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| import gradio as gr | |
| import pandas as pd | |
| from datetime import datetime | |
| import random | |
| # Simplified simulation for demo purposes | |
| class SimpleOasisDemo: | |
| def __init__(self): | |
| self.agents = [] | |
| self.posts = [] | |
| self.interactions = [] | |
| self.simulation_running = False | |
| def create_sample_agents(self, num_agents=5): | |
| """Create sample agents for demo""" | |
| agent_profiles = [ | |
| {"id": 1, "name": "Alice", "interests": ["technology", "AI"], "personality": "curious"}, | |
| {"id": 2, "name": "Bob", "interests": ["sports", "fitness"], "personality": "active"}, | |
| {"id": 3, "name": "Carol", "interests": ["art", "music"], "personality": "creative"}, | |
| {"id": 4, "name": "David", "interests": ["politics", "news"], "personality": "analytical"}, | |
| {"id": 5, "name": "Eve", "interests": ["travel", "food"], "personality": "adventurous"} | |
| ] | |
| self.agents = agent_profiles[:num_agents] | |
| return f"Created {num_agents} agents: {', '.join([agent['name'] for agent in self.agents])}" | |
| def simulate_posts(self, topic="general", num_posts=3): | |
| """Simulate posts creation""" | |
| sample_posts = { | |
| "technology": [ | |
| "Just read about the latest AI breakthrough! 🤖", | |
| "The future of quantum computing looks promising", | |
| "New smartphone features are getting crazy good" | |
| ], | |
| "sports": [ | |
| "Great game last night! 🏀", | |
| "Training hard for the marathon next month", | |
| "Team performance was outstanding today" | |
| ], | |
| "general": [ | |
| "Beautiful sunset today! 🌅", | |
| "Coffee tastes better on Monday mornings ☕", | |
| "Weekend plans: relax and recharge" | |
| ] | |
| } | |
| posts_to_create = sample_posts.get(topic, sample_posts["general"]) | |
| for i in range(min(num_posts, len(posts_to_create))): | |
| if i < len(self.agents): | |
| post = { | |
| "id": len(self.posts) + 1, | |
| "author": self.agents[i]["name"], | |
| "content": posts_to_create[i], | |
| "timestamp": datetime.now().strftime("%H:%M:%S"), | |
| "likes": random.randint(0, 10), | |
| "reposts": random.randint(0, 5) | |
| } | |
| self.posts.append(post) | |
| return f"Created {min(num_posts, len(posts_to_create))} posts about {topic}" | |
| def simulate_interactions(self): | |
| """Simulate agent interactions""" | |
| if not self.posts: | |
| return "No posts to interact with. Create some posts first!" | |
| interactions = [] | |
| for agent in self.agents: | |
| # Random chance to interact with posts | |
| if random.random() < 0.7: # 70% chance to interact | |
| post = random.choice(self.posts) | |
| action = random.choice(["like", "repost", "comment"]) | |
| interaction = { | |
| "agent": agent["name"], | |
| "action": action, | |
| "post_id": post["id"], | |
| "post_author": post["author"], | |
| "timestamp": datetime.now().strftime("%H:%M:%S") | |
| } | |
| if action == "like": | |
| post["likes"] += 1 | |
| elif action == "repost": | |
| post["reposts"] += 1 | |
| interactions.append(interaction) | |
| self.interactions.append(interaction) | |
| return f"Generated {len(interactions)} interactions" | |
| def get_simulation_status(self): | |
| """Get current simulation status""" | |
| status = f""" | |
| **Simulation Status:** | |
| - Agents: {len(self.agents)} | |
| - Posts: {len(self.posts)} | |
| - Interactions: {len(self.interactions)} | |
| """ | |
| return status | |
| def get_posts_display(self): | |
| """Format posts for display""" | |
| if not self.posts: | |
| return "No posts yet. Start the simulation!" | |
| posts_text = "**Recent Posts:**\n\n" | |
| for post in self.posts[-5:]: # Show last 5 posts | |
| posts_text += f"**{post['author']}** ({post['timestamp']})\n" | |
| posts_text += f"{post['content']}\n" | |
| posts_text += f"👍 {post['likes']} | 🔄 {post['reposts']}\n\n" | |
| return posts_text | |
| def get_interactions_display(self): | |
| """Format interactions for display""" | |
| if not self.interactions: | |
| return "No interactions yet." | |
| interactions_text = "**Recent Interactions:**\n\n" | |
