| import os |
| import json |
| import gradio as gr |
| import nashpy as nash |
| import numpy as np |
| from huggingface_hub import InferenceClient |
|
|
| HF_TOKEN = os.getenv("HF_TOKEN") |
| client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct", token=HF_TOKEN) |
|
|
| def get_agent_data(p_a, p_b): |
| prompt = f""" |
| Analyze this conflict: |
| A: "{p_a}" |
| B: "{p_b}" |
| Extract 2 actions for each and assign happiness scores (0-10) for [A1/B1, A1/B2, A2/B1, A2/B2]. |
| Return ONLY JSON: |
| {{"a_actions": ["A1", "A2"], "b_actions": ["B1", "B2"], "payoffs_a": [v1,v2,v3,v4], "payoffs_b": [v1,v2,v3,v4]}} |
| """ |
| response = client.text_generation(prompt, max_new_tokens=400) |
| return json.loads(response[response.find("{"):response.rfind("}")+1]) |
|
|
| def resolve(p_a, p_b): |
| data = get_agent_data(p_a, p_b) |
| A = np.array(data["payoffs_a"]).reshape(2, 2) |
| B = np.array(data["payoffs_b"]).reshape(2, 2) |
| social = A + B |
| idx = np.unravel_index(np.argmax(social), social.shape) |
| |
| |
| choice_a = data["a_actions"][idx[0]] |
| choice_b = data["b_actions"][idx[1]] |
| |
| |
| explanation = f""" |
| ### ⚖️ Resolution: {choice_a} + {choice_b} |
| |
| --- |
| ### 🧠 Agent Logic Trace (The "Why") |
| I assigned the following 'Happiness Scores' to your situation: |
| * **{choice_a} + {choice_b}:** Combined Score of **{social[idx]}** (This is the highest possible total). |
| * **Other Options:** Lower combined scores (ranging from {np.min(social)} to {np.sort(social.flatten())[-2]}). |
| |
| **The Decision:** This outcome was chosen because it represents the **Social Optimum**. Even if it feels like a compromise, mathematically, no other combination of actions makes you 'happier' as a unit. |
| """ |
| return explanation |
|
|
| with gr.Blocks(theme=gr.themes.Soft()) as demo: |
| gr.Markdown("# 🤝 Agentic Conflict Mediator") |
| with gr.Row(): |
| a = gr.Textbox(label="Person A Perspective") |
| b = gr.Textbox(label="Person B Perspective") |
| btn = gr.Button("Analyze & Explain") |
| out = gr.Markdown() |
| btn.click(resolve, [a, b], out) |
|
|
| demo.launch() |
