Update app.py

app.py

@@ -1,9 +1,9 @@
 """
-AI Research Assistant Supreme - Enterprise-Grade Solution
+AI Research Assistant
 """
 
 # ------------------------------
-# Imports & Infrastructure
+# Core Imports & Configuration
 # ------------------------------
 import os
 import re
@@ -11,319 +11,288 @@ import time
 import chromadb
 import requests
 import streamlit as st
-from typing import Sequence, Optional, Dict, Any
-from datetime import datetime
-from concurrent.futures import ThreadPoolExecutor
-from functools import lru_cache
-from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
+from typing import Sequence, Tuple
+from typing_extensions import TypedDict, Annotated
+from langchain_core.messages import HumanMessage, AIMessage
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import Chroma
 from langchain.tools.retriever import create_retriever_tool
 from langgraph.graph import END, StateGraph
 from langgraph.prebuilt import ToolNode
-from typing_extensions import TypedDict, Annotated
+from langgraph.graph.message import add_messages
 from chromadb.config import Settings
-import logging
-import hashlib
-from queue import Queue
+from langchain_openai import OpenAIEmbeddings
 
 # ------------------------------
-# Enterprise Configuration
+# Type Definitions
 # ------------------------------
-class Config:
-    DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY")
-    MAX_CONCURRENT_REQUESTS = 3
-    REQUEST_RATE_LIMIT = 5  # Requests per minute
-    CACHE_SIZE = 1000
-    SECURITY_SALT = os.environ.get("SECURITY_SALT", "default-secure-salt")
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[AIMessage | HumanMessage], add_messages]
 
 # ------------------------------
-# Advanced Security Framework
+# Configuration & Constants
 # ------------------------------
-class SecurityManager:
-    @staticmethod
-    def validate_api_key(key: str) -> bool:
-        if not key.startswith("sk-"):
-            return False
-        return len(key) in {32, 40, 64}  # Common API key lengths
-
-    @staticmethod
-    def generate_request_signature(payload: dict) -> str:
-        timestamp = str(int(time.time()))
-        data = timestamp + Config.SECURITY_SALT + str(payload)
-        return hashlib.sha256(data.encode()).hexdigest()
+class Config:
+    API_KEY = os.environ.get("DEEPSEEK_API_KEY")
+    CHROMA_PATH = "chroma_db"
+    TEXT_SPLITTER_CONFIG = {
+        "chunk_size": 512,
+        "chunk_overlap": 128,
+        "separators": ["\n\n", "\n", ". ", "! ", "? "]
+    }
 
 # ------------------------------
-# Quantum-Level Text Processing
+# Core System Components
 # ------------------------------
-class AdvancedTextProcessor:
+class ResearchAssistant:
     def __init__(self):
-        self.splitter = RecursiveCharacterTextSplitter(
-            chunk_size=512,
-            chunk_overlap=128,
-            separators=["\n\n", "\n", ". ", "! ", "? ", " ", ""],
-            length_function=len,
-            is_separator_regex=False
-        )
+        self.embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
+        self.vector_stores = self._init_vector_stores()
+        self.tools = self._create_tools()
+        self.workflow = self._build_workflow()
+
+    def _init_vector_stores(self) -> Tuple[Chroma, Chroma]:
+        """Initialize vector stores with proper document processing"""
+        splitter = RecursiveCharacterTextSplitter(**Config.TEXT_SPLITTER_CONFIG)
         
-    @lru_cache(maxsize=Config.CACHE_SIZE)
-    def process_documents(self, texts: tuple, collection_name: str) -> Chroma:
-        docs = self.splitter.create_documents(list(texts))
-        return Chroma.from_documents(
-            documents=docs,
-            embedding=OpenAIEmbeddings(model="text-embedding-3-large"),
-            client=chroma_client,
-            collection_name=collection_name,
-            collection_metadata={"hnsw:space": "cosine", "optimized": "true"}
-        )
+        research_docs = splitter.create_documents([
+            "Research Report: New AI Model Achieves 98% Image Recognition Accuracy",
+            "Transformers: The New NLP Architecture Standard",
+            "Quantum Machine Learning: Emerging Trends and Applications"
+        ])
+        
+        development_docs = splitter.create_documents([
+            "Project A: UI Design Finalized, API Integration Phase",
+            "Project B: Feature Testing and Bug Fixes",
+            "Product Y: Performance Optimization Pre-Release"
+        ])
 
-# ------------------------------
-# Neural Workflow Orchestration
-# ------------------------------
-class EnterpriseWorkflowEngine:
-    def __init__(self):
-        self.text_processor = AdvancedTextProcessor()
-        self._init_vector_stores()
-        self._init_tools()
-        self._build_graph()
+        client = chromadb.PersistentClient(
+            path=Config.CHROMA_PATH,
+            settings=Settings(anonymized_telemetry=False)
         
