Ollama X Twitter Engagement Optimization: AI Agent Analysis Guide 2025

# Core dependencies for Twitter AI agent
import ollama
import tweepy
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import json
import sqlite3
from apscheduler.schedulers.background import BackgroundScheduler
import matplotlib.pyplot as plt
import seaborn as sns

# Install core models for Twitter optimization
ollama pull llama3.1:8b
ollama pull mistral:7b
ollama pull codegemma:7b

# Verify model installation
ollama list

class TwitterAPIHandler:
    def __init__(self, api_key, api_secret, access_token, access_secret):
        """Initialize Twitter API connection with authentication"""
        auth = tweepy.OAuthHandler(api_key, api_secret)
        auth.set_access_token(access_token, access_secret)
        self.api = tweepy.API(auth, wait_on_rate_limit=True)
        
    def get_user_tweets(self, username, count=200):
        """Fetch recent tweets for engagement analysis"""
        tweets = []
        try:
            for tweet in tweepy.Cursor(
                self.api.user_timeline, 
                screen_name=username, 
                exclude_replies=True,
                include_rts=False,
                tweet_mode='extended'
            ).items(count):
                tweets.append({
                    'id': tweet.id,
                    'text': tweet.full_text,
                    'created_at': tweet.created_at,
                    'retweets': tweet.retweet_count,
                    'likes': tweet.favorite_count,
                    'replies': tweet.reply_count if hasattr(tweet, 'reply_count') else 0
                })
        except tweepy.TweepyException as e:
            print(f"Error fetching tweets: {e}")
        return tweets

class EngagementAnalyzer:
    def __init__(self):
        self.model = "llama3.1:8b"
        
    def analyze_tweet_performance(self, tweets_data):
        """Analyze engagement patterns using Ollama AI"""
        
        # Calculate engagement metrics
        df = pd.DataFrame(tweets_data)
        df['engagement_rate'] = (df['likes'] + df['retweets'] + df['replies']) / df['likes'].max()
        df['hour'] = pd.to_datetime(df['created_at']).dt.hour
        df['day_of_week'] = pd.to_datetime(df['created_at']).dt.dayofweek
        
        # Prepare data for AI analysis
        top_tweets = df.nlargest(10, 'engagement_rate')[['text', 'engagement_rate']].to_dict('records')
        low_tweets = df.nsmallest(10, 'engagement_rate')[['text', 'engagement_rate']].to_dict('records')
        
        # AI-powered pattern analysis
        analysis_prompt = f"""
        Analyze these Twitter engagement patterns and provide actionable insights:
        
        HIGH ENGAGEMENT TWEETS:
        {json.dumps(top_tweets, indent=2)}
        
        LOW ENGAGEMENT TWEETS:
        {json.dumps(low_tweets, indent=2)}
        
        Identify:
        1. Content themes that drive engagement
        2. Writing patterns in successful tweets
        3. Timing optimization recommendations
        4. Hashtag and mention strategies
        
        Provide specific, actionable recommendations.
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'user', 'content': analysis_prompt}
        ])
        
        return {
            'ai_insights': response['message']['content'],
            'performance_data': df.groupby('hour')['engagement_rate'].mean().to_dict(),
            'top_performing_content': top_tweets
        }

class CompetitorAnalyzer:
    def __init__(self, competitors_list):
        self.competitors = competitors_list
        self.model = "mistral:7b"
        
    def analyze_competitor_strategies(self):
        """Monitor and analyze competitor engagement tactics"""
        
        competitor_data = {}
        twitter_handler = TwitterAPIHandler() # Initialize with your credentials
        
        for competitor in self.competitors:
            tweets = twitter_handler.get_user_tweets(competitor, count=50)
            
            # AI analysis of competitor content
            content_analysis = self._analyze_content_strategy(tweets)
            competitor_data[competitor] = content_analysis
            
        return self._generate_competitive_insights(competitor_data)
    
    def _analyze_content_strategy(self, tweets):
        """Use Ollama to analyze competitor content patterns"""
        
        tweet_texts = [tweet['text'] for tweet in tweets[:10]]
        
        analysis_prompt = f"""
        Analyze these competitor tweets for strategic insights:
        
