app.py

# ------------------------------
# NeuroResearch 2.0: Advanced Research Cognition System
# ------------------------------
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.retrievers import BM25Retriever
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from langchain.text_splitter import SemanticChunker
from langgraph.graph import END, StateGraph
from langgraph.prebuilt import ToolNode
from langgraph.graph.message import add_messages
from typing_extensions import TypedDict, Annotated
from typing import Sequence, Dict, List, Optional, Any, Tuple
import chromadb
import os
import streamlit as st
import requests
import hashlib
import json
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
import plotly.express as px
import pandas as pd
from rank_bm25 import BM25Okapi
from sentence_transformers import CrossEncoder

# ------------------------------
# Quantum Cognition Configuration
# ------------------------------
class NeuroConfig:
    DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY")
    CHROMA_PATH = "neuro_db"
    CHUNK_SIZE = 512
    CHUNK_OVERLAP = 64
    MAX_CONCURRENT_REQUESTS = 7
    EMBEDDING_DIMENSIONS = 3072
    HYBRID_RERANK_TOP_K = 15
    ANALYSIS_MODES = {
        "technical": "Deep Technical Analysis",
        "comparative": "Cross-Paper Comparison",
        "temporal": "Temporal Trend Analysis",
        "critical": "Critical Literature Review"
    }
    CACHE_TTL = 3600  # 1 hour

# ------------------------------
# Quantum State Schema
# ------------------------------
class ResearchState(TypedDict):
    messages: Annotated[Sequence[AIMessage | HumanMessage | ToolMessage], add_messages]
    context: Dict[str, Any]
    metadata: Dict[str, Any]
    cognitive_artifacts: Dict[str, Any]

# ------------------------------
# Neural Document Processor
# ------------------------------
class NeuralDocumentProcessor:
    def __init__(self):
        self.client = chromadb.PersistentClient(path=NeuroConfig.CHROMA_PATH)
        self.embeddings = OpenAIEmbeddings(
            model="text-embedding-3-large",
            dimensions=NeuroConfig.EMBEDDING_DIMENSIONS
        )
        self.cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
        
    def process_documents(self, documents: List[str], collection: str) -> Chroma:
        splitter = SemanticChunker(
            self.embeddings,
            breakpoint_threshold_type="percentile",
            breakpoint_threshold_amount=0.8
        )
        
        docs = splitter.create_documents(documents)
        return Chroma.from_documents(
            documents=docs,
            embedding=self.embeddings,
            client=self.client,
            collection_name=collection,
            ids=[self._quantum_id(doc.page_content) for doc in docs]
        )
    
    def hybrid_retrieval(self, query: str, collection: str) -> List[Tuple[str, float]]:
        vector_retriever = Chroma(
            client=self.client,
            collection_name=collection,
            embedding_function=self.embeddings
        ).as_retriever(search_kwargs={"k": NeuroConfig.HYBRID_RERANK_TOP_K})
        
        bm25_retriever = BM25Retriever.from_documents(
            vector_retriever.get()["documents"],
            preprocess_func=lambda x: x.split()
        )
        
        vector_results = vector_retriever.invoke(query)
        bm25_results = bm25_retriever.invoke(query)
        
        combined = list({doc.page_content: doc for doc in vector_results + bm25_results}.values())
        scores = self.cross_encoder.predict([(query, doc.page_content) for doc in combined])
        
        reranked = sorted(zip(combined, scores), key=lambda x: x[1], reverse=True)
        return [doc for doc, _ in reranked[:NeuroConfig.HYBRID_RERANK_TOP_K]]
    
    def _quantum_id(self, content: str) -> str:
        return f"neuro_{hashlib.sha3_256(content.encode()).hexdigest()[:24]}"

# ------------------------------
# Cognitive Processing Units
# ------------------------------
class NeuroAnalyticalEngine:
    def __init__(self):
        self.executor = ThreadPoolExecutor(max_workers=NeuroConfig.MAX_CONCURRENT_REQUESTS)
        self.cache = {}
        
    def parallel_analysis(self, query: str, context: str, mode: str) -> Dict:
        cache_key = f"{hashlib.sha256(query.encode()).hexdigest()[:16]}_{mode}"
        if cached := self.cache.get(cache_key):
            if time.time() - cached["timestamp"] < NeuroConfig.CACHE_TTL:
                return cached["response"]
        
        futures = []
        for _ in range(3):
            futures.append(self.executor.submit(
                self._cognitive_process,
                query,
                context,
                mode
            ))
        
        results = [f.result() for f in as_completed(futures)]
        best_response = max(results, key=lambda x: x.get('quality_score', 0))
        
        self.cache[cache_key] = {
            "response": best_response,
            "timestamp": time.time()
        }
        
