AI Features Guide¶

from xeepy.ai import ContentGenerator

# Initialize with your preferred provider
ai = ContentGenerator(
    provider="openai",
    api_key="sk-your-api-key",  # or use OPENAI_API_KEY env var
    model="gpt-4-turbo"
)

# Generate a reply
reply = await ai.generate_reply(
    tweet_text="Just shipped my first SaaS product! 🚀",
    style="supportive"
)
print(reply)  # "Congrats on the launch! 🎉 What problem does it solve?"

from xeepy.ai import ContentGenerator

ai = ContentGenerator(provider="openai")

# Generate a tweet about a topic
tweet = await ai.generate_tweet(
    topic="Python programming tips",
    style="educational",
    include_hashtags=True,
    max_length=280
)

print(tweet)
# "🐍 Python tip: Use list comprehensions instead of loops for cleaner code.
#
# # Instead of this:
# result = []
# for x in items:
#     result.append(x * 2)
#
# # Do this:
# result = [x * 2 for x in items]
#
# #Python #CodingTips"

ai = ContentGenerator(provider="anthropic", model="claude-3-opus")

thread = await ai.generate_thread(
    topic="How I grew from 0 to 10k followers",
    key_points=[
        "Consistency is key",
        "Engage with your community",
        "Provide value, not self-promotion",
        "Use threads effectively"
    ],
    style="storytelling",
    thread_length=8  # Number of tweets
)

for i, tweet in enumerate(thread.tweets, 1):
    print(f"Tweet {i}/{len(thread.tweets)}:")
    print(tweet)
    print()

# Available styles
styles = [
    "professional",    # Business-appropriate
    "casual",          # Friendly, conversational
    "witty",           # Clever, humorous
    "supportive",      # Encouraging, positive
    "educational",     # Teaching, informative
    "controversial",   # Thought-provoking (use carefully)
    "storytelling",    # Narrative format
    "crypto",          # Web3/crypto community style
    "tech",            # Developer community style
]

# Use different styles
tweet_pro = await ai.generate_tweet(topic, style="professional")
tweet_casual = await ai.generate_tweet(topic, style="casual")
tweet_witty = await ai.generate_tweet(topic, style="witty")

from xeepy import Xeepy
from xeepy.ai import ContentGenerator

async with Xeepy() as x:
    ai = ContentGenerator(provider="openai")

    # Get a tweet to reply to
    tweet_url = "https://x.com/user/status/123456789"
    tweet = await x.scrape.tweet(tweet_url)

    # Generate contextual reply
    reply = await ai.generate_reply(
        tweet_text=tweet.text,
        author_bio=tweet.author.bio,
        style="supportive",
        max_length=280
    )

    print(f"Suggested reply: {reply}")

# Get multiple reply options
replies = await ai.generate_reply(
    tweet_text="What's the best programming language to learn in 2024?",
    style="educational",
    variations=3  # Generate 3 different replies
)

for i, reply in enumerate(replies, 1):
    print(f"Option {i}: {reply}")

async with Xeepy() as x:
    ai = ContentGenerator(provider="openai")

    # Get mentions
    mentions = await x.scrape.mentions("yourusername", limit=10)

    for mention in mentions:
        # Skip if already replied
        if mention.has_my_reply:
            continue

        # Generate reply
        reply = await ai.generate_reply(
            tweet_text=mention.text,
            style="supportive"
        )

        # Review before posting (recommended!)
        print(f"Tweet: {mention.text}")
        print(f"Suggested: {reply}")

        if input("Post? (y/n): ").lower() == 'y':
            await x.engage.reply(mention.url, reply)

from xeepy.ai import SentimentAnalyzer

analyzer = SentimentAnalyzer(provider="openai")

# Single tweet
result = await analyzer.analyze("This product is amazing! Best purchase ever 🙌")
print(f"Sentiment: {result.label}")  # "positive"
print(f"Score: {result.score}")      # 0.95
print(f"Confidence: {result.confidence}")  # 0.98

