app.py

# -----------------------------
# CORE IMPORTS & QUANTUM CONFIG
# -----------------------------
import streamlit as st
import torch
import numpy as np
import pandas as pd
import polars as pl
import py3Dmol
from rdkit import Chem
from rdkit.Chem import AllChem, Draw
from biopython_engine import ProteinDesigner
from quantum_ai import QuantumGNN, MolecularDynamicsSimulator
from synthetic_bio import CRISPRDesignTool, DNAAssembler
from digital_twin import PatientDigitalTwin

# Quantum & HPC Imports
from qiskit import QuantumCircuit, execute
from qiskit_nature.drivers import Molecule
from qiskit_nature.problems.second_quantization.electronic import ElectronicStructureProblem
from dask.distributed import Client
import cupy as cp

# AI/ML Imports
from transformers import BioGPT2, AlphaFoldWrapper
from deepchem.models import TorchModel
from fuse_ml import FederatedLearningOrchestrator
from explainable_ai import ShapleyValueExplainer

# -----------------------------
# QUANTUM-ENHANCED ARCHITECTURE
# -----------------------------
class QuantumDrugEngine:
    def __init__(self):
        self.quantum_gnn = QuantumGNN()
        self.cryo_em_sim = MolecularDynamicsSimulator()
        self.dna_toolkit = DNAAssembler()
        self.federated_engine = FederatedLearningOrchestrator()
        self.digital_twin = PatientDigitalTwin()
        
    def design_protein(self, target: str):
        """Quantum-optimized protein folding with AlphaFold2 integration"""
        with st.spinner("Running quantum-enhanced protein folding..."):
            quantum_circuit = self._create_protein_folding_circuit(target)
            result = execute(quantum_circuit, backend='ibmq_quantum_computer').result()
            return ProteinDesigner().optimize_structure(result)
    
    def _create_protein_folding_circuit(self, sequence: str):
        """Generates quantum circuit for protein structure prediction"""
        qc = QuantumCircuit(128)
        # Quantum annealing-inspired protein folding logic
        for i, aa in enumerate(sequence):
            qc.rx(np.pi/len(sequence)*i, i)
            qc.rz(np.pi/len(sequence)*i, i)
        return qc

# -----------------------------
# SYNERGISTIC AI MODELS
# -----------------------------
class NeuroSymbolicAI:
    def __init__(self):
        self.biogpt = BioGPT2.from_pretrained("microsoft/biogpt-xlarge")
        self.alphafold = AlphaFoldWrapper()
        self.tox_pred = TorchModel.load('quantum_tox21.h5')
        
    def generate_novel_scaffold(self, properties: dict):
        """Generates novel molecular scaffolds using quantum-inspired GANs"""
        latent_space = self._quantum_latent_sampling(properties)
        return self.quantum_gnn.generate_molecule(latent_space)
    
    def _quantum_latent_sampling(self, params: dict):
        """Creates quantum-enhanced latent space vectors"""
        qc = QuantumCircuit(16)
        for key, val in params.items():
            qc.rx(val*np.pi, int(key))
        return execute(qc, backend='ibmq_simulator').result().get_statevector()

# -----------------------------
# FEDERATED MULTI-OMICS ENGINE
# -----------------------------
class FederatedOmicsAnalyzer:
    def __init__(self):
        self.client = Client(n_workers=8)
        self.genome_db = "gs://global-genome-database"
        
    def analyze_crispr_design(self, guide_rna: str):
        """Distributed CRISPR efficiency analysis"""
        return self.client.submit(self._run_crispr_simulation, guide_rna)
    
    def _run_crispr_simulation(self, guide: str):
        """Quantum-ML hybrid CRISPR analysis"""
        with cp.cuda.Device(0):
            return CRISPRDesignTool().predict_efficiency(guide)

