import openai
import json
from typing import Dict, List, Optional

class ChatGPTCoPilot:
    """
    Prototype implementation of Reddit's predicted ChatGPT evolution.
    Moves from reactive chat to proactive task automation.
    """
    
    def __init__(self, api_key: str):
        self.client = openai.OpenAI(api_key=api_key)
        self.context_history = []
        
    def anticipate_task(self, user_input: str) -> Dict:
        """
        Predicts and executes tasks based on user context.
        Reddit's key insight: AI should anticipate needs, not just respond.
        """
        prompt = f"""Based on user input: '{user_input}',
        predict the most likely next 3 tasks they'll need.
        Return as JSON with keys: 'predicted_tasks', 'immediate_action', 'automation_possible'.
        """
        
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            response_format={"type": "json_object"}
        )
        
        return json.loads(response.choices[0].message.content)
    
    def execute_workflow(self, task_plan: Dict) -> str:
        """
        Executes the predicted workflow automatically.
        This eliminates copy-paste manual steps mentioned in the article.
        """
        # Implementation would integrate with user's apps/APIs
        return f"Executed: {task_plan.get('immediate_action', 'No action')}"

# Usage example:
# copilot = ChatGPTCoPilot("your-api-key")
# prediction = copilot.anticipate_task("I need to analyze this dataset")
# result = copilot.execute_workflow(prediction)

From Conversation to Co-Pilot: The Data-Backed Prediction

A recent, highly engaged discussion on the OpenAI subreddit, garnering 235 upvotes and 41 comments, provides a unique data point on user expectations. The central thesis is clear: ChatGPT is poised to evolve from a reactive conversational partner into a proactive, integrated co-pilot. This isn't about marginal improvements in response quality; it's a paradigm shift in functionality.

Why This Community Insight Matters

This data is significant because it comes from power users—the early adopters and developers who stress-test these systems daily. Their collective prediction, evidenced by the high engagement (an 87% upvote ratio), signals a demand for AI that doesn't just answer questions but anticipates needs and executes tasks within a user's digital environment. It reflects a growing impatience with copy-paste workflows and a desire for seamless automation.

The discussion highlights three key anticipated shifts, moving from:

• Text-Based Q&A to Action-Oriented Execution: Users expect to command, "Draft the email, pull the relevant sales data from our CRM, and schedule the follow-up," with ChatGPT handling the cross-application legwork.
• Isolated Chat to Context-Aware Integration: Future iterations are predicted to have deep, permission-based awareness of your open documents, emails, and code, allowing for assistance grounded in your immediate context.
• General Knowledge to Personalized Expertise: The AI would learn from your specific projects and communication style, becoming a true extension of your individual workflow.

The Immediate Impact: A Change in Daily Workflow

For professionals, this predicted shift means the end of treating AI as a separate tool. Instead, it becomes the central orchestrator of your digital workspace. The 41-comment thread frequently cited examples like automated report generation from raw data, intelligent meeting summarization with action item delegation, and dynamic code refactoring based on real-time error logs. The value proposition shifts from information retrieval to time reclamation.

What Comes Next: The Integration Frontier

The logical next step, as implied by the community data, is not a smarter chatbot in a vacuum. It's the race for dominant integration depth. The winning AI platform will be the one that most securely and powerfully connects to the suite of tools—from Google Workspace and Microsoft 365 to GitHub and Figma—that people already use. The challenge will be balancing this profound utility with robust privacy and user control.

The clear takeaway from this user-driven data: The most significant near-future development in AI may not be a larger model, but a more deeply embedded one. The conversation has moved on from what AI knows to what it can do for you without being asked.