# Batch analysis
tweets = ["Great work!", "This is terrible", "It's okay I guess"]
results = await analyzer.analyze_batch(tweets)

for tweet, result in zip(tweets, results):
    print(f"{tweet}: {result.label} ({result.score:.2f})")

async with Xeepy() as x:
    analyzer = SentimentAnalyzer(provider="openai")

    # Get replies to your tweet
    replies = await x.scrape.replies(my_tweet_url, limit=100)

    # Analyze sentiment
    sentiments = await analyzer.analyze_batch([r.text for r in replies])

    positive = sum(1 for s in sentiments if s.label == "positive")
    negative = sum(1 for s in sentiments if s.label == "negative")
    neutral = sum(1 for s in sentiments if s.label == "neutral")

    print(f"""
    Sentiment Breakdown:
    ✅ Positive: {positive} ({positive/len(sentiments):.0%})
    ❌ Negative: {negative} ({negative/len(sentiments):.0%})
    😐 Neutral: {neutral} ({neutral/len(sentiments):.0%})
    """)

from xeepy.ai import BotDetector

detector = BotDetector(provider="openai")

# Check single account
result = await detector.analyze("suspicious_account")

print(f"Bot probability: {result.bot_probability:.0%}")
print(f"Signals: {', '.join(result.signals)}")
# Signals: "High posting frequency", "Generic bio", "Recent account"

# Classification
if result.bot_probability > 0.7:
    print("⚠️ Likely bot")
elif result.bot_probability > 0.4:
    print("🤔 Possibly bot")
else:
    print("✅ Likely human")

async with Xeepy() as x:
    detector = BotDetector(provider="openai")

    # Get followers
    followers = await x.scrape.followers("yourusername", limit=500)

    # Analyze for bots
    real_followers = []
    bot_followers = []

    for follower in followers:
        result = await detector.analyze_profile(follower)

        if result.bot_probability > 0.7:
            bot_followers.append(follower)
        else:
            real_followers.append(follower)

    print(f"Real followers: {len(real_followers)}")
    print(f"Likely bots: {len(bot_followers)}")

from xeepy.ai import SmartTargeting

targeting = SmartTargeting(provider="openai")

# Define your ideal audience
ideal_audience = await targeting.find_accounts(
    criteria={
        "interests": ["startups", "SaaS", "indie hacking"],
        "follower_range": (1000, 50000),
        "engagement_rate_min": 0.02,
        "active_days": 7,
        "language": "en"
    },
    sample_from="followers_of:competitor_account",
    limit=100
)

for account in ideal_audience:
    print(f"@{account.username}")
    print(f"  Relevance: {account.relevance_score:.0%}")
    print(f"  Why: {account.relevance_reason}")

targeting = SmartTargeting(provider="anthropic")

# Get content recommendations based on your audience
recommendations = await targeting.content_recommendations(
    based_on="my_top_tweets",
    audience_analysis=True
)

print("📝 Content Recommendations:")
for rec in recommendations:
    print(f"  - {rec.topic}")
    print(f"    Predicted engagement: {rec.predicted_engagement}")
    print(f"    Best time to post: {rec.best_time}")

from xeepy.ai import ContentGenerator

# Use local Ollama
ai = ContentGenerator(
    provider="ollama",
    model="llama3",
    base_url="http://localhost:11434"  # Default Ollama URL
)

# Works the same as cloud providers
tweet = await ai.generate_tweet(
    topic="Python automation",
    style="educational"
)

from xeepy.ai import ContentGenerator

# Full configuration
ai = ContentGenerator(
    provider="openai",
    api_key="sk-...",
    model="gpt-4-turbo",
    temperature=0.7,      # Creativity (0-1)
    max_tokens=500,       # Response length
    timeout=30,           # Request timeout
)