Update app.py

app.py

@@ -1,205 +1,343 @@
-
-
 # -----------------------------
-# CORE IMPORTS & QUANTUM CONFIG
+# IMPORTS & CONFIGURATION
 # -----------------------------
 import streamlit as st
-import torch
-import numpy as np
 import pandas as pd
-import polars as pl
-import py3Dmol
+import matplotlib.pyplot as plt
+import seaborn as sns
+import logging
+import re
+import time
 from rdkit import Chem
-from rdkit.Chem import AllChem, Draw
-from biopython  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
+from rdkit.Chem import Draw
+
+# Configure logging for detailed diagnostics
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[logging.FileHandler("pris_debug.log"), logging.StreamHandler()]
+)
+logger = logging.getLogger("PRIS")
 
 # -----------------------------
-# QUANTUM-ENHANCED ARCHITECTURE
+# FALLBACK / SCRAPED DATA
 # -----------------------------
-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
+# Fallback data for compound profiling (simulated scraped or cached data)
+FALLBACK_COMPOUND_DATA = {
+    "aspirin": {
+        "molecular_formula": "C9H8O4",
+        "iupac_name": "2-acetoxybenzoic acid",
+        "canonical_smiles": "CC(=O)OC1=CC=CC=C1C(=O)O",  # Valid SMILES for Aspirin
+        "molecular_weight": "180.16",
+        "logp": "N/A"
+    },
+    "ibuprofen": {
+        "molecular_formula": "C13H18O2",
+        "iupac_name": "2-(4-isobutylphenyl)propanoic acid",
+        "canonical_smiles": "CC(C)Cc1ccc(cc1)C(C)C(=O)O",
+        "molecular_weight": "206.28",
+        "logp": "3.97"
+    }
+}
+
+# Fallback clinical trial data (simulated scraped data)
+FALLBACK_CLINICAL_DATA = {
+    "nct1": [
+        {
+            "protocolSection": {
+                "identificationModule": {"briefTitle": "A Study of Novel Therapeutics in Diabetes"},
+                "statusModule": {"overallStatus": "Completed"},
+                "designModule": {"phases": ["Phase II"], "enrollmentInfo": {"count": "120"}}
+            }
+        },
+        {
+            "protocolSection": {
+                "identificationModule": {"briefTitle": "Evaluation of Biomarkers in Diabetic Patients"},
+                "statusModule": {"overallStatus": "Recruiting"},
+                "designModule": {"phases": ["Phase I"], "enrollmentInfo": {"count": "60"}}
+            }
+        }
+    ],
+    "cancer": [
+        {
+            "protocolSection": {
+                "identificationModule": {"briefTitle": "Immunotherapy Trials in Advanced Cancer"},
+                "statusModule": {"overallStatus": "Active, not recruiting"},
+                "designModule": {"phases": ["Phase III"], "enrollmentInfo": {"count": "250"}}
+            }
+        }
+    ]
+}
 
 # -----------------------------
-# SYNERGISTIC AI MODELS
+# CORE INFRASTRUCTURE
 # -----------------------------
-class NeuroSymbolicAI:
+class PharmaResearchEngine:
+    """
+    Core engine using local fallback data.
+    This engine simulates data retrieval without any external API calls.
+    """
     def __init__(self):
-        self.biogpt = BioGPT2.from_pretrained("microsoft/biogpt-xlarge")
-        self.alphafold = AlphaFoldWrapper()
-        self.tox_pred = TorchModel.load('quantum_tox21.h5')
+        # In a live system, API clients would be initialized here.
+        pass
+
+    def get_compound_profile(self, identifier: str) -> dict:
+        """
+        Retrieve a comprehensive compound profile.
+        Checks input validity and returns fallback data if available.
+        """
+        if not self._is_valid_compound_input(identifier):
+            msg = (f"The input '{identifier}' appears to reference a disease term rather than a chemical compound. "
+                   "For disease-related inquiries, please use the Clinical Trial Analytics module.")
+            logger.warning(msg)
+            st.error(msg)
+            return {}
         
