for indicator in spam_indicators) |
|
|
| def extract_comment_data(comment_text: str) -> Tuple[Optional[str], Optional[str], int, int]: |
| """Извлекает данные из комментария""" |
| try: |
| |
| username_match = re.search(r'Фото профиля\s+(.+?)\n', comment_text) |
| username = username_match.group(1).strip() if username_match else None |
| |
| if not username: |
| return None, None, 0, 0 |
| |
| |
| comment_lines = comment_text.split('\n') |
| comment = "" |
| time_pattern = r'\d+\s*(?:ч\.|нед\.)' |
| |
| |
| for i, line in enumerate(comment_lines): |
| if re.search(time_pattern, line): |
| if i + 1 < len(comment_lines): |
| comment = comment_lines[i + 1].strip() |
| break |
| |
| |
| comment = re.sub(r'\d+\s*(?:ч\.|нед\.)\s*$', '', comment) |
| comment = re.sub(r'"Нравится":\s*\d+\s*Ответить\s*$', '', comment) |
| |
| |
| likes_match = re.search(r'"Нравится":\s*(\d+)', comment_text) |
| likes = int(likes_match.group(1)) if likes_match else 0 |
| |
| |
| time_match = re.search(r'(\d+)\s*(?:ч\.|нед\.)', comment_text) |
| time = int(time_match.group(1)) if time_match else 0 |
| |
| return username, comment.strip(), likes, time |
|
|
| except Exception as e: |
| logger.error(f"Error extracting data: {e}") |
| return None, None, 0, 0 |
|
|
| @lru_cache(maxsize=100) |
| def analyze_post(content_type: str, link: str, post_likes: int, |
| post_date: str, description: str, comment_count: int, |
| all_comments: str) -> Tuple[str, str, str, str, str]: |
| """Анализирует пост и комментарии""" |
| try: |
| if not all_comments or 'Фото профиля' not in all_comments: |
| return "Ошибка: неверный формат данных", "", "", "", "0" |
|
|
| blocks = re.split(r'(?=Фото профиля)', all_comments) |
| blocks = [b.strip() for b in blocks if b.strip()] |
| |
| comments_data = [] |
| total_emojis = 0 |
| mentions = [] |
| spam_count = 0 |
| |
| for block in blocks: |
| username, comment, likes, time = extract_comment_data(block) |
| if username and comment: |
| emoji_count = count_emojis(comment) |
| comment_mentions = extract_mentions(comment) |
| is_spam_comment = is_spam(comment) |
| |
| comments_data.append({ |
| 'username': username, |
| 'comment': comment, |
| 'likes': likes, |
| 'time': time, |
| 'emoji_count': emoji_count, |
| 'mentions': comment_mentions, |
| 'is_spam': is_spam_comment |
| }) |
| |
| total_emojis += emoji_count |
| mentions.extend(comment_mentions) |
| if is_spam_comment: |
| spam_count += 1 |
|
|
| |
| total_comments = len(comments_data) |
| unique_users = len(set(item['username'] for item in comments_data)) |
| total_likes = sum(item['likes'] for item in comments_data) |
| avg_likes = total_likes / total_comments if total_comments > 0 else 0 |
|
|
| |
| commenter_counts = Counter(item['username'] for item in comments_data) |
| top_commenters = commenter_counts.most_common(5) |
|
|
| analytics = f""" |
| 📊 Подробный анализ комментариев: |
| Основные метрики: |
| • Всего комментариев: {total_comments} |
| • Уникальных пользователей: {unique_users} |
| • Общее количество лайков: {total_likes} |
| • Среднее количество лайков: {avg_likes:.1f} |
| Дополнительная информация: |
| • Использовано эмодзи: {total_emojis} |
| • Количество упоминаний: {len(mentions)} |
| • Выявлено спам-комментариев: {spam_count} |
| Топ комментаторы: |
| {chr(10).join(f'• {user}: {count} комментария' for user, count in top_commenters if count > 1)} |
| """ |
|
|
| return ( |
| analytics, |
| "\n".join(item['username'] for item in comments_data), |
| "\n".join(item['comment'] for item in comments_data), |
| "\n".join(str(item['likes']) for item in comments_data), |
| str(total_likes) |
| ) |
|
|
| except Exception as e: |
| logger.error(f"Analysis error: {e}") |
| return str(e), "", "", "", "0" |
|
