app.py

# ------------------------------
# Imports & Dependencies
# ------------------------------
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.messages import HumanMessage, AIMessage
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
from langgraph.graph import END, StateGraph
from typing_extensions import TypedDict, Annotated
from typing import Sequence, Dict, List, Optional, Any
import chromadb
import numpy as np
import os
import streamlit as st
import requests
import hashlib
import re
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
from sklearn.metrics.pairwise import cosine_similarity

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

# ------------------------------
# Configuration
# ------------------------------
class ResearchConfig:
    DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY")
    CHROMA_PATH = "chroma_db"
    CHUNK_SIZE = 512
    CHUNK_OVERLAP = 64
    MAX_CONCURRENT_REQUESTS = 5
    EMBEDDING_DIMENSIONS = 1536
    RESEARCH_EMBEDDING = np.random.randn(1536)
    
    DOCUMENT_MAP = {
        "CV-Transformer Hybrid Architecture": {
            "title": "Research Report: CV-Transformer Model (98% Accuracy)",
            "content": """
            Hybrid architecture combining CNNs and Transformers achieves 98% image recognition accuracy.
            Key equation: $f(x) = \text{Attention}(\text{CNN}(x))$
            Validation on ImageNet-1k: Top-1 Accuracy 98.2%, Inference Speed 42ms/img
            """
        },
        "Transformer Architecture Analysis": {
            "title": "Academic Paper: Transformers in NLP",
            "content": """
            Self-attention mechanism remains core innovation: 
            $\text{Attention}(Q, K, V) = \text{softmax}(\frac{QK^T}{\sqrt{d_k}})V$
            GLUE Benchmark Score: 92.4%, Training Efficiency: 1.8x vs RNNs
            """
        },
        "Quantum ML Frontiers": {
            "title": "Quantum Machine Learning Review",
            "content": """
            Quantum gradient descent enables faster optimization:
            $\theta_{t+1} = \theta_t - \eta \nabla_\theta \mathcal{L}(\theta_t)$
            100x speedup on optimization tasks, 58% energy reduction
            """
        }
    }
    
    ANALYSIS_TEMPLATE = """Analyze these technical documents:
{context}

Respond in MARKDOWN with:
1. **Key Technical Contributions** (bullet points with equations)
2. **Novel Methodologies** (algorithms with math notation)
3. **Empirical Results** (comparative metrics)
4. **Applications** (domain-specific implementations)
5. **Limitations** (theoretical/practical boundaries)

Include LaTeX equations where applicable."""

if not ResearchConfig.DEEPSEEK_API_KEY:
    st.error("""**Configuration Required**  
    1. Get DeepSeek API key: [platform.deepseek.com](https://platform.deepseek.com/)  
    2. Set secret: `DEEPSEEK_API_KEY`  
    3. Rebuild deployment""")
    st.stop()

# ------------------------------
# Document Processing System
# ------------------------------
class QuantumDocumentManager:
    def __init__(self):
        self.client = chromadb.PersistentClient(path=ResearchConfig.CHROMA_PATH)
        self.embeddings = OpenAIEmbeddings(
            model="text-embedding-3-large",
            dimensions=ResearchConfig.EMBEDDING_DIMENSIONS
        )
        
    def create_collection(self, document_map: Dict[str, Dict[str, str]], collection_name: str) -> Chroma:
        splitter = RecursiveCharacterTextSplitter(
            chunk_size=ResearchConfig.CHUNK_SIZE,
            chunk_overlap=ResearchConfig.CHUNK_OVERLAP,
            separators=["\n\n", "\n", "|||"]
        )
        
        docs = []
        for key, data in document_map.items():
            chunks = splitter.split_text(data["content"])
            for chunk in chunks:
                docs.append(Document(
                    page_content=chunk,
                    metadata={
                        "title": data["title"],
                        "source": collection_name,
                        "hash": hashlib.sha256(chunk.encode()).hexdigest()[:16]
                    }
                ))
        
        return Chroma.from_documents(
            documents=docs,
            embedding=self.embeddings,
            collection_name=collection_name,
            ids=[self._document_id(doc.page_content) for doc in docs]
        )
    
    def _document_id(self, content: str) -> str:
        return f"{hashlib.sha256(content.encode()).hexdigest()[:16]}-{int(time.time())}"

# Initialize document system
qdm = QuantumDocumentManager()
research_docs = qdm.create_collection(ResearchConfig.DOCUMENT_MAP, "research")

# ------------------------------
# Intelligent Retrieval System
# ------------------------------
class ResearchRetriever:
    def __init__(self):
        self.retriever = research_docs.as_retriever(
            search_type="mmr",
            search_kwargs={
                'k': 4,
                'fetch_k': 20,
                'lambda_mult': 0.85
            }
        )
    
    def retrieve(self, query: str) -> List[Document]:
        try:
            docs = self.retriever.invoke(query)
            if not docs:
                raise ValueError("No relevant documents found")
            return docs
        except Exception as e:
            st.error(f"Retrieval Error: {str(e)}")
            return []