-    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()
+        key = identifier.lower().strip()
+        if key in FALLBACK_COMPOUND_DATA:
+            logger.info(f"Using fallback data for compound '{identifier}'.")
+            return FALLBACK_COMPOUND_DATA[key]
+        else:
+            msg = f"No compound data found for '{identifier}'. Please verify the compound name or SMILES."
+            logger.error(msg)
+            st.error(msg)
+            return {}
+
+    def _is_valid_compound_input(self, user_input: str) -> bool:
+        """
+        Determines if the user input is a valid compound identifier.
+        Rejects inputs containing known disease terms.
+        """
+        input_lower = user_input.lower().strip()
+        disease_terms = ['diabetes', 'cancer', 'hypertension', 'asthma']
+        if any(term in input_lower for term in disease_terms):
+            return False
+        # Accept input with SMILES-specific characters (e.g., '=', '(', ')', '#')
+        if re.search(r"[=\(\)#]", user_input):
+            return True
+        # Otherwise, accept if input is alphanumeric with spaces/hyphens
+        if re.match(r'^[A-Za-z0-9\s\-]+$', user_input):
+            return True
+        return False
 
 # -----------------------------
-# FEDERATED MULTI-OMICS ENGINE
+# INTELLIGENCE MODULES
 # -----------------------------
-class FederatedOmicsAnalyzer:
+class ClinicalIntelligence:
+    """
+    Module for clinical trial analytics using fallback data.
+    """
     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)
+        self.engine = PharmaResearchEngine()
+
+    def get_trial_landscape(self, query: str) -> list:
+        """
+        Retrieve clinical trial data based on the query.
+        Returns fallback data if available.
+        """
+        key = query.lower().strip()
+        if key in FALLBACK_CLINICAL_DATA:
+            logger.info(f"Using fallback clinical data for query '{query}'.")
+            return FALLBACK_CLINICAL_DATA[key]
+        else:
+            msg = f"No clinical trial data available for '{query}'."
+            logger.error(msg)
+            st.error("Failed to retrieve clinical trials. Please try a different query.")
+            return []
+
+class AIDrugInnovator:
+    """
+    Simulated AI module that returns a pre-defined drug development strategy.
+    """
+    def __init__(self):
+        # No external AI API is used in offline mode.
+        pass
+
+    def generate_strategy(self, target: str, strategy: str) -> str:
+        """
+        Returns a comprehensive, simulated strategy for drug development.
+        """
+        simulated_response = f"""
+# First-in-Class Strategy for {target.title()}
+
+## Target Validation Approach
+- **Literature Review:** Perform an exhaustive review of current literature to understand the molecular mechanisms of {target}.
+- **Preclinical Studies:** Validate targets using state-of-the-art in vitro and in vivo models.
+- **Omics Integration:** Analyze genomic and proteomic data to identify actionable targets.
+- **Collaborative Research:** Partner with leading academic and clinical institutions.
+
+## Lead Optimization Tactics
+- **Chemical Optimization:** Enhance lead compounds to improve potency, selectivity, and pharmacokinetics.
+- **High-Throughput Screening:** Utilize modern screening techniques for iterative lead refinement.
+- **In Vivo Efficacy:** Conduct thorough animal studies to assess safety and efficacy.
+- **Intellectual Property:** Secure robust patents through comprehensive patent landscaping.
+
+## Clinical Trial Design
+- **Phase I:** Safety and dosage studies.
+- **Phase II:** Efficacy and side-effect profiling.
+- **Phase III:** Large-scale trials comparing with current standards.
+- **Phase IV:** Post-marketing surveillance.
+- **Adaptive Designs:** Incorporate adaptive trial designs for efficiency.
+
+## Regulatory Pathway Analysis
+- **Pre-IND Consultation:** Initiate early dialogue with regulatory authorities.
+- **IND Submission:** Prepare robust submissions with complete preclinical data.
+- **Continuous Engagement:** Maintain ongoing communication with regulators.
+- **Expedited Programs:** Explore Fast Track and Breakthrough Therapy designations.
+
+## Commercial Potential Assessment
+- **Market Research:** Analyze market trends and unmet needs.
+- **Patient Segmentation:** Identify target patient demographics.
+- **Pricing & Reimbursement:** Develop strategic pricing and reimbursement plans.
+- **Go-To-Market Strategy:** Formulate a comprehensive marketing and sales plan.
+"""
+        return simulated_response
 
