Update app.py

app.py

@@ -3,15 +3,15 @@
 # -----------------------------
 import streamlit as st
 import requests
-from rdkit import Chem
-from rdkit.Chem import Draw
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 import logging
 import re
-from typing import Optional, Dict, List, Any
-from openai import OpenAI
+import time
+from rdkit import Chem
+from rdkit.Chem import Draw
+from bs4 import BeautifulSoup  # For future scraping implementations if needed
 
 # Configure advanced logging for full traceability and diagnostics
 logging.basicConfig(
@@ -22,109 +22,79 @@ logging.basicConfig(
 logger = logging.getLogger("PRIS")
 
 # -----------------------------
-# GLOBAL CONSTANTS
+# FALLBACK / SCRAPED DATA
 # -----------------------------
-API_ENDPOINTS = {
-    "clinical_trials": "https://clinicaltrials.gov/api/v2/studies",
-    "fda_drug_approval": "https://api.fda.gov/drug/label.json",
-    "pubchem": "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/{}/JSON",
-    # ... other endpoints omitted for brevity ...
+# Fallback data for compound profiling (e.g., 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"
+    }
 }
 
-DEFAULT_HEADERS = {
-    "User-Agent": "PharmaResearchIntelligenceSuite/1.0 (Professional Use)",
-    "Accept": "application/json"
+# Fallback clinical trial data (e.g., sample scraped data)
+FALLBACK_CLINICAL_DATA = {
+    "nct1": [
+        {
+            "Title": "A Study of Novel Therapeutics in Diabetes",
+            "Status": "Completed",
+            "Phase": "Phase II",
+            "Enrollment": "120"
+        }
+    ]
 }
 
-# -----------------------------
-# SECRETS MANAGEMENT
-# -----------------------------
-class APIConfigurationError(Exception):
-    """Custom exception for missing API configurations."""
-    pass
-
-try:
-    OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
-    BIOPORTAL_API_KEY = st.secrets["BIOPORTAL_API_KEY"]
-    PUB_EMAIL = st.secrets["PUB_EMAIL"]
-    OPENFDA_KEY = st.secrets["OPENFDA_KEY"]
-    
-    if not all([OPENAI_API_KEY, BIOPORTAL_API_KEY, PUB_EMAIL, OPENFDA_KEY]):
-        raise APIConfigurationError("One or more required API credentials are missing.")
-except (KeyError, APIConfigurationError) as e:
-    st.error(f"Critical configuration error: {str(e)}")
-    st.stop()
-
 # -----------------------------
 # CORE INFRASTRUCTURE
 # -----------------------------
 class PharmaResearchEngine:
-    """Core engine for integrating diverse pharmaceutical datasets and performing advanced analyses."""
+    """
+    Core engine for integrating pharmaceutical datasets using fallback/scraped data.
+    No external APIs are required; fallback data is used instead.
+    """
     
     def __init__(self):
-        self.openai_client = OpenAI(api_key=OPENAI_API_KEY)
-        
-    @staticmethod
-    def api_request(endpoint: str, 
-                    params: Optional[Dict] = None,
-                    headers: Optional[Dict] = None) -> Optional[Dict]:
+        # In a production environment, this is where an AI client would be initialized.
+        pass
+
+    def api_request(self, endpoint: str, params: dict = None, headers: dict = None) -> dict:
         """
-        Enterprise-grade API request handler with detailed error logging.
+        Dummy API request handler. In this implementation, live API calls are bypassed.
+        Instead, the method always returns an empty dictionary.
         """
-        try:
-            response = requests.get(
-                endpoint,
-                params=params,
-                headers={**DEFAULT_HEADERS, **(headers or {})},
-                timeout=(3.05, 15)
-            )
-            response.raise_for_status()  # Raises HTTPError for 4xx/5xx responses
-            return response.json()
-        except requests.exceptions.HTTPError as e:
-            logger.error(f"HTTP Error {e.response.status_code} for {endpoint} with params {params}")
-            st.error(f"API Error: {e.response.status_code} - {e.response.reason}")
-        except Exception as e:
-            logger.error(f"Network error for {endpoint}: {str(e)}")
-            st.error(f"Network error: {str(e)}")
-        return None
+        logger.info(f"Simulated API request to {endpoint} with params {params}")
+        # Simulate a network delay
+        time.sleep(0.5)
+        return {}  # Always empty because no API is actually used
 
-    def get_compound_profile(self, identifier: str) -> Optional[Dict]:
+    def get_compound_profile(self, identifier: str) -> dict:
         """
-        Retrieve comprehensive chemical profile data from PubChem for a given compound.
-        Accepts both common compound names and SMILES strings.
+        Retrieve a comprehensive compound profile.
+        This function first checks if the input is valid, then returns fallback data.
         """
         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 None
+            return {}
 
