Update app.py

app.py

@@ -1,355 +1,135 @@
-import spaces
-import logging
-from datetime import datetime
-from pathlib import Path
-import gradio as gr
-import torch
-import torchaudio
-import os
 import requests
-from transformers import pipeline
-import tempfile
-import numpy as np
-from einops import rearrange
-import cv2
-from scipy.io import wavfile
-import librosa
-import json
-from typing import Optional, Tuple, List
-import atexit
-
-try:
-    import mmaudio
-except ImportError:
-    os.system("pip install -e .")
-    import mmaudio
-
-from mmaudio.eval_utils import (ModelConfig, all_model_cfg, generate, load_video, make_video,
-                                setup_eval_logging)
-from mmaudio.model.flow_matching import FlowMatching
-from mmaudio.model.networks import MMAudio, get_my_mmaudio
-from mmaudio.model.sequence_config import SequenceConfig
-from mmaudio.model.utils.features_utils import FeaturesUtils
-
-# 로깅 설정
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-log = logging.getLogger()
-
-# CUDA 설정
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cudnn.allow_tf32 = True
-    torch.backends.cudnn.benchmark = True
-else:
-    device = torch.device("cpu")
-
-dtype = torch.bfloat16
-
-# 모델 설정
-model: ModelConfig = all_model_cfg['large_44k_v2']
-model.download_if_needed()
-output_dir = Path('./output/gradio')
-
-setup_eval_logging()
-
-# 번역기 및 Pixabay API 설정
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu")
-PIXABAY_API_KEY = "33492762-a28a596ec4f286f84cd328b17"
-
-
-
-def cleanup_temp_files():
-    temp_dir = tempfile.gettempdir()
-    for file in os.listdir(temp_dir):
-        if file.endswith(('.mp4', '.flac')):
-            try:
-                os.remove(os.path.join(temp_dir, file))
-            except:
-                pass
-
-atexit.register(cleanup_temp_files)
-
-def get_model() -> tuple[MMAudio, FeaturesUtils, SequenceConfig]:
-    with torch.cuda.device(device):
-        seq_cfg = model.seq_cfg
-        net: MMAudio = get_my_mmaudio(model.model_name).to(device, dtype).eval()
-        net.load_weights(torch.load(model.model_path, map_location=device, weights_only=True))
-        log.info(f'Loaded weights from {model.model_path}')
-
-        feature_utils = FeaturesUtils(
-            tod_vae_ckpt=model.vae_path,
-            synchformer_ckpt=model.synchformer_ckpt,
-            enable_conditions=True,
-            mode=model.mode,
-            bigvgan_vocoder_ckpt=model.bigvgan_16k_path,
-            need_vae_encoder=False
-        ).to(device, dtype).eval()
-
-        return net, feature_utils, seq_cfg
+from bs4 import BeautifulSoup
+from urllib.parse import urlparse, urljoin
+import gradio as gr
 
-net, feature_utils, seq_cfg = get_model()
+def seo_check(url):
+    report = []
+    suggestions = []
 
+    # Ensure HTTPS
+    if not url.startswith("http"):
+        url = "https://" + url
 
-# search_videos 함수 수정
-@torch.no_grad()
-def search_videos(query):
     try:
-        # CPU에서 번역 실행
-        query = translate_prompt(query)
-        return search_pixabay_videos(query, PIXABAY_API_KEY)
+        response = requests.get(url, timeout=10)
+        response.raise_for_status()
+        html = response.text
     except Exception as e:
-        logging.error(f"Video search error: {e}")
-        return []
-
-# translate_prompt 함수도 수정
-def translate_prompt(text):
+        return f"❌ Error accessing URL: {e}", ""
+
+    soup = BeautifulSoup(html, "html.parser")
+
+    # Title Tag
+    title = soup.title.string.strip() if soup.title else ""
+    if not title:
+        report.append("❌ Missing <title> tag.")
+        suggestions.append("Add a <title> tag that describes your page in 50–60 characters.")
+    elif len(title) > 70:
+        report.append("⚠️ Title is too long.")
+        suggestions.append("Keep title under 70 characters.")
