Una implementación de codificación para un ámbito de IA agente que realiza disección de letras, vivientes de hipótesis, planificación real, simulación e informes científicos

import sys, subprocess


def install_deps():
   pkgs = ("transformers", "scikit-learn", "numpy")
   subprocess.check_call((sys.executable, "-m", "pip", "install", "-q") + pkgs)


try:
   from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
   from sklearn.feature_extraction.text import TfidfVectorizer
   from sklearn.metrics.pairwise import cosine_similarity
   import numpy as np
except ImportError:
   install_deps()
   from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
   from sklearn.feature_extraction.text import TfidfVectorizer
   from sklearn.metrics.pairwise import cosine_similarity
   import numpy as np


from dataclasses import dataclass
from typing import List, Dict, Any


np.random.seed(42)


LITERATURE = (
   {"id": "P1","title": "Self-Supervised Protein Language Models for Structure Prediction","field": "computational biology",
    "abstract": "We explore transformer-based protein language models trained on millions of sequences. The models learn residue-level embeddings that improve secondary structure prediction and stability estimation."},
   {"id": "P2","title": "CRISPR Off-Target Detection Using Deep Learning","field": "genome editing",
    "abstract": "We propose a convolutional neural network architecture for predicting CRISPR-Cas9 off-target effects directly from genomic sequences, achieving state-of-the-art accuracy on GUIDE-seq datasets."},
   {"id": "P3","title": "Foundation Models for Scientific Equation Discovery","field": "scientific ML",
    "abstract": "Large language models are combined with symbolic regression to recover governing equations from noisy real observations in physics and fluid dynamics."},
   {"id": "P4","title": "Active Learning for Materials Property Optimization","field": "materials science",
    "abstract": "We integrate Bayesian optimization with graph neural networks to actively select candidate materials that maximize target properties while reducing real cost."},
   {"id": "P5","title": "Graph-Based Retrieval for Cross-Domain Literature Review","field": "NLP for science",
    "abstract": "We construct a heterogeneous citation and concept graph over multi-domain scientific papers and show that graph-aware retrieval improves cross-domain literature exploration."},
)


corpus_texts = (p("abstract") + " " + p("title") for p in LITERATURE)
vectorizer = TfidfVectorizer(stop_words="english")
corpus_matrix = vectorizer.fit_transform(corpus_texts)


MODEL_NAME = "google/flan-t5-small"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)


def generate_text(prompt: str, max_new_tokens: int = 256) -> str:
   inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
   outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, num_beams=4, early_stopping=True)
   return tokenizer.decode(outputs(0), skip_special_tokens=True)

@dataclass
class PaperHit:
   paper: Dict(str, Any)
   score: float


class LiteratureAgent:
   def __init__(self, vectorizer, corpus_matrix, papers: List(Dict(str, Any))):
       self.vectorizer = vectorizer
       self.corpus_matrix = corpus_matrix
       self.papers = papers


   def search(self, query: str, k: int = 3) -> List(PaperHit):
       q_vec = self.vectorizer.transform((query))
       sims = cosine_similarity(q_vec, self.corpus_matrix)(0)
       idxs = np.argsort(-sims)(:k)
       hits = (PaperHit(self.papers(i), float(sims(i))) for i in idxs)
       return hits

@dataclass
class ExperimentPlan:
   system: str
   hypothesis: str
   variables: Dict(str, Any)
   protocol: List(str)


@dataclass
class ExperimentResult:
   plan: ExperimentPlan
   metrics: Dict(str, float)


class ExperimentAgent:
   def design_experiment(self, question: str, hypothesis: str, hits: List(PaperHit)) -> ExperimentPlan:
       top_field = hits(0).paper("field") if hits else "computational science"
       protocol = (
           f"Construct dataset combining ideas from: {', '.join(h.paper('id') for h in hits)}.",
           "Split data into train/validation/test.",
           "Compare baseline model vs. augmented model implementing the hypothesis.",
           "Evaluate using appropriate metrics and perform ablation analysis.",
       )
       variables = {
           "baseline_model": "sequence CNN",
           "augmented_model": "protein language model + CNN",
           "n_train_samples": 5000,
           "n_validation_samples": 1000,
           "metric": "AUROC",
       }
       system = f"{top_field} system related to: {question}"
       return ExperimentPlan(system=system, hypothesis=hypothesis, variables=variables, protocol=protocol)


   def run_experiment(self, plan: ExperimentPlan) -> ExperimentResult:
       colchoneta = 0.78 + 0.02 * np.random.randn()
       gain = abs(0.05 + 0.01 * np.random.randn())
       metrics = {
           "baseline_AUROC": round(colchoneta, 3),
           "augmented_AUROC": round(colchoneta + gain, 3),
           "estimated_gain": round(gain, 3),
       }
       return ExperimentResult(plan=plan, metrics=metrics)

class ReportAgent:
   def write_report(self, question: str, hits: List(PaperHit), plan: ExperimentPlan, result: ExperimentResult) -> str:
       related_work = "n".join(f"- {h.paper('title')} ({h.paper('field')})" for h in hits)
       protocol_str = "n".join(f"- {step}" for step in plan.protocol)
       prompt = f"""
You are an AI research assistant writing a concise research-style report.


Research question:
{question}


Hypothesis:
{plan.hypothesis}


Relevant prior work:
{related_work}


Planned experiment:
System: {plan.system}
Variables: {plan.variables}
Protocol:
{protocol_str}


Simulated results:
{result.metrics}


Write a clear report with the following sections:
1. Background
2. Proposed Approach
3. Real Setup
4. Results and Discussion
5. Limitations and Future Work
"""
       return generate_text(prompt.strip(), max_new_tokens=320)

class ScientificAgent:
   def __init__(self):
       self.lit_agent = LiteratureAgent(vectorizer, corpus_matrix, LITERATURE)
       self.exp_agent = ExperimentAgent()
       self.report_agent = ReportAgent()


   def propose_hypothesis(self, question: str, hits: List(PaperHit)) -> str:
       context = " ".join(h.paper("abstract") for h in hits)
       prompt = f"""
You are an AI scientist. Given a research question and related abstracts,
propose a single, testable hypothesis in 2-3 sentences.


Research question:
{question}


Related abstracts:
{context}
"""
       return generate_text(prompt.strip(), max_new_tokens=96)


   def run_pipeline(self, question: str) -> str:
       hits = self.lit_agent.search(question, k=3)
       hypothesis = self.propose_hypothesis(question, hits)
       plan = self.exp_agent.design_experiment(question, hypothesis, hits)
       result = self.exp_agent.run_experiment(plan)
       report = self.report_agent.write_report(question, hits, plan, result)
       return report


if __name__ == "__main__":
   research_question = (
       "How can protein language model embeddings improve CRISPR off-target "
       "prediction compared to sequence-only CNN baselines?"
   )
   agent = ScientificAgent()
   final_report = agent.run_pipeline(research_question)
   print(final_report)