        {json.dumps(tweet_texts, indent=2)}
        
        Identify:
        1. Content pillars and themes
        2. Posting frequency patterns
        3. Engagement tactics used
        4. Audience interaction style
        5. Hashtag strategies
        
        Provide actionable intelligence for competitive advantage.
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'user', 'content': analysis_prompt}
        ])
        
        return response['message']['content']

class ContentOptimizer:
    def __init__(self):
        self.model = "llama3.1:8b"
        
    def generate_optimized_tweets(self, topic, engagement_data, count=5):
        """Generate tweets optimized for engagement based on historical data"""
        
        # Extract successful patterns from engagement data
        successful_patterns = self._extract_success_patterns(engagement_data)
        
        generation_prompt = f"""
        Generate {count} optimized tweets about "{topic}" based on these successful engagement patterns:
        
        {successful_patterns}
        
        Requirements:
        - Use proven engagement tactics from the patterns
        - Include relevant hashtags
        - Optimize for X/Twitter algorithm
        - Maintain authentic voice
        - Each tweet under 280 characters
        
        Format as JSON array with engagement prediction score (1-10).
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'user', 'content': generation_prompt}
        ])
        
        return self._parse_generated_content(response['message']['content'])
    
    def _extract_success_patterns(self, engagement_data):
        """Extract patterns from high-performing content"""
        # Implementation for pattern extraction
        pass

class EngagementDashboard:
    def __init__(self):
        self.db_connection = sqlite3.connect('twitter_analytics.db')
        self._create_tables()
        
    def _create_tables(self):
        """Create database tables for analytics storage"""
        cursor = self.db_connection.cursor()
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS tweet_performance (
                id INTEGER PRIMARY KEY,
                tweet_id TEXT UNIQUE,
                content TEXT,
                posted_at TIMESTAMP,
                likes INTEGER,
                retweets INTEGER,
                replies INTEGER,
                engagement_rate REAL,
                ai_prediction_score REAL
            )
        ''')
        self.db_connection.commit()
        
    def update_performance_metrics(self, tweet_data):
        """Update real-time engagement metrics"""
        cursor = self.db_connection.cursor()
        
        for tweet in tweet_data:
            cursor.execute('''
                INSERT OR REPLACE INTO tweet_performance 
                (tweet_id, content, posted_at, likes, retweets, replies, engagement_rate)
                VALUES (?, ?, ?, ?, ?, ?, ?)
            ''', (
                tweet['id'],
                tweet['text'],
                tweet['created_at'],
                tweet['likes'],
                tweet['retweets'], 
                tweet['replies'],
                (tweet['likes'] + tweet['retweets'] + tweet['replies']) / 100  # Normalize
            ))
        
        self.db_connection.commit()
        
    def generate_performance_report(self):
        """Generate AI-powered performance insights"""
        # Query recent performance data
        df = pd.read_sql_query('''
            SELECT * FROM tweet_performance 
            WHERE posted_at >= datetime('now', '-7 days')
            ORDER BY posted_at DESC
        ''', self.db_connection)
        
        # Generate visualizations
        self._create_engagement_charts(df)
        
        # AI-powered insights
        insights = self._generate_ai_insights(df)
        
        return {
            'performance_summary': df.describe(),
            'ai_insights': insights,
            'recommendations': self._get_optimization_recommendations(df)
        }

class SmartScheduler:
    def __init__(self):
        self.scheduler = BackgroundScheduler()
        self.model = "mistral:7b"
        
    def optimize_posting_schedule(self, historical_data):
        """Use AI to determine optimal posting times"""
        
        # Analyze engagement by time of day and day of week
        df = pd.DataFrame(historical_data)
        df['hour'] = pd.to_datetime(df['created_at']).dt.hour
        df['day_of_week'] = pd.to_datetime(df['created_at']).dt.dayofweek
        
        # Calculate average engagement by time slots
        time_analysis = df.groupby(['day_of_week', 'hour'])['engagement_rate'].mean().reset_index()
        
        # AI analysis for schedule optimization
        schedule_prompt = f"""
        Analyze this engagement data by time and recommend optimal posting schedule:
        