# ------------------------------
# Robust Processing Core
# ------------------------------
class CognitiveProcessor:
    def __init__(self):
        self.executor = ThreadPoolExecutor(max_workers=ResearchConfig.MAX_CONCURRENT_REQUESTS)
    
    def process_query(self, prompt: str) -> Dict:
        futures = [self.executor.submit(self._api_request, prompt) for _ in range(3)]
        return self._best_result([f.result() for f in as_completed(futures)])

    def _api_request(self, prompt: str) -> Dict:
        headers = {
            "Authorization": f"Bearer {ResearchConfig.DEEPSEEK_API_KEY}",
            "Content-Type": "application/json"
        }
        
        try:
            response = requests.post(
                "https://api.deepseek.com/v1/chat/completions",
                headers=headers,
                json={
                    "model": "deepseek-chat",
                    "messages": [{
                        "role": "user",
                        "content": f"Respond as Senior AI Researcher:\n{prompt}"
                    }],
                    "temperature": 0.7,
                    "max_tokens": 1500,
                    "top_p": 0.9
                },
                timeout=45
            )
            response.raise_for_status()
            return response.json()
        except Exception as e:
            return {"error": str(e)}
    
    def _best_result(self, results: List[Dict]) -> Dict:
        valid = [r for r in results if "error" not in r]
        if not valid:
            return {"error": "All API requests failed"}
        
        # Select response with most technical content
        contents = [r.get('choices', [{}])[0].get('message', {}).get('content', '') for r in valid]
        tech_scores = [len(re.findall(r"\$.*?\$", c)) for c in contents]
        return valid[np.argmax(tech_scores)]

# ------------------------------
# Validation Workflow Engine
# ------------------------------
class ResearchWorkflow:
    def __init__(self):
        self.retriever = ResearchRetriever()
        self.processor = CognitiveProcessor()
        self.workflow = StateGraph(AgentState)
        self._build_workflow()

    def _build_workflow(self):
        self.workflow.add_node("ingest", self.ingest_query)
        self.workflow.add_node("retrieve", self.retrieve_documents)
        self.workflow.add_node("analyze", self.analyze_content)
        self.workflow.add_node("validate", self.validate_output)
        self.workflow.add_node("refine", self.refine_results)

        self.workflow.set_entry_point("ingest")
        self.workflow.add_edge("ingest", "retrieve")
        self.workflow.add_edge("retrieve", "analyze")
        self.workflow.add_conditional_edges(
            "analyze",
            self._quality_check,
            {"valid": "validate", "invalid": "refine"}
        )
        self.workflow.add_edge("validate", END)
        self.workflow.add_edge("refine", "retrieve")

        self.app = self.workflow.compile()

    def ingest_query(self, state: AgentState) -> Dict:
        try:
            query = state["messages"][-1].content
            return {
                "messages": [AIMessage(content="Query ingested successfully")],
                "context": {"raw_query": query},
                "metadata": {"timestamp": datetime.now().isoformat()}
            }
        except Exception as e:
            return self._error_state(f"Ingestion Error: {str(e)}")

    def retrieve_documents(self, state: AgentState) -> Dict:
        try:
            docs = self.retriever.retrieve(state["context"]["raw_query"])
            if not docs:
                return self._error_state("Document correlation failure - no relevant papers found")
            return {
                "messages": [AIMessage(content=f"Retrieved {len(docs)} documents")],
                "context": {"documents": docs}
            }
        except Exception as e:
            return self._error_state(f"Retrieval Error: {str(e)}")

    def analyze_content(self, state: AgentState) -> Dict:
        try:
            docs = state["context"]["documents"]
            context = "\n\n".join([f"### {doc.metadata['title']}\n{doc.page_content}" for doc in docs])
            prompt = ResearchConfig.ANALYSIS_TEMPLATE.format(context=context)
            response = self.processor.process_query(prompt)
            
            if "error" in response:
                raise RuntimeError(response["error"])
            
            analysis = response['choices'][0]['message']['content']
            self._validate_analysis_structure(analysis)
            
            return {
                "messages": [AIMessage(content=analysis)],
                "context": {"analysis": analysis}
            }
        except Exception as e:
            return self._error_state(f"Analysis Error: {str(e)}")

    def validate_output(self, state: AgentState) -> Dict:
        validation_prompt = f"""Validate this technical analysis:
{state["messages"][-1].content}

Check for:
1. Mathematical accuracy
2. Empirical evidence
3. Technical depth
4. Logical consistency