+
+    # Meta Description
+    desc_tag = soup.find("meta", attrs={"name": "description"})
+    desc = desc_tag["content"].strip() if desc_tag and desc_tag.get("content") else ""
+    if not desc:
+        report.append("❌ Missing meta description.")
+        suggestions.append("Add a <meta name='description'> summarizing the page.")
+    elif len(desc) > 160:
+        report.append("⚠️ Meta description is too long.")
+        suggestions.append("Keep meta descriptions under 160 characters.")
+
+    # Canonical Tag
+    canonical = soup.find("link", rel="canonical")
+    if not canonical:
+        report.append("❌ Missing canonical link.")
+        suggestions.append("Add a <link rel='canonical'> to avoid duplicate content.")
+
+    # H1 Tag
+    h1_tags = soup.find_all("h1")
+    if len(h1_tags) != 1:
+        report.append(f"⚠️ Found {len(h1_tags)} <h1> tags.")
+        suggestions.append("Use exactly one <h1> tag for SEO clarity.")
+
+    # Mobile viewport
+    viewport = soup.find("meta", attrs={"name": "viewport"})
+    if not viewport:
+        report.append("⚠️ No viewport meta tag.")
+        suggestions.append("Add a viewport meta tag for mobile responsiveness.")
+
+    # HTTPS check
+    if not url.startswith("https://"):
+        report.append("⚠️ URL is not secure (no HTTPS).")
+        suggestions.append("Install SSL and redirect HTTP to HTTPS.")
+
+    # Robots.txt and sitemap.xml
+    parsed = urlparse(url)
+    base = f"{parsed.scheme}://{parsed.netloc}"
+    robots_url = urljoin(base, "/robots.txt")
+    sitemap_url = urljoin(base, "/sitemap.xml")
     try:
-        if text and any(ord(char) >= 0x3131 and ord(char) <= 0xD7A3 for char in text):
-            # CPU에서 번역 실행
-            with torch.no_grad():
-                translation = translator(text)[0]['translation_text']
-            return translation
-        return text
-    except Exception as e:
-        logging.error(f"Translation error: {e}")
-        return text
-
-# 디바이스 설정 부분 수정
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.backends.cudnn.allow_tf32 = True
-    torch.backends.cudnn.benchmark = True
-else:
-    device = torch.device("cpu")
-
-# 번역기 설정 수정
-translator = pipeline("translation", 
-                     model="Helsinki-NLP/opus-mt-ko-en", 
-                     device="cpu")  # 명시적으로 CPU 지정
-
+        r1 = requests.get(robots_url)
+        if r1.status_code != 200:
+            report.append("❌ robots.txt not found.")
+            suggestions.append("Create a robots.txt to guide search bots.")
+    except:
+        report.append("❌ Could not access robots.txt.")
 
-
-def search_pixabay_videos(query, api_key):
     try:
-        base_url = "https://pixabay.com/api/videos/"
-        params = {
-            "key": api_key,
-            "q": query,
-            "per_page": 40
-        }
-        
-        response = requests.get(base_url, params=params)
-        if response.status_code == 200:
-            data = response.json()
-            return [video['videos']['large']['url'] for video in data.get('hits', [])]
-        return []
-    except Exception as e:
-        logging.error(f"Pixabay API error: {e}")
-        return []
-
-
-@spaces.GPU
-@torch.inference_mode()
-def video_to_audio(video: gr.Video, prompt: str, negative_prompt: str, seed: int, num_steps: int,
-                   cfg_strength: float, duration: float):
-    prompt = translate_prompt(prompt)
-    negative_prompt = translate_prompt(negative_prompt)
-
-    rng = torch.Generator(device=device)
-    rng.manual_seed(seed)
-    fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps)
-
-    clip_frames, sync_frames, duration = load_video(video, duration)
-    clip_frames = clip_frames.unsqueeze(0)
-    sync_frames = sync_frames.unsqueeze(0)
-    seq_cfg.duration = duration
-    net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len)
-
-    audios = generate(clip_frames,
-                      sync_frames, [prompt],
-                      negative_text=[negative_prompt],
-                      feature_utils=feature_utils,
-                      net=net,
-                      fm=fm,
-                      rng=rng,
-                      cfg_strength=cfg_strength)