-        pubchem_url = API_ENDPOINTS["pubchem"].format(identifier)
-        pubchem_data = self.api_request(pubchem_url)
-        if not pubchem_data or not pubchem_data.get("PC_Compounds"):
-            logger.warning(f"No compound data returned for identifier: {identifier}")
+        # Attempt simulated API call (which in this case does nothing)
+        pubchem_data = self.api_request("https://pubchem.fakeendpoint/rest/pug/compound/name/{}/JSON".format(identifier))
+        
+        # If no data is returned from API simulation, use fallback scraped data
+        if identifier.lower() in FALLBACK_COMPOUND_DATA:
+            logger.info(f"Using fallback data for compound '{identifier}'.")
+            return FALLBACK_COMPOUND_DATA[identifier.lower()]
+        else:
+            logger.warning(f"No compound data found for '{identifier}'.")
             st.error("No compound data found. Please verify your input (e.g., check for typos or use a recognized compound name).")
-            return None
-            
-        compound = pubchem_data["PC_Compounds"][0]
-        return {
-            'molecular_formula': self._extract_property(compound, 'Molecular Formula'),
-            'iupac_name': self._extract_property(compound, 'IUPAC Name'),
-            'canonical_smiles': self._extract_property(compound, 'Canonical SMILES'),
-            'molecular_weight': self._extract_property(compound, 'Molecular Weight'),
-            'logp': self._extract_property(compound, 'LogP')
-        }
+            return {}
 
-    def _extract_property(self, compound: Dict, prop_name: str) -> str:
-        """Helper to extract a specific property from PubChem compound data."""
-        for prop in compound.get("props", []):
-            if prop.get("urn", {}).get("label") == prop_name:
-                return prop["value"].get("sval", "N/A")
-        return "N/A"
-
-    @staticmethod
-    def _is_valid_compound_input(user_input: str) -> bool:
+    def _is_valid_compound_input(self, user_input: str) -> bool:
         """
         Determines whether the user input is a valid chemical compound identifier.
         Accepts both conventional compound names and SMILES strings.
@@ -136,11 +106,11 @@ class PharmaResearchEngine:
         if any(term in input_lower for term in disease_terms):
             return False
         
-        # If the input contains characters common in SMILES (e.g., '=', '(', ')', '#'), treat as SMILES.
+        # Check for SMILES-specific characters (e.g., '=', '(', ')', '#')
         if re.search(r"[=\(\)#]", user_input):
             return True
         
-        # Otherwise, if input is alphanumeric with spaces or hyphens, assume it's a valid compound name.
+        # If input is alphanumeric (with spaces or hyphens), assume it's a valid compound name.
         if re.match(r'^[A-Za-z0-9\s\-]+$', user_input):
             return True
         
@@ -151,94 +121,79 @@ class PharmaResearchEngine:
 # -----------------------------
 class ClinicalIntelligence:
     """
-    Module for clinical trial and regulatory intelligence.
-    Provides deep insights into trial landscapes and FDA approval data.
+    Module for clinical trial and regulatory intelligence using fallback data.
     """
     
     def __init__(self):
         self.engine = PharmaResearchEngine()
     
-    def get_trial_landscape(self, query: str) -> List[Dict]:
-        params = {"query.term": query, "retmax": 10} if not query.startswith("NCT") else {"id": query}
-        trials = self.engine.api_request(API_ENDPOINTS["clinical_trials"], params=params)
-        if trials is None:
-            logger.error(f"Clinical trial API returned no data for query: {query}")
+    def get_trial_landscape(self, query: str) -> list:
+        """
+        Retrieve clinical trial information using fallback data.
+        If the query matches a known identifier, return fallback data.
+        """
+        # Simulate API call (which always fails in this offline mode)
+        trials = self.engine.api_request("https://clinicaltrials.fakeendpoint/api", params={"query": query})
+        
+        # Use fallback data if the query (e.g., "nct1") exists in our fallback dataset.
+        key = query.lower().strip()
+        if key in FALLBACK_CLINICAL_DATA:
+            logger.info(f"Using fallback clinical trial data for query '{query}'.")
+            return FALLBACK_CLINICAL_DATA[key]
+        else:
+            logger.error(f"No clinical trial data available for query '{query}'.")
             st.error("Failed to retrieve clinical trials. Please try a different query or check your network connection.")
             return []
-        return trials.get("studies", [])[:5]
-
-    def get_fda_approval(self, drug_name: str) -> Optional[Dict]:
-        if not OPENFDA_KEY:
-            st.error("OpenFDA API key not configured.")
-            return None
-        
-        params = {
-            "api_key": OPENFDA_KEY,
-            "search": f'openfda.brand_name:"{drug_name}"',
-            "limit": 1
-        }
-        data = self.engine.api_request(API_ENDPOINTS["fda_drug_approval"], params=params)
-        if data and data.get("results"):
-            return data["results"][0]
-        logger.warning(f"No FDA data found for drug: {drug_name}")
-        st.error("No FDA regulatory data found for the specified drug.")
-        return None
 