        {time_analysis.to_string()}
        
        Provide:
        1. Top 5 optimal posting times (day/hour)
        2. Times to avoid posting
        3. Frequency recommendations
        4. Seasonal adjustments to consider
        
        Format as actionable schedule recommendations.
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'user', 'content': schedule_prompt}
        ])
        
        return self._parse_schedule_recommendations(response['message']['content'])
    
    def schedule_optimized_posts(self, content_queue, optimal_times):
        """Schedule posts at AI-optimized times"""
        for i, content in enumerate(content_queue):
            optimal_time = optimal_times[i % len(optimal_times)]
            
            self.scheduler.add_job(
                func=self._post_tweet,
                trigger='cron',
                day_of_week=optimal_time['day'],
                hour=optimal_time['hour'],
                args=[content],
                id=f'tweet_{i}'
            )
        
        self.scheduler.start()

class ROIAnalyzer:
    def __init__(self):
        self.model = "llama3.1:8b"
        
    def calculate_engagement_roi(self, before_data, after_data, business_metrics):
        """Calculate ROI from engagement optimization"""
        
        # Calculate engagement improvements
        before_avg = np.mean([tweet['engagement_rate'] for tweet in before_data])
        after_avg = np.mean([tweet['engagement_rate'] for tweet in after_data])
        
        improvement_rate = ((after_avg - before_avg) / before_avg) * 100
        
        # AI analysis of business impact
        roi_prompt = f"""
        Analyze the business impact of this Twitter engagement optimization:
        
        Engagement Improvement: {improvement_rate:.2f}%
        Before Average Engagement: {before_avg:.4f}
        After Average Engagement: {after_avg:.4f}
        
        Business Metrics:
        {json.dumps(business_metrics, indent=2)}
        
        Calculate and explain:
        1. Estimated reach improvement
        2. Lead generation impact
        3. Brand awareness metrics
        4. Revenue attribution potential
        5. Cost savings from automation
        
        Provide specific ROI calculations and recommendations.
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'user', 'content': roi_prompt}
        ])
        
        return {
            'engagement_improvement': improvement_rate,
            'ai_roi_analysis': response['message']['content'],
            'performance_metrics': self._calculate_detailed_metrics(before_data, after_data)
        }

# Model selection optimization
def select_optimal_model(task_complexity):
    """Choose the most efficient model for each task"""
    if task_complexity == 'simple':
        return 'gemma:7b'
    elif task_complexity == 'medium':
        return 'mistral:7b'
    else:
        return 'llama3.1:8b'

class RateLimitManager:
    def __init__(self):
        self.request_times = []
        self.max_requests_per_window = 300  # Twitter API limit
        self.time_window = 900  # 15 minutes in seconds
        
    def can_make_request(self):
        """Check if request is within rate limits"""
        now = time.time()
        # Remove requests outside current window
        self.request_times = [t for t in self.request_times if now - t < self.time_window]
        
        return len(self.request_times) < self.max_requests_per_window

class MultiPlatformAnalyzer:
    def __init__(self):
        self.platforms = {
            'twitter': TwitterAPIHandler(),
            'linkedin': LinkedInAPIHandler(),
            'instagram': InstagramAPIHandler()
        }
        
    def cross_platform_analysis(self, content_strategy):
        """Analyze content performance across platforms"""
        platform_insights = {}
        
        for platform, handler in self.platforms.items():
            platform_data = handler.get_engagement_data()
            platform_insights[platform] = self._analyze_platform_specific_patterns(
                platform_data, platform
            )
            
        return self._generate_unified_strategy(platform_insights)

class TrendPredictor:
    def __init__(self):
        self.model = "mistral:7b"
        
    def predict_trending_topics(self, current_trends, historical_data):
        """Predict emerging trends using AI analysis"""
        
        prediction_prompt = f"""
        Based on current trending topics and historical patterns, predict emerging trends:
        
        Current Trends: {current_trends}
        Historical Patterns: {historical_data}
        
        Predict:
        1. Topics likely to trend in next 24 hours
        2. Content angles to capitalize on trends
        3. Optimal timing for trend-based content
        4. Risk assessment for trend participation
        
        Provide specific, actionable predictions.
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'user', 'content': prediction_prompt}
        ])
        
        return self._parse_trend_predictions(response['message']['content'])