-    audio = audios.float().cpu()[0]
-
-    video_save_path = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4').name
-    make_video(video,
-               video_save_path,
-               audio,
-               sampling_rate=seq_cfg.sampling_rate,
-               duration_sec=seq_cfg.duration)
-    return video_save_path
-
-@spaces.GPU
-@torch.inference_mode()
-def text_to_audio(prompt: str, negative_prompt: str, seed: int, num_steps: int, cfg_strength: float,
-                  duration: float):
-    prompt = translate_prompt(prompt)
-    negative_prompt = translate_prompt(negative_prompt)
-
-    rng = torch.Generator(device=device)
-    rng.manual_seed(seed)
-    fm = FlowMatching(min_sigma=0, inference_mode='euler', num_steps=num_steps)
-
-    clip_frames = sync_frames = None
-    seq_cfg.duration = duration
-    net.update_seq_lengths(seq_cfg.latent_seq_len, seq_cfg.clip_seq_len, seq_cfg.sync_seq_len)
-
-    audios = generate(clip_frames,
-                      sync_frames, [prompt],
-                      negative_text=[negative_prompt],
-                      feature_utils=feature_utils,
-                      net=net,
-                      fm=fm,
-                      rng=rng,
-                      cfg_strength=cfg_strength)
-    audio = audios.float().cpu()[0]
-
-    audio_save_path = tempfile.NamedTemporaryFile(delete=False, suffix='.flac').name
-    torchaudio.save(audio_save_path, audio, seq_cfg.sampling_rate)
-    return audio_save_path
-
-
-
-# CSS 스타일 수정
-custom_css = """
-.gradio-container {
-    background: linear-gradient(45deg, #1a1a1a, #2a2a2a);
-    border-radius: 15px;
-    box-shadow: 0 8px 32px rgba(0,0,0,0.3);
-    color: #e0e0e0;
-}
-
-.input-container, .output-container {
-    background: rgba(40, 40, 40, 0.95);
-    backdrop-filter: blur(10px);
-    border-radius: 10px;
-    padding: 20px;
-    transform-style: preserve-3d;
-    transition: transform 0.3s ease;
-    border: 1px solid rgba(255, 255, 255, 0.1);
-}
-
-.input-container:hover {
-    transform: translateZ(20px);
-    box-shadow: 0 8px 32px rgba(0,0,0,0.5);
-}
-
-.gallery-item {
-    transition: transform 0.3s ease;
-    border-radius: 8px;
-    overflow: hidden;
-    background: #2a2a2a;
-}
-
-.gallery-item:hover {
-    transform: scale(1.05);
-    box-shadow: 0 4px 15px rgba(0,0,0,0.4);
-}
-
-.tabs {
-    background: rgba(30, 30, 30, 0.95);
-    border-radius: 10px;
-    padding: 10px;
-    border: 1px solid rgba(255, 255, 255, 0.05);
-}
-
-button {
-    background: linear-gradient(45deg, #2196F3, #1976D2);
-    border: none;
-    border-radius: 5px;
-    transition: all 0.3s ease;
-    color: white;
-}
-
-button:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 4px 15px rgba(33,150,243,0.3);
-}
-
-/* 텍스트 입력 필드 스타일 */
-textarea, input[type="text"], input[type="number"] {
-    background: rgba(30, 30, 30, 0.95) !important;
-    border: 1px solid rgba(255, 255, 255, 0.1) !important;
-    color: #e0e0e0 !important;
-    border-radius: 5px !important;
-}
-
-/* 레이블 스타일 */
-label {
-    color: #e0e0e0 !important;
-}
-
-/* 갤러리 그리드 스타일 */
-.gallery {
-    background: rgba(30, 30, 30, 0.95);
-    padding: 15px;
-    border-radius: 10px;
-    border: 1px solid rgba(255, 255, 255, 0.05);
-}
-"""
-
-text_to_audio_tab = gr.Interface(
-    fn=text_to_audio,
-    inputs=[
-        gr.Textbox(label="Prompt(한글지원)"),
-        gr.Textbox(label="Negative Prompt"),
-        gr.Number(label="Seed", value=0),
-        gr.Number(label="Steps", value=25),
-        gr.Number(label="Guidance Scale", value=4.5),
-        gr.Number(label="Duration (sec)", value=8),
-    ],
-    outputs=gr.Audio(label="Generated Audio"),
-    css=custom_css
-)
-
-
-video_to_audio_tab = gr.Interface(
-    fn=video_to_audio,
-    inputs=[
-        gr.Video(label="Input Video"),
-        gr.Textbox(label="Prompt(한글지원)"),
-        gr.Textbox(label="Negative Prompt", value="music"),
-        gr.Number(label="Seed", value=0),
-        gr.Number(label="Steps", value=25),
-        gr.Number(label="Guidance Scale", value=4.5),
-        gr.Number(label="Duration (sec)", value=8),
+        r2 = requests.get(sitemap_url)
+        if r2.status_code != 200:
+            report.append("❌ sitemap.xml not found.")