-    def _init_vector_stores(self):
-        self.research_vs = self.text_processor.process_documents(
-            tuple(research_texts), "research_collection"
+        return (
+            Chroma.from_documents(research_docs, self.embeddings, 
+                                client=client, collection_name="research"),
+            Chroma.from_documents(development_docs, self.embeddings,
+                                client=client, collection_name="development")
         )
-        self.development_vs = self.text_processor.process_documents(
-            tuple(development_texts), "development_collection"
+
+    def _create_tools(self):
+        """Create retrieval tools with optimized search parameters"""
+        research_retriever = self.vector_stores[0].as_retriever(
+            search_kwargs={"k": 3, "score_threshold": 0.7}
+        )
+        development_retriever = self.vector_stores[1].as_retriever(
+            search_kwargs={"k": 3, "score_threshold": 0.7}
         )
         
-    def _init_tools(self):
-        self.tools = [
+        return [
             create_retriever_tool(
-                self.research_vs.as_retriever(search_kwargs={"k": 5}),
+                research_retriever,
                 "research_db",
-                "Semantic search across research documents"
+                "Access technical research papers and reports"
             ),
             create_retriever_tool(
-                self.development_vs.as_retriever(search_kwargs={"k": 5}),
+                development_retriever,
                 "development_db",
-                "Search through project development updates"
+                "Retrieve project development status updates"
             )
         ]
-    
-    def _build_graph(self):
-        self.workflow = StateGraph(AgentState)
-        self.workflow.add_node("agent", self.quantum_agent)
-        self.workflow.add_node("retrieve", ToolNode(self.tools))
-        self.workflow.add_node("generate", self.generate_answer)
-        self.workflow.add_node("rewrite", self.rewrite_query)
+
+    def _build_workflow(self):
+        """Construct and return the processing workflow"""
+        workflow = StateGraph(AgentState)
         
-        self.workflow.set_entry_point("agent")
-        self.workflow.add_conditional_edges(
-            "agent", self._route_action,
-            {"retrieve": "retrieve", "direct": "generate"}
-        )
-        self.workflow.add_conditional_edges(
-            "retrieve", self._evaluate_results,
-            {"generate": "generate", "rewrite": "rewrite"}
-        )
-        self.workflow.add_edge("generate", END)
-        self.workflow.add_edge("rewrite", "agent")
+        workflow.add_node("analyze", self.analyze_query)
+        workflow.add_node("retrieve", ToolNode(self.tools))
+        workflow.add_node("synthesize", self.synthesize_response)
         
-        self.app = self.workflow.compile()
-    
-    def _route_action(self, state: AgentState) -> str:
-        # Advanced routing logic using ML-based classification
-        last_msg = state["messages"][-1].content.lower()
-        research_keywords = {"research", "study", "paper", "algorithm"}
-        dev_keywords = {"project", "status", "development", "update"}
+        workflow.set_entry_point("analyze")
         
-        if any(kw in last_msg for kw in research_keywords):
-            return "retrieve"
-        elif any(kw in last_msg for kw in dev_keywords):
-            return "retrieve"
-        return "direct"
-    
-    def _evaluate_results(self, state: AgentState) -> str:
-        # Advanced result evaluation with confidence scoring
-        results = state["messages"][-1].content
-        doc_count = results.count("Document(")
-        confidence = min(doc_count / 5, 1.0)  # Scale based on retrieved docs
+        workflow.add_conditional_edges(
+            "analyze",
+            self._needs_retrieval,
+            {"retrieve": "retrieve", "direct": "synthesize"}
+        )
         
-        if confidence >= 0.7:
-            return "generate"
-        return "rewrite"
-
-    # Core Components with Enterprise Features
-    def quantum_agent(self, state: AgentState):
-        # Implementation with advanced security and rate limiting
-        pass
-    
-    def generate_answer(self, state: AgentState):
-        # Multi-stage generation with fact-checking
-        pass
-    
-    def rewrite_query(self, state: AgentState):
-        # Context-aware query refinement
-        pass
-
-# ------------------------------
-# Military-Grade Security Setup
-# ------------------------------
-if not SecurityManager.validate_api_key(Config.DEEPSEEK_API_KEY):
-    st.error("""
-    🔐 Critical Security Alert:
-    Invalid API key configuration detected!
-    Please verify your DEEPSEEK_API_KEY environment variable.
-    """)
-    st.stop()
+        workflow.add_edge("retrieve", "synthesize")
+        workflow.add_edge("synthesize", END)
+        
+        return workflow.compile()
 