 # -----------------------------
-# STREAMLIT QUANTUM INTERFACE
+# STREAMLIT INTERFACE
 # -----------------------------
-class QuantumPharmX:
+class PharmaResearchInterface:
+    """
+    Streamlit interface for the Pharma Research Intelligence Suite using fallback data.
+    """
     def __init__(self):
-        self.engine = QuantumDrugEngine()
-        self._configure_quantum_interface()
-        
-    def _configure_quantum_interface(self):
+        self.engine = PharmaResearchEngine()
+        self.clinical_intel = ClinicalIntelligence()
+        self.ai_innovator = AIDrugInnovator()
+        self._configure_page()
+
+    def _configure_page(self):
         st.set_page_config(
-            page_title="QuantumPharm X",
+            page_title="PRIS - Next-Generation Pharmaceutical Research Suite",
             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>
+        <style>
+            .main {background-color: #f0f2f6; padding: 20px;}
+            .stAlert {padding: 20px; font-size: 1.1em;}
+            .reportview-container .markdown-text-container {font-family: 'Helvetica Neue', Arial, sans-serif;}
+        </style>
         """, unsafe_allow_html=True)
 
     def render(self):
-        st.title("🧬 QuantumPharm X - Post-Moore Drug Discovery")
-        self._build_quantum_dashboard()
+        st.title("Next-Generation Pharmaceutical Research Intelligence Suite")
+        self._render_navigation()
 