+            suggestions.append("Add sitemap.xml for better crawling.")
+    except:
+        report.append("❌ Could not access sitemap.xml.")
+
+    # Open Graph Tags
+    og_title = soup.find("meta", property="og:title")
+    if not og_title:
+        report.append("⚠️ Missing Open Graph (og:title).")
+        suggestions.append("Add OG tags to improve sharing on social media.")
+
+    # Image alt text
+    images = soup.find_all("img")
+    alt_missing = [img for img in images if not img.get("alt")]
+    if alt_missing:
+        report.append(f"⚠️ {len(alt_missing)} images missing alt text.")
+        suggestions.append("Add descriptive alt attributes to all images.")
+
+    # Internal and external links
+    links = soup.find_all("a", href=True)
+    internal = 0
+    external = 0
+    for link in links:
+        href = link['href']
+        if parsed.netloc in href:
+            internal += 1
+        elif href.startswith("http"):
+            external += 1
+    report.append(f"ℹ️ Internal Links: {internal} | External Links: {external}")
+    suggestions.append("Ensure most important links are internal. Check broken links.")
+
+    # Keyword density (basic)
+    body_text = soup.get_text().lower()
+    words = body_text.split()
+    word_count = len(words)
+    keyword = parsed.netloc.replace("www.", "").split(".")[0]
+    keyword_freq = words.count(keyword)
+    density = (keyword_freq / word_count) * 100 if word_count else 0
+    report.append(f"ℹ️ Keyword '{keyword}' appears {keyword_freq} times ({density:.2f}% density)")
+    if density < 0.5:
+        suggestions.append("Consider using your main keyword more often (target 1–2%).")
+
+    return "\n".join(report), "\n".join(suggestions)
+
+# Gradio UI
+gr.Interface(
+    fn=seo_check,
+    inputs=gr.Textbox(label="Enter Website URL"),
+    outputs=[
+        gr.Textbox(label="SEO Report", lines=15),
+        gr.Textbox(label="Suggestions & Fixes", lines=15)
     ],
-    outputs=gr.Video(label="Generated Result"),
-    css=custom_css
-)
-
-# 인터페이스 정의 수정 (영문으로 변경)
-video_search_tab = gr.Interface(
-    fn=search_videos,
-    inputs=gr.Textbox(label="Search Query(한글지원)"),
-    outputs=gr.Gallery(label="Search Results", columns=4, rows=20),
-    css=custom_css,
-    api_name=False
-)
-
-
-
-# CSS 스타일 수정
-css = """
-footer {
-    visibility: hidden;
-}
-""" + custom_css  # 기존 custom_css와 새로운 css를 결합
-
-# 메인 실행 부분 수정
-if __name__ == "__main__":
-    gr.TabbedInterface(
-        [video_search_tab, video_to_audio_tab, text_to_audio_tab],
-        ["Video Search", "Video-to-Audio", "Text-to-Audio"],
-        theme="Yntec/HaleyCH_Theme_Orange",
-        css=css
-    ).launch(allowed_paths=[output_dir])
+    title="SEO Website Checker",
+    description="Analyze your website's SEO like Sitechecker.pro & SEOSiteCheckup, with clear solutions!"
+).launch()