 class AIDrugInnovator:
     """
-    GPT-4 powered module for generating advanced, cutting-edge drug development strategies.
+    Simulated AI module for generating drug development strategies.
+    In this offline mode, a pre-defined strategy is returned.
     """
     
     def __init__(self):
         self.engine = PharmaResearchEngine()
         
     def generate_strategy(self, target: str, strategy: str) -> str:
-        prompt = f"""As the Chief Scientific Officer at a leading pharmaceutical company, please develop a {strategy} strategy for the target: {target}.
-        
-**Target Validation Approach**  
-- Perform an exhaustive literature review to understand the molecular basis of the disease.  
-- Validate targets using in vitro and in vivo models.  
-- Integrate genomic and proteomic data to identify actionable targets.  
-- Collaborate with top-tier academic and clinical institutions.
+        """
+        Return a robust, pre-defined strategic blueprint for drug development.
+        """
+        # Here, we simulate the GPT-4 response with a comprehensive text.
+        simulated_response = f"""
+# First-in-Class Strategy for {target.title()}
 
-**Lead Optimization Tactics**  
-- Conduct chemical optimization to improve potency, selectivity, and pharmacokinetic properties.  
-- Utilize high-throughput screening and biological assays for iterative lead refinement.  
-- Perform rigorous in vivo efficacy and safety evaluations.  
-- Secure intellectual property through comprehensive patent landscaping.
+## Target Validation Approach
+- **Literature Review:** Perform an exhaustive review to understand the molecular underpinnings of {target}.
+- **Preclinical Models:** Validate targets using state-of-the-art in vitro and in vivo models.
+- **Omics Integration:** Leverage genomic, proteomic, and metabolomic data to pinpoint actionable targets.
+- **Collaborative Research:** Engage with leading academic and clinical institutions for validation.
 
-**Clinical Trial Design**  
-- Initiate Phase I trials focused on safety and dosage determination.  
-- Scale to Phase II for efficacy and side-effect profiling in a broader patient cohort.  
-- Execute large-scale Phase III trials to validate clinical benefits against current standards of care.  
-- Plan for Phase IV post-marketing surveillance for long-term outcome assessment.  
-- Incorporate adaptive trial designs to enhance responsiveness and efficiency.
+## Lead Optimization Tactics
+- **Chemical Optimization:** Refine lead compounds to improve potency, selectivity, and pharmacokinetic profiles.
+- **High-Throughput Screening:** Utilize cutting-edge assays for iterative lead refinement.
+- **In Vivo Efficacy:** Conduct rigorous animal studies to assess efficacy and safety.
+- **Patent Strategy:** Secure intellectual property with comprehensive patent landscaping.
 
-**Regulatory Pathway Analysis**  
-- Engage in early pre-IND consultations with regulatory authorities.  
-- Prepare robust IND submissions incorporating comprehensive preclinical data.  
-- Maintain continuous dialogue with regulators throughout clinical development.  
-- Strategize for expedited review pathways (e.g., Fast Track, Breakthrough Therapy).
+## Clinical Trial Design
+- **Phase I:** Safety and dosage trials with a focus on pharmacodynamics.
+- **Phase II:** Efficacy and side-effect profiling in an expanded cohort.
+- **Phase III:** Large-scale trials comparing with the current standard of care.
+- **Phase IV:** Post-marketing surveillance for long-term outcomes.
+- **Adaptive Designs:** Employ adaptive and basket trial designs to optimize trial efficiency.
 
-**Commercial Potential Assessment**  
-- Conduct detailed market research to understand the competitive landscape and unmet needs.  
-- Segment patient populations to tailor therapeutic approaches.  
-- Devise dynamic pricing and reimbursement strategies aligned with payer requirements.  
-- Formulate a comprehensive go-to-market plan leveraging multi-channel marketing strategies.
+## Regulatory Pathway Analysis
+- **Pre-IND Consultation:** Early engagement with regulatory bodies to align on requirements.
+- **IND Preparation:** Develop robust IND submissions incorporating all preclinical data.
+- **Continuous Dialogue:** Maintain proactive communication with regulators throughout development.
+- **Expedited Programs:** Explore Fast Track and Breakthrough Therapy designations.
 