|
| |
| iface = gr.Interface( |
| fn=analyze_post, |
| inputs=[ |
| gr.Radio( |
| choices=["Photo", "Video"], |
| label="Content Type", |
| value="Photo" |
| ), |
| gr.Textbox( |
| label="Link to Post", |
| placeholder="Вставьте ссылку на пост" |
| ), |
| gr.Number( |
| label="Likes", |
| value=0, |
| minimum=0 |
| ), |
| gr.Textbox( |
| label="Post Date", |
| placeholder="YYYY-MM-DD" |
| ), |
| gr.Textbox( |
| label="Description", |
| lines=3, |
| placeholder="Описание поста" |
| ), |
| gr.Number( |
| label="Comment Count", |
| value=0, |
| minimum=0 |
| ), |
| gr.Textbox( |
| label="Comments", |
| lines=10, |
| placeholder="Вставьте комментарии" |
| ) |
| ], |
| outputs=[ |
| gr.Textbox(label="Analytics Summary", lines=15), |
| gr.Textbox(label="Usernames"), |
| gr.Textbox(label="Comments"), |
| gr.Textbox(label="Likes Chronology"), |
| gr.Textbox(label="Total Likes on Comments") |
| ], |
| title="Enhanced Instagram Comment Analyzer", |
| description="Анализатор комментариев Instagram с расширенной аналитикой", |
| theme="default" |
| ) |
|
|
| if __name__ == "__main__": |
| try: |
| iface.launch( |
| share=True, |
| debug=True, |
| show_error=True |
| ) |
| except Exception as e: |
| logger.error(f"Error launching interface: {e}", exc_info=True) |
| import re |
| import emoji |
| import gradio as gr |
| from collections import defaultdict, Counter |
|
|
| def extract_comment_data(comment_text: str) -> dict: |
| """Extracts data from a comment string.""" |
| comment_data = {} |
| |
| |
| match = re.search(r"Фото профиля\s*(.+?)\n", comment_text) |
| comment_data["username"] = match.group(1).strip() if match else None |
| if not comment_data["username"]: |
| return None |
|
|
| |
| lines = comment_text.splitlines() |
| comment_text = "" |
| for i, line in enumerate(lines): |
| if re.search(r"\d+\s*(?:нед\.|ч\.)", line): |
| comment_text = "\n".join(lines[i+1:]).strip() |
| break |
| comment_text += line + "\n" |
| comment_text = comment_text.strip() |
| comment_data["comment"] = comment_text |
|
|
| |
| match = re.search(r'"Нравится":\s*(\d+)', comment_text) |
| comment_data["likes"] = int(match.group(1)) if match else 0 |
|
|
| |
| time_match = re.search(r"(\d+)\s*(?:нед\.|ч\.)", comment_text) |
| comment_data["time"] = int(time_match.group(1)) if time_match else None |
|
|
|
|
| return comment_data |
|
|
| def analyze_comments(comments_text: str) -> dict: |
| """Analyzes a block of comments text.""" |
| comments = [] |
| blocks = re.split(r'(Фото профиля)', comments_text, flags=re.IGNORECASE) |
| for i in range(1,len(blocks),2): |
| comment_data = extract_comment_data(blocks[i]) |
| if comment_data: |
| comments.append(comment_data) |
|
|
| |
| analytics = defaultdict(int) |
| unique_users = set() |
| top_commenters = Counter() |
|
|
| for comment in comments: |
| analytics["total_comments"] += 1 |
| unique_users.add(comment["username"]) |
| analytics["total_likes"] += comment["likes"] |
| top_commenters[comment["username"]] += 1 |
| analytics["emojis"] += len(emoji.demojize(comment["comment"])) |
|
|
| analytics["unique_users"] = len(unique_users) |
| analytics["avg_likes"] = analytics["total_likes"] / analytics["total_comments"] if analytics["total_comments"] > 0 else 0 |
| analytics["top_commenters"] = dict(top_commenters.most_common(5)) |
|
|
|
|
| return analytics, comments |
|
|
|
|
| iface = gr.Interface( |
| fn=analyze_comments, |
| inputs=gr.Textbox(label="Instagram Comments (Paste here)", lines=10), |
| outputs=[ |
| gr.Textbox(label="Analytics Summary"), |
| gr.JSON(label="Individual Comment Data") |
| ], |
| title="Enhanced Instagram Comment Analyzer", |
| description="Improved analyzer for Instagram comments.", |
| ) |
|
|
| iface.launch(share=True) |