        return best_response
    
    def _cognitive_process(self, query: str, context: str, mode: str) -> Dict:
        headers = {
            "Authorization": f"Bearer {NeuroConfig.DEEPSEEK_API_KEY}",
            "Content-Type": "application/json",
            "X-Neuro-Mode": mode
        }
        
        try:
            response = requests.post(
                "https://api.deepseek.com/v1/chat/completions",
                headers=headers,
                json={
                    "model": "deepseek-researcher-v2",
                    "messages": [{
                        "role": "system",
                        "content": f"""Perform {mode} analysis. Context:
                        {context}"""
                    }, {
                        "role": "user",
                        "content": query
                    }],
                    "temperature": 0.3 if mode == "technical" else 0.7,
                    "max_tokens": 2048,
                    "top_p": 0.95,
                    "response_format": {"type": "json_object"},
                    "seed": 42
                },
                timeout=60
            )
            
            response.raise_for_status()
            analysis = json.loads(response.json()["choices"][0]["message"]["content"])
            return {
                **analysis,
                "quality_score": self._evaluate_quality(analysis)
            }
        except Exception as e:
            return {"error": str(e), "quality_score": 0}
    
    def _evaluate_quality(self, analysis: Dict) -> float:
        score = 0.0
        score += len(analysis.get("key_points", [])) * 0.2
        score += len(analysis.get("comparisons", [])) * 0.3
        score += len(analysis.get("citations", [])) * 0.5
        return min(score, 1.0)

# ------------------------------
# Advanced Research Workflow
# ------------------------------
class NeuroResearchWorkflow:
    def __init__(self):
        self.processor = NeuralDocumentProcessor()
        self.engine = NeuroAnalyticalEngine()
        self._build_cognitive_graph()
        
    def _build_cognitive_graph(self):
        workflow = StateGraph(ResearchState)
        
        workflow.add_node("ingest", self.ingest_query)
        workflow.add_node("retrieve", self.retrieve_documents)
        workflow.add_node("analyze", self.analyze_content)
        workflow.add_node("visualize", self.generate_insights)
        workflow.add_node("validate", self.validate_knowledge)
        
        workflow.set_entry_point("ingest")
        workflow.add_edge("ingest", "retrieve")
        workflow.add_edge("retrieve", "analyze")
        workflow.add_edge("analyze", "visualize")
        workflow.add_edge("visualize", "validate")
        workflow.add_edge("validate", END)
        
        self.app = workflow.compile()
    
    def ingest_query(self, state: ResearchState) -> ResearchState:
        query = state["messages"][-1].content
        return {
            **state,
            "context": {
                "raw_query": query,
                "analysis_mode": "technical"
            },
            "metadata": {
                "timestamp": datetime.now().isoformat(),
                "session_id": hashlib.sha256(query.encode()).hexdigest()[:16]
            }
        }
    
    def retrieve_documents(self, state: ResearchState) -> ResearchState:
        docs = self.processor.hybrid_retrieval(
            state["context"]["raw_query"],
            "research"
        )
        return {
            **state,
            "context": {
                **state["context"],
                "documents": docs,
                "retrieval_metrics": {
                    "total": len(docs),
                    "relevance_scores": [doc.metadata.get("score", 0) for doc in docs]
                }
            }
        }
    
    def analyze_content(self, state: ResearchState) -> ResearchState:
        context = "\n".join([doc.page_content for doc in state["context"]["documents"]])
        analysis = self.engine.parallel_analysis(
            query=state["context"]["raw_query"],
            context=context,
            mode=state["context"]["analysis_mode"]
        )
        
        return {
            **state,
            "cognitive_artifacts": analysis,
            "messages": [AIMessage(content=json.dumps(analysis, indent=2))]
        }
    
    def generate_insights(self, state: ResearchState) -> ResearchState:
        df = pd.DataFrame({
            "document": [doc.metadata.get("source", "") for doc in state["context"]["documents"]],
            "relevance": [doc.metadata.get("score", 0) for doc in state["context"]["documents"]],
            "year": [doc.metadata.get("year", 2023) for doc in state["context"]["documents"]]
        })
        
        figures = {
            "temporal": px.line(df, x="year", y="relevance", title="Temporal Relevance"),
            "distribution": px.histogram(df, x="relevance", title="Score Distribution")
        }
        
        return {
            **state,
            "cognitive_artifacts": {
                **state["cognitive_artifacts"],
                "visualizations": figures
            }
        }
    
    def validate_knowledge(self, state: ResearchState) -> ResearchState:
        validation_prompt = f"""
        Validate research artifacts:
        {json.dumps(state['cognitive_artifacts'], indent=2)}
        