-Please format your response in Markdown with clear section headers."""
-        try:
-            response = self.engine.openai_client.chat.completions.create(
-                model="gpt-4",
-                messages=[{"role": "user", "content": prompt}],
-                temperature=0.7,
-                max_tokens=1500
-            )
-            return response.choices[0].message.content
-        except Exception as e:
-            logger.error(f"AI Strategy Generation Error: {str(e)}")
-            st.error("Failed to generate strategy. Please check the API configuration or try again later.")
-            return "Strategy generation failed due to an internal error."
+## Commercial Potential Assessment
+- **Market Research:** Conduct deep-dive analyses into market size, competition, and unmet needs.
+- **Patient Segmentation:** Identify target patient populations for tailored therapeutic approaches.
+- **Pricing & Reimbursement:** Develop dynamic pricing strategies and secure payer alignment.
+- **Go-To-Market:** Formulate a comprehensive multi-channel marketing strategy.
+"""
+        return simulated_response
 
 # -----------------------------
 # STREAMLIT INTERFACE
@@ -246,12 +201,13 @@ Please format your response in Markdown with clear section headers."""
 class PharmaResearchInterface:
     """
     Next-generation Streamlit interface for the Pharma Research Intelligence Suite.
-    Provides an integrated, intuitive dashboard for advanced pharmaceutical data analytics and AI-driven strategy generation.
+    Uses fallback and scraped data to simulate a fully operational research platform.
     """
     
     def __init__(self):
         self.clinical_intel = ClinicalIntelligence()
         self.ai_innovator = AIDrugInnovator()
+        self.engine = PharmaResearchEngine()
         self._configure_page()
 
     def _configure_page(self):
@@ -262,8 +218,8 @@ class PharmaResearchInterface:
         )
         st.markdown("""
             <style>
-            .main {background-color: #f0f2f6;}
-            .stAlert {padding: 20px;}
+            .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)
@@ -296,8 +252,7 @@ class PharmaResearchInterface:
         col1, col2 = st.columns([1, 3])
         with col1:
             target = st.text_input("Target Pathobiology:", placeholder="e.g., EGFR mutant NSCLC")
-            strategy = st.selectbox("Development Paradigm:", 
-                                     ["First-in-class", "Fast-follower", "Biologic", "ADC", "Gene Therapy"])
+            strategy = 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, strategy)
@@ -310,20 +265,8 @@ class PharmaResearchInterface:
             with st.spinner("Fetching trial data..."):
                 trials = self.clinical_intel.get_trial_landscape(trial_query)
                 if trials:
-                    st.subheader("Top 5 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.subheader("Top Clinical Trials")
+                    df = pd.DataFrame(trials)
                     st.dataframe(df)
                     st.subheader("Trial Phase Distribution")
                     phase_counts = df["Phase"].value_counts()
@@ -340,13 +283,13 @@ class PharmaResearchInterface:
         compound = st.text_input("Analyze Compound:", placeholder="Enter drug name or SMILES (e.g., Aspirin, CC(=O)OC1=CC=CC=C1C(=O)O)")
         if st.button("Profile Compound"):
             with st.spinner("Decoding molecular profile..."):
-                profile = PharmaResearchEngine().get_compound_profile(compound)
+                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 != "N/A" else None
+                        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")
@@ -363,20 +306,22 @@ class PharmaResearchInterface:
 
     def _regulatory_hub(self):
         st.header("Regulatory Intelligence Hub")
-        st.write("Gain insights into FDA approvals and regulatory pathways.")
+        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"):
-            with st.spinner("Retrieving regulatory information..."):
-                fda_data = self.clinical_intel.get_fda_approval(drug_name)
-                if fda_data:
-                    st.subheader("FDA Approval Details")
-                    st.json(fda_data)
-                else:
-                    st.warning("No FDA regulatory data found for the specified drug.")
+            # Since no API is available, we simulate regulatory data.
+            sample_regulatory_data = {
+                "drug_name": drug_name,
+                "approval_status": "Approved",
+                "approval_date": "1985-07-01",
+                "label": "Sample regulatory label information."
+            }
+            st.subheader("FDA Approval Details")
+            st.json(sample_regulatory_data)
 
     def _ai_strategist(self):
         st.header("AI Drug Development Strategist")
-        st.write("Leverage GPT-4 to generate cutting-edge drug development strategies.")
+        st.write("Leverage simulated GPT-4 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..."):