Create app.py

app.py

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+import torch
+from transformers import pipeline, set_seed, AutoTokenizer, AutoModelForSequenceClassification
+from datasets import load_dataset
+import gradio as gr
+import requests
+from bs4 import BeautifulSoup
+from nltk.sentiment import SentimentIntensityAnalyzer
+from flair.models import TextClassifier
+from flair.data import Sentence
+import newspaper3k
+
+# Konfigurasi Model
+set_seed(42)
+nltk.download('vader_lexicon')
+
+# Model Text Generation
+generator = pipeline('text-generation', model='gpt2-xl')  # Model lebih canggih
+
+# Model Sentiment Analysis
+sentiment_analyzer = SentimentIntensityAnalyzer()
+classifier = TextClassifier.load('en-sentiment')
+
+# Model Klasifikasi Topik
+tokenizer_topic = AutoTokenizer.from_pretrained("facebook/bart-large-mnli")
+model_topic = AutoModelForSequenceClassification.from_pretrained("facebook/bart-large-mnli")
+
+# Fungsi Helper
+def get_trending_topics(platform):
+  if platform == "Twitter":
+    # Scraping trending topics dari Twitter
+    url = "https://twitter.com/i/trends"
+    response = requests.get(url)
+    soup = BeautifulSoup(response.content, "html.parser")
+    trends = [trend.text.strip() for trend in soup.find_all("div", class_="css-901oao r-1awozwy r-18jsvk2 r-6koalj r-370sk r-a023e6 r-b88u0q r-rjixqe r-bcqeeo r-1udh08x r-3s2u2q r-qvutc0")]
+    return trends
+  elif platform == "TikTok":
+    # Scraping trending topics dari TikTok (perlu metode khusus)
+    # ...
+    return ["Trending TikTok 1", "Trending TikTok 2"]
+  else:  # Instagram
+    # Scraping trending topics dari Instagram (perlu metode khusus)
+    # ...
+    return ["Trending Instagram 1", "Trending Instagram 2"]
+
+def find_related_trend(topic, trends):
+  # Menggunakan model klasifikasi topik untuk mencari tren yang relevan
+  topic_sentence = Sentence(topic)
+  classifier.predict(topic_sentence)
+  topic_sentiment = topic_sentence.labels[0]
+
+  related_trends = []
+  for trend in trends:
+    trend_sentence = Sentence(trend)
+    classifier.predict(trend_sentence)
+    trend_sentiment = trend_sentence.labels[0]
+    if topic_sentiment.value == trend_sentiment.value:
+      related_trends.append(trend)
+  return related_trends
+
+def make_clickbait_title(title):
+  # Menggunakan pola clickbait dan analisis sentimen
+  title = title.strip()
+  sentiment = sentiment_analyzer.polarity_scores(title)
+
+  if sentiment['compound'] > 0.5:
+    # Positif -> "Rahasia...", "Terungkap...", dll.
+    clickbait_phrases = ["Rahasia ", "Terungkap ", "Hebat! ", "Menakjubkan! "]
+  elif sentiment['compound'] < -0.5:
+    # Negatif -> "Mengerikan...", "Kontroversial...", dll.
+    clickbait_phrases = ["Mengerikan! ", "Kontroversial! ", "Awas! ", "Bahaya! "]
+  else:
+    # Netral -> "Kamu Tidak Akan Percaya...", "Heboh...", dll.
+    clickbait_phrases = ["Kamu Tidak Akan Percaya...", "Heboh! ", "Viral! ", "Trending! "]
+
+  return clickbait_phrases[0] + title
+
+def analyze_content(content):
+  # Analisis sentimen dan topik
+  sentence = Sentence(content)
+  classifier.predict(sentence)
+  sentiment = sentence.labels[0]
+
+  # Klasifikasi topik (perlu pengembangan lebih lanjut)
+  inputs = tokenizer_topic(content, return_tensors="pt")
+  outputs = model_topic(**inputs)
+  topic_probs = torch.softmax(outputs.logits, dim=1)
+  # ... (Interpretasi hasil klasifikasi topik) ...
+  return sentiment, "Topik yang Diprediksi"
+
+def generate_content(topic, format, tone, platform):
+  trending_topics = get_trending_topics(platform)
+  related_trends = find_related_trend(topic, trending_topics)
+
+  prompt = f"Buat konten {format} tentang {topic} dengan gaya {tone} yang "
+  if related_trends:
+    prompt += f"berkaitan dengan tren {', '.join(related_trends)}."
+  else:
+    prompt += f"berpotensi menjadi viral."
+
+  output = generator(prompt, max_length=500, num_return_sequences=1)
+  content = output[0]['generated_text']
+
+  if format == "Judul":
+    content = make_clickbait_title(content)
+
+  sentiment, predicted_topic = analyze_content(content)
+  return content, sentiment.value, predicted_topic
+
+# Antarmuka Gradio
+iface = gr.Interface(
+    fn=generate_content,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Masukkan topik..."),
+        gr.Dropdown(["Teks", "Judul", "Tweet", "Artikel"], label="Format Konten"),
+        gr.Dropdown(["Netral", "Provokatif", "Humor", "Marah", "Sedih"], label="Tone"),
+        gr.Dropdown(["Twitter", "TikTok", "Instagram"], label="Platform")
+    ],
+    outputs=[
+        "text",
+        "text",
+        "text"
+    ],
+    title="Pembuat Konten Viral (At Any Cost)",
+    description="Hasilkan konten yang dirancang untuk menjadi viral (Eksperimental)."
+)
+
+iface.launch(share=True)  # share=True untuk mendapatkan link publik