Respond with 'VALID' or 'INVALID'"""
        
        response = self.processor.process_query(validation_prompt)
        content = response.get('choices', [{}])[0].get('message', {}).get('content', '')
        return {
            "messages": [AIMessage(content=f"{state['messages'][-1].content}\n\n## Validation\n{content}")],
            "context": {"valid": "VALID" in content}
        }

    def refine_results(self, state: AgentState) -> Dict:
        refinement_prompt = f"""Improve this analysis:
{state["messages"][-1].content}

Focus on:
1. Enhancing mathematical rigor
2. Adding empirical references
3. Strengthening technical arguments"""
        
        response = self.processor.process_query(refinement_prompt)
        return {
            "messages": [AIMessage(content=response['choices'][0]['message']['content'])],
            "context": state["context"]
        }

    def _quality_check(self, state: AgentState) -> str:
        return "valid" if state.get("context", {}).get("valid", False) else "invalid"

    def _validate_analysis_structure(self, content: str):
        required_sections = [
            "Key Technical Contributions",
            "Novel Methodologies",
            "Empirical Results",
            "Applications",
            "Limitations"
        ]
        missing = [s for s in required_sections if f"## {s}" not in content]
        if missing:
            raise ValueError(f"Missing critical sections: {', '.join(missing)}")
            
        if not re.search(r"\$.*?\$", content):
            raise ValueError("Analysis lacks required mathematical notation")

    def _error_state(self, message: str) -> Dict:
        return {
            "messages": [AIMessage(content=f"❌ {message}")],
            "context": {"error": True},
            "metadata": {"status": "error"}
        }

# ------------------------------
# Research Interface
# ------------------------------
class ResearchInterface:
    def __init__(self):
        self.workflow = ResearchWorkflow()
        self._initialize_interface()

    def _initialize_interface(self):
        st.set_page_config(
            page_title="NeuroResearch AI",
            layout="wide",
            initial_sidebar_state="expanded"
        )
        self._inject_styles()
        self._build_sidebar()
        self._build_main_interface()

    def _inject_styles(self):
        st.markdown("""
        <style>
        :root {
            --primary: #2ecc71;
            --secondary: #3498db;
            --background: #0a0a0a;
            --text: #ecf0f1;
        }
        
        .stApp {
            background: var(--background);
            color: var(--text);
            font-family: 'Roboto', sans-serif;
        }
        
        .stTextArea textarea {
            background: #1a1a1a !important;
            color: var(--text) !important;
            border: 2px solid var(--secondary);
            border-radius: 8px;
            padding: 1rem;
        }
        
        .stButton>button {
            background: linear-gradient(135deg, var(--primary), var(--secondary));
            border: none;
            border-radius: 8px;
            padding: 1rem 2rem;
            transition: all 0.3s;
        }
        
        .stButton>button:hover {
            transform: translateY(-2px);
            box-shadow: 0 4px 12px rgba(46, 204, 113, 0.3);
        }
        
        .stExpander {
            background: #1a1a1a;
            border: 1px solid #2a2a2a;
            border-radius: 8px;
            margin: 1rem 0;
        }
        
        code {
            color: #2ecc71;
            background: #002200;
            padding: 2px 4px;
            border-radius: 4px;
        }
        </style>
        """, unsafe_allow_html=True)

    def _build_sidebar(self):
        with st.sidebar:
            st.title("🔍 Research Database")
            for key, data in ResearchConfig.DOCUMENT_MAP.items():
                with st.expander(data["title"]):
                    st.markdown(f"```\n{data['content']}\n```")
            st.metric("Embedding Dimensions", ResearchConfig.EMBEDDING_DIMENSIONS)
            st.metric("Document Chunks", len(research_docs.get()['ids']))

    def _build_main_interface(self):
        st.title("🧠 NeuroResearch AI")
        query = st.text_area("Research Query:", height=200,
                           placeholder="Enter technical research question...")
        
        if st.button("Execute Analysis", type="primary"):
            self._execute_analysis(query)

    def _execute_analysis(self, query: str):
        try:
            with st.spinner("Performing deep technical analysis..."):
                result = self.workflow.app.invoke(
                    {"messages": [HumanMessage(content=query)]}
                )
                
                if result.get("context", {}).get("error"):
                    self._show_error(result["context"].get("error", "Unknown error"))
                else:
                    self._display_results(result)
        except Exception as e:
            self._show_error(str(e))

    def _display_results(self, result):
        content = result["messages"][-1].content
        with st.expander("Technical Analysis Report", expanded=True):
            st.markdown(content)
            
        with st.expander("Source Documents", expanded=False):
            for doc in result["context"].get("documents", []):
                st.markdown(f"**{doc.metadata['title']}**")
                st.code(doc.page_content, language='latex')

    def _show_error(self, message):
        st.error(f"""
        ⚠️ Analysis Failed: {message}
        
        Troubleshooting Steps:
        1. Check query specificity
        2. Verify document connections
        3. Ensure mathematical notation in sources
        4. Review API key validity
        5. Simplify complex query structures
        """)

if __name__ == "__main__":
    ResearchInterface()