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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):
"""Integraci贸n 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 cient铆fico"""
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 espec铆ficos"""
module = self.modules[domain]
optimized_params = self._optimize_parameters(parameters)
return module.predict(optimized_params)
def _optimize_parameters(self, params):
"""Optimizaci贸n mediante redes neuronales diferenciales"""
return torch.nn.functional.normalize(torch.tensor(params), dim=0)
# Configuraci贸n 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="Descripci贸n"),
gr.Dropdown(["protein_folding", "material_design", "drug_discovery", "climate_modeling"])
],
outputs=gr.JSON(),
live=True
).launch() |