| for interaction in self.interactions[-10:]: # Show last 10 interactions | |
| interactions_text += f"**{interaction['agent']}** {interaction['action']}d post by **{interaction['post_author']}** ({interaction['timestamp']})\n" | |
| return interactions_text | |
| # Initialize demo | |
| demo_instance = SimpleOasisDemo() | |
| def run_simulation_step(num_agents, topic, num_posts): | |
| """Run one step of simulation""" | |
| results = [] | |
| # Create agents | |
| if not demo_instance.agents or len(demo_instance.agents) != num_agents: | |
| result = demo_instance.create_sample_agents(num_agents) | |
| results.append(result) | |
| # Create posts | |
| result = demo_instance.simulate_posts(topic, num_posts) | |
| results.append(result) | |
| # Simulate interactions | |
| result = demo_instance.simulate_interactions() | |
| results.append(result) | |
| # Get displays | |
| status = demo_instance.get_simulation_status() | |
| posts = demo_instance.get_posts_display() | |
| interactions = demo_instance.get_interactions_display() | |
| return status, posts, interactions, "\n".join(results) | |
| def reset_simulation(): | |
| """Reset the simulation""" | |
| demo_instance.agents = [] | |
| demo_instance.posts = [] | |
| demo_instance.interactions = [] | |
| return "Simulation reset!", "", "", "" | |
| # Create Gradio interface | |
| with gr.Blocks(title="OASIS Demo - Social Media Simulation", theme=gr.themes.Soft()) as app: | |
| gr.Markdown(""" | |
| # 🏝️ OASIS Demo: Open Agent Social Interaction Simulations | |
| This is a simplified demo of OASIS - a scalable social media simulator with AI agents. | |
| **Features demonstrated:** | |
| - Multi-agent social interactions | |
| - Post creation and engagement | |
| - Real-time simulation updates | |
| **Note:** This is a simplified version for demonstration. The full OASIS supports up to 1 million agents! | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Simulation Controls") | |
| num_agents = gr.Slider( | |
| minimum=2, | |
| maximum=5, | |
| value=3, | |
| step=1, | |
| label="Number of Agents" | |
| ) | |
| topic = gr.Dropdown( | |
| choices=["general", "technology", "sports"], | |
| value="general", | |
| label="Post Topic" | |
| ) | |
| num_posts = gr.Slider( | |
| minimum=1, | |
| maximum=5, | |
| value=2, | |
| step=1, | |
| label="Posts per Step" | |
| ) | |
| run_btn = gr.Button("🚀 Run Simulation Step", variant="primary") | |
| reset_btn = gr.Button("🔄 Reset Simulation", variant="secondary") | |
| status_output = gr.Textbox( | |
| label="Simulation Status", | |
| lines=4, | |
| interactive=False | |
| ) | |
| log_output = gr.Textbox( | |
| label="Action Log", | |
| lines=3, | |
| interactive=False | |
| ) | |
| with gr.Column(scale=2): | |
| gr.Markdown("### Simulation Output") | |
| posts_output = gr.Markdown( | |
| value="No posts yet. Start the simulation!", | |
| label="Posts Feed" | |
| ) | |
| interactions_output = gr.Markdown( | |
| value="No interactions yet.", | |
| label="Interactions Feed" | |
| ) | |
| gr.Markdown(""" | |
| ### About OASIS | |
| OASIS (Open Agent Social Interaction Simulations) is a research framework for studying social media dynamics at scale: | |
| - **Scalable**: Supports up to 1 million AI agents | |
| - **Realistic**: Agents exhibit human-like social behaviors | |
| - **Flexible**: Supports multiple platforms (Twitter, Reddit) | |
| - **Research-focused**: Study information spread, polarization, and social phenomena | |
| **Repository:** [github.com/camel-ai/oasis](https://github.com/camel-ai/oasis) | |
| **Documentation:** [docs.oasis.camel-ai.org](https://docs.oasis.camel-ai.org) | |
| """) | |
| # Event handlers | |
| run_btn.click( | |
| fn=run_simulation_step, | |
| inputs=[num_agents, topic, num_posts], | |
| outputs=[status_output, posts_output, interactions_output, log_output] | |
| ) | |
| reset_btn.click( | |
| fn=reset_simulation, | |
| outputs=[status_output, posts_output, interactions_output, log_output] | |
| ) | |
| if __name__ == "__main__": | |
| app.launch(server_name="0.0.0.0", server_port=7860, share=False) | |