import gradio as gr
import json
import numpy as np
import time

class NebulaXSimulator:
    def __init__(self):
        self.benchmarks = {
            "MMLU": 92.3,
            "GSM8K": 94.8,
            "HumanEval": 89.6,
            "HellaSwag": 95.1,
            "ARC": 96.5,
            "TruthfulQA": 78.9
        }
        
    def process_query(self, query, max_length=200):
        time.sleep(0.5)
        responses = {
            "capital": "The capital of France is Paris.",
            "hello": "Hello! I am NEBULA-X, processing at the speed of light!",
            "default": f"Processing: {query}"
        }
        
        query_lower = query.lower()
        for key in responses:
            if key in query_lower:
                return responses[key]
        return responses["default"]
    
    def run_benchmark(self, benchmark_name):
        if benchmark_name in self.benchmarks:
            return {
                "benchmark": benchmark_name,
                "score": self.benchmarks[benchmark_name],
                "status": "completed"
            }
        return None

simulator = NebulaXSimulator()

def process_text(input_text, max_length):
    return simulator.process_query(input_text, max_length)

def run_benchmark(benchmark_name):
    result = simulator.run_benchmark(benchmark_name)
    if result:
        return f"Benchmark: {result['benchmark']}\nScore: {result['score']}%\nStatus: {result['status']}"
    return "Benchmark not found"

with gr.Blocks(title="NEBULA-X") as demo:
    gr.Markdown("# NEBULA-X: Photonic Neural Network")
    gr.Markdown("### 175B Parameters - Processing at Speed of Light")
    
    with gr.Tab("Chat"):
        text_input = gr.Textbox(label="Input", placeholder="Ask anything...")
        max_length = gr.Slider(50, 500, 200, label="Max Length")
        generate_btn = gr.Button("Generate")
        output_text = gr.Textbox(label="Response")
        
        generate_btn.click(process_text, [text_input, max_length], output_text)
    
    with gr.Tab("Benchmarks"):
        benchmark_select = gr.Dropdown(
            choices=["MMLU", "GSM8K", "HumanEval", "HellaSwag", "ARC", "TruthfulQA"],
            label="Select Benchmark",
            value="MMLU"
        )
        run_btn = gr.Button("Run")
        benchmark_output = gr.Textbox(label="Results")
        
        run_btn.click(run_benchmark, benchmark_select, benchmark_output)
    
    gr.Markdown("By Francisco Angulo de Lafuente (Agnuxo)")

demo.launch()