-    def _build_quantum_dashboard(self):
+    def _render_navigation(self):
         tabs = st.tabs([
-            "🌌 Quantum Protein Design",
-            "🧫 Synthetic Biology Lab",
-            "🫀 Digital Twin Clinic",
-            "⚛️ Quantum Chemistry",
-            "🔬 Federated Research"
+            "🚀 Drug Innovation",
+            "📈 Trial Analytics",
+            "🧪 Compound Profiler",
+            "📜 Regulatory Hub",
+            "🤖 AI Strategist"
         ])
-        
-        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 tabs[0]:
+            self._drug_innovation()
+        with tabs[1]:
+            self._trial_analytics()
+        with tabs[2]:
+            self._compound_profiler()
+        with tabs[3]:
+            self._regulatory_hub()
+        with tabs[4]:
+            self._ai_strategist()
+
+    def _drug_innovation(self):
+        st.header("AI-Powered Drug Innovation Engine")
+        col1, col2 = st.columns([1, 3])
         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)
+            target = st.text_input("Target Pathobiology:", placeholder="e.g., EGFR mutant NSCLC")
+            paradigm = st.selectbox("Development Paradigm:", ["First-in-class", "Fast-follower", "Biologic", "ADC", "Gene Therapy"])
+            if st.button("Generate Development Blueprint"):
+                with st.spinner("Formulating strategic plan..."):
+                    blueprint = self.ai_innovator.generate_strategy(target, paradigm)
+                    st.markdown(blueprint, unsafe_allow_html=True)
+
+    def _trial_analytics(self):
+        st.header("Clinical Trial Landscape Analysis")
+        query = st.text_input("Search Clinical Trials:", placeholder="Enter condition, intervention, or NCT number")
+        if st.button("Analyze Trial Landscape"):
+            with st.spinner("Fetching trial data..."):
+                trials = self.clinical_intel.get_trial_landscape(query)
+                if trials:
+                    st.subheader("Top Clinical Trials")
+                    trial_data = []
+                    for study in trials:
+                        title = study.get("protocolSection", {}).get("identificationModule", {}).get("briefTitle", "N/A")
+                        status = study.get("protocolSection", {}).get("statusModule", {}).get("overallStatus", "N/A")
+                        phase = study.get("protocolSection", {}).get("designModule", {}).get("phases", ["N/A"])[0]
+                        enrollment = study.get("protocolSection", {}).get("designModule", {}).get("enrollmentInfo", {}).get("count", "N/A")
+                        trial_data.append({
+                            "Title": title,
+                            "Status": status,
+                            "Phase": phase,
+                            "Enrollment": enrollment
+                        })
+                    df = pd.DataFrame(trial_data)
+                    st.dataframe(df)
+                    st.subheader("Trial Phase Distribution")
+                    phase_counts = df["Phase"].value_counts()
+                    fig, ax = plt.subplots()
+                    sns.barplot(x=phase_counts.index, y=phase_counts.values, ax=ax)
+                    ax.set_xlabel("Trial Phase")
+                    ax.set_ylabel("Number of Trials")
+                    st.pyplot(fig)
+                else:
+                    st.warning("No clinical trials found for the provided query.")
+
+    def _compound_profiler(self):
+        st.header("Advanced Multi-Omics Compound Profiler")
+        compound = st.text_input("Analyze Compound:", placeholder="Enter drug name or SMILES (e.g., Aspirin)")
+        if st.button("Profile Compound"):
+            with st.spinner("Decoding molecular profile..."):
+                profile = self.engine.get_compound_profile(compound)
+                if profile:
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        st.subheader("Structural Insights")
+                        smiles = profile.get('canonical_smiles', '')
+                        mol = Chem.MolFromSmiles(smiles) if smiles and smiles != "N/A" else None
+                        if mol:
+                            img = Draw.MolToImage(mol, size=(400, 300))
+                            st.image(img, caption="2D Molecular Structure")
+                        else:
+                            st.error("Could not generate molecular structure image. Verify the SMILES string or compound name.")
+                    with col2:
+                        st.subheader("Physicochemical Profile")
+                        st.metric("Molecular Weight", profile.get('molecular_weight', "N/A"))
+                        st.metric("LogP", profile.get('logp', "N/A"))
+                        st.metric("IUPAC Name", profile.get('iupac_name', "N/A"))
+                        st.code(f"SMILES: {profile.get('canonical_smiles', 'N/A')}")
+                else:
+                    st.warning("Compound profiling failed. Please ensure you have entered a valid chemical compound.")
+
+    def _regulatory_hub(self):
+        st.header("Regulatory Intelligence Hub")
+        st.write("Gain insights into FDA approvals and regulatory pathways using local sample data.")
+        drug_name = st.text_input("Enter Drug Name for Regulatory Analysis:", placeholder="e.g., Aspirin")
+        if st.button("Fetch Regulatory Data"):
+            sample_regulatory_data = {
+                "drug_name": drug_name,
+                "approval_status": "Approved",
+                "approval_date": "1985-07-01",
+                "label": "Sample regulatory label information."
             }
-        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)
+            st.subheader("FDA Approval Details")
+            st.json(sample_regulatory_data)
+
+    def _ai_strategist(self):
+        st.header("AI Drug Development Strategist")
+        st.write("Leverage our simulated GPT-4 engine to generate cutting-edge drug development strategies.")
+        target = st.text_input("Enter Target Disease or Pathway:", placeholder="e.g., KRAS G12C mutation")
+        if st.button("Generate AI Strategy"):
+            with st.spinner("Generating AI-driven strategy..."):
+                strategy = self.ai_innovator.generate_strategy(target, "First-in-class")
+                st.markdown(strategy, unsafe_allow_html=True)
 
 # -----------------------------
 # MAIN EXECUTION
 # -----------------------------
 if __name__ == "__main__":
-    qpx = QuantumPharmX()
-    qpx.render()
+    interface = PharmaResearchInterface()
+    interface.render()