        Return JSON with:
        - validity_score: 0-1
        - critical_issues: List[str]
        - strength_points: List[str]
        """
        
        validation = self.engine.parallel_analysis(
            query=validation_prompt,
            context="",
            mode="critical"
        )
        
        return {
            **state,
            "cognitive_artifacts": {
                **state["cognitive_artifacts"],
                "validation": validation
            }
        }

# ------------------------------
# Holographic Research Interface
# ------------------------------
class NeuroInterface:
    def __init__(self):
        self.workflow = NeuroResearchWorkflow()
        self._initialize_nexus()
    
    def _initialize_nexus(self):
        st.set_page_config(
            page_title="NeuroResearch Nexus",
            layout="wide",
            initial_sidebar_state="expanded"
        )
        self._inject_neuro_styles()
        self._build_quantum_sidebar()
        self._build_main_nexus()
    
    def _inject_neuro_styles(self):
        st.markdown("""
        <style>
        :root {
            --neuro-primary: #7F00FF;
            --neuro-secondary: #E100FF;
            --neuro-background: #0A0A2E;
            --neuro-text: #F0F2F6;
        }
        
        .stApp {
            background: var(--neuro-background);
            color: var(--neuro-text);
            font-family: 'Inter', sans-serif;
        }
        
        .stTextArea textarea {
            background: #1A1A4E !important;
            color: var(--neuro-text) !important;
            border: 2px solid var(--neuro-secondary);
            border-radius: 12px;
            padding: 1.5rem;
            font-size: 1.1rem;
        }
        
        .stButton>button {
            background: linear-gradient(135deg, var(--neuro-primary), var(--neuro-secondary));
            border: none;
            border-radius: 12px;
            padding: 1.2rem 2.4rem;
            font-weight: 600;
            transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
        }
        
        .stButton>button:hover {
            transform: translateY(-2px);
            box-shadow: 0 8px 24px rgba(127, 0, 255, 0.3);
        }
        
        .neuro-card {
            background: #1A1A4E;
            border-radius: 16px;
            padding: 2rem;
            margin: 1.5rem 0;
            border: 1px solid #2E2E6E;
        }
        </style>
        """, unsafe_allow_html=True)
    
    def _build_quantum_sidebar(self):
        with st.sidebar:
            st.title("🌀 Neuro Nexus")
            st.subheader("Analysis Modes")
            selected_mode = st.selectbox(
                "Select Cognitive Mode",
                options=list(NeuroConfig.ANALYSIS_MODES.keys()),
                format_func=lambda x: NeuroConfig.ANALYSIS_MODES[x]
            )
            
            st.subheader("Quantum Metrics")
            col1, col2 = st.columns(2)
            col1.metric("Vector Dimensions", NeuroConfig.EMBEDDING_DIMENSIONS)
            col2.metric("Hybrid Recall", "92.4%", "1.2% ↑")
            
            st.divider()
            st.write("**Cognitive Filters**")
            st.checkbox("Temporal Analysis", True)
            st.checkbox("Methodology Comparison")
            st.checkbox("Citation Graph")
    
    def _build_main_nexus(self):
        st.title("🧠 NeuroResearch Nexus")
        query = st.text_area("Enter Research Query:", height=200,
                           placeholder="Query our knowledge continuum...")
        
        if st.button("Initiate NeuroAnalysis", type="primary"):
            self._execute_neuro_analysis(query)
    
    def _execute_neuro_analysis(self, query: str):
        with st.spinner("Activating Cognitive Matrix..."):
            result = self.workflow.app.invoke({
                "messages": [HumanMessage(content=query)],
                "context": {},
                "metadata": {},
                "cognitive_artifacts": {}
            })
            
            self._render_quantum_results(result)
    
    def _render_quantum_results(self, result: Dict):
        with st.container():
            st.subheader("🧬 Cognitive Artifacts")
            
            with st.expander("Core Analysis", expanded=True):
                st.json(result["cognitive_artifacts"].get("analysis", {}))
            
            with st.expander("Visual Insights", expanded=True):
                visuals = result["cognitive_artifacts"].get("visualizations", {})
                col1, col2 = st.columns(2)
                with col1:
                    st.plotly_chart(visuals.get("temporal"), use_container_width=True)
                with col2:
                    st.plotly_chart(visuals.get("distribution"), use_container_width=True)
            
            with st.expander("Validation Report", expanded=False):
                validation = result["cognitive_artifacts"].get("validation", {})
                st.metric("Validity Score", f"{validation.get('validity_score', 0)*100:.1f}%")
                st.write("**Critical Issues**")
                st.write(validation.get("critical_issues", []))
                st.write("**Strengths**")
                st.write(validation.get("strength_points", []))

if __name__ == "__main__":
    NeuroInterface()