app.py

# NEXUS_CORE.py
import torch
from transformers import AutoModel, AutoProcessor
from sentence_transformers import SentenceTransformer
import numpy as np
import gradio as gr
from datetime import datetime

class NexusScientificAccelerator:
    def __init__(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.modules = {
            "protein_folding": self._load_protein_module(),
            "material_design": self._load_materials_module(),
            "drug_discovery": self._load_drug_module(),
            "climate_modeling": self._load_climate_module()
        }
        self.multimodal_processor = AutoProcessor.from_pretrained("Open-Qwen2VL")
        self.science_agent = AutoModel.from_pretrained("Molmo-72B")
        self.voice_engine = pipeline("automatic-speech-recognition", model="Whisper-Large-Turbo")
    
    def _load_protein_module(self):
        return SentenceTransformer("prot_bert", device=self.device)
    
    def _load_materials_module(self):
        return AutoModel.from_pretrained("MatSci-BERT", device=self.device)
    
    def _load_drug_module(self):
        return AutoModel.from_pretrained("DrugDiscovery-3B", device=self.device)
    
    def _load_climate_module(self):
        return AutoModel.from_pretrained("ClimaNet-XL", device=self.device)
    
    def process_multimodal_input(self, inputs):
        """Integración multimodal avanzada"""
        if 'audio' in inputs:
            transcribed = self.voice_engine(inputs['audio'])
            inputs['text'] = f"{inputs.get('text', '')} {transcribed['text']}"
        
        if 'image' in inputs:
            visual_emb = self.multimodal_processor(images=inputs['image'], return_tensors="pt").to(self.device)
            inputs['visual_emb'] = visual_emb
        
        return inputs
    
    def scientific_reasoning(self, query, context):
        """Motor de razonamiento científico"""
        inputs = self.multimodal_processor(
            text=query,
            images=context.get('image'),
            return_tensors="pt"
        ).to(self.device)
        
        with torch.no_grad():
            outputs = self.science_agent(**inputs)
        
        return outputs.logits.cpu().numpy()
    
    def accelerate_discovery(self, domain, parameters):
        """Acelerador de descubrimientos específicos"""
        module = self.modules[domain]
        optimized_params = self._optimize_parameters(parameters)
        return module.predict(optimized_params)
    
    def _optimize_parameters(self, params):
        """Optimización mediante redes neuronales diferenciales"""
        return torch.nn.functional.normalize(torch.tensor(params), dim=0)

# Configuración de la API
nexus = NexusScientificAccelerator()

def gradio_interface(audio, image, text, domain):
    inputs = nexus.process_multimodal_input({
        'audio': audio,
        'image': image,
        'text': text
    })
    
    results = nexus.accelerate_discovery(
        domain=domain,
        parameters=inputs
    )
    
    return {
        "hypothesis": results[:5],
        "simulation_data": results[5:10],
        "optimized_parameters": results[10:]
    }

if __name__ == "__main__":
    gr.Interface(
        fn=gradio_interface,
        inputs=[
            gr.Audio(source="microphone"),
            gr.Image(type="filepath"),
            gr.Textbox(label="Descripción"),
            gr.Dropdown(["protein_folding", "material_design", "drug_discovery", "climate_modeling"])
        ],
        outputs=gr.JSON(),
        live=True
    ).launch()