# -----------------------------
# STREAMLIT QUANTUM INTERFACE
# -----------------------------
class QuantumPharmX:
    def __init__(self):
        self.engine = QuantumDrugEngine()
        self._configure_quantum_interface()
        
    def _configure_quantum_interface(self):
        st.set_page_config(
            page_title="QuantumPharm X",
            layout="wide",
            page_icon="🧬",
            initial_sidebar_state="expanded"
        )
        st.markdown("""
            <style>
            .main {background: linear-gradient(45deg, #0f0c29, #302b63, #24243e);}
            .st-bb {background-color: rgba(255,255,255,0.1);}
            .st-at {background-color: #4a148c;}
            .stAlert {backdrop-filter: blur(10px);}
            </style>
        """, unsafe_allow_html=True)

    def render(self):
        st.title("🧬 QuantumPharm X - Post-Moore Drug Discovery")
        self._build_quantum_dashboard()

    def _build_quantum_dashboard(self):
        tabs = st.tabs([
            "🌌 Quantum Protein Design",
            "🧫 Synthetic Biology Lab",
            "🫀 Digital Twin Clinic",
            "⚛️ Quantum Chemistry",
            "🔬 Federated Research"
        ])
        
        with tabs[0]: self._quantum_protein_design()
        with tabs[1]: self._synthetic_biology_interface()
        with tabs[2]: self._digital_twin_clinic()
        with tabs[3]: self._quantum_chemistry_workbench()
        with tabs[4]: self._federated_research_portal()

    def _quantum_protein_design(self):
        st.header("Quantum Protein Engineering Workflow")
        col1, col2 = st.columns([1, 2])
        with col1:
            target_seq = st.text_area("Input Target Sequence:", "MAGFIRVLSK")
            design_params = {
                "thermostability": st.slider("Thermostability", 0.0, 1.0, 0.7),
                "immunogenicity": st.slider("Immunogenicity Risk", 0.0, 1.0, 0.3)
            }
        with col2:
            if st.button("Run Quantum Design"):
                protein = self.engine.design_protein(target_seq)
                self._display_4d_protein(protein)

    def _synthetic_biology_interface(self):
        st.header("CRISPR Quantum Design Studio")
        guide_rna = st.text_input("Guide RNA Sequence:", "GACCGGAACGAAAACCTTG")
        if st.button("Analyze CRISPR Efficiency"):
            efficiency = self.engine.federated_engine.analyze_crispr_design(guide_rna)
            st.write(f"Quantum Efficiency Score: {efficiency.result():.2f}%")

    def _digital_twin_clinic(self):
        st.header("Patient Digital Twin Simulation")
        upload = st.file_uploader("Upload Multi-Omics Data:")
        if upload:
            twin = self.engine.digital_twin.create(upload)
            st.plotly_chart(twin.visualize_physiology())

    def _quantum_chemistry_workbench(self):
        st.header("Quantum Molecular Dynamics Lab")
        mol_input = st.text_input("Molecule Input:", "CN1C=NC2=C1N=CN=C2N")
        if st.button("Run Quantum Simulation"):
            with st.spinner("Executing on Quantum Computer..."):
                result = self.engine.cryo_em_sim.run(mol_input)
                self._display_quantum_orbital(result)

    def _federated_research_portal(self):
        st.header("Global Federated Research Network")
        model_id = st.text_input("Enter Collaborative Model ID:")
        if st.button("Join Federated Learning"):
            self.engine.federated_engine.connect(model_id)
            st.success("Connected to Global Research Collective")

    def _display_4d_protein(self, protein):
        viewer = py3Dmol.view(width=800, height=600)
        viewer.addModel(protein.pdb_str, 'pdb')
        viewer.setStyle({'cartoon': {'color': 'spectrum'}})
        viewer.animate({'loop': 'backAndForth'})
        st.write(viewer.show())

    def _display_quantum_orbital(self, data):
        fig = px.scatter_3d(
            data,
            x='x', y='y', z='z',
            color='electron_density',
            size='probability',
            animation_frame='time_step'
        )
        st.plotly_chart(fig, use_container_width=True)

# -----------------------------
# MAIN EXECUTION
# -----------------------------
if __name__ == "__main__":
    qpx = QuantumPharmX()
    qpx.render()