-# ------------------------------
-# Zero-Trust Vector Database
-# ------------------------------
-os.makedirs("chroma_db", exist_ok=True)
-chroma_client = chromadb.PersistentClient(
-    path="chroma_db",
-    settings=Settings(allow_reset=False, anonymized_telemetry=False)
-)
+    def _needs_retrieval(self, state: AgentState) -> str:
+        """Determine if document retrieval is needed"""
+        query = state["messages"][-1].content.lower()
+        return "retrieve" if any(kw in query for kw in {
+            "research", "study", "project", "develop", "trend"
+        }) else "direct"
 
-# ------------------------------
-# Cybernetic UI Framework
-# ------------------------------
-class HolographicInterface:
-    def __init__(self):
-        self._init_style()
-        self._init_session_state()
-        
-    def _init_style(self):
-        st.set_page_config(
-            page_title="NeuroSphere AI Analyst",
-            layout="wide",
-            initial_sidebar_state="expanded",
-            menu_items={
-                'Get Help': 'https://neurosphere.ai',
-                'Report a bug': "https://neurosphere.ai/support",
-                'About': "# NeuroSphere v2.0 - Cognitive Analysis Suite"
-            }
-        )
-        
-        st.markdown(f"""
-        <style>
-            :root {{
-                --primary: #2ecc71;
-                --secondary: #3498db;
-                --background: #0f0f12;
-                --text: #ecf0f1;
-            }}
+    def analyze_query(self, state: AgentState):
+        """Analyze user query and determine next steps"""
+        try:
+            user_input = state["messages"][-1].content
             
-            .stApp {{
-                background: var(--background);
-                color: var(--text);
-                font-family: 'Roboto Mono', monospace;
-            }}
+            headers = {
+                "Authorization": f"Bearer {Config.API_KEY}",
+                "Content-Type": "application/json"
+            }
             
-            .stTextInput input, .stTextArea textarea {{
-                background: #1a1a1f !important;
-                color: var(--text) !important;
-                border: 1px solid #2c3e50;
-                border-radius: 8px;
-                padding: 15px !important;
-            }}
+            response = requests.post(
+                "https://api.deepseek.com/v1/chat/completions",
+                headers=headers,
+                json={
+                    "model": "deepseek-chat",
+                    "messages": [{
+                        "role": "user",
+                        "content": f"""Analyze this query and format as:
+                        CATEGORY: [RESEARCH|DEVELOPMENT|GENERAL]
+                        KEY_TERMS: comma-separated list
+                        {user_input}"""
+                    }],
+                    "temperature": 0.3
+                },
+                timeout=15
+            )
             
-            .stButton>button {{
-                background: linear-gradient(135deg, var(--primary), var(--secondary));
-                border: none;
-                border-radius: 8px;
-                padding: 12px 24px;
-                font-weight: 700;
-                transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
-            }}
+            response.raise_for_status()
+            analysis = response.json()["choices"][0]["message"]["content"]
             
-            .stButton>button:hover {{
-                transform: translateY(-2px);
-                box-shadow: 0 4px 15px rgba(46, 204, 113, 0.3);
-            }}
+            return {"messages": [AIMessage(content=analysis)]}
+        
+        except Exception as e:
+            return {"messages": [AIMessage(
+                content=f"Analysis Error: {str(e)}. Please rephrase your question."
+            )]}
+
+    def synthesize_response(self, state: AgentState):
+        """Generate final response with citations"""
+        try:
+            context = "\n".join([msg.content for msg in state["messages"]])
             
-            .document-card {{
-                background: #1a1a1f;
-                border-left: 4px solid var(--secondary);
-                border-radius: 8px;
-                padding: 1.2rem;
-                margin: 1rem 0;
-                box-shadow: 0 2px 8px rgba(0,0,0,0.3);
-            }}
-        </style>
-        """, unsafe_allow_html=True)
-    
-    def _init_session_state(self):
-        if "conversation" not in st.session_state:
-            st.session_state.conversation = []
-        if "last_request" not in st.session_state:
-            st.session_state.last_request = 0
-    
-    def render(self):
-        st.title("🧠 NeuroSphere AI Research Analyst")
-        self._render_sidebar()
-        self._render_main_interface()
-    
-    def _render_sidebar(self):
-        with st.sidebar:
-            st.header("📡 Knowledge Nucleus")
-            with st.expander("🔬 Research Corpus", expanded=True):
-                for text in research_texts:
-                    st.markdown(f'<div class="document-card">{text}</div>', 
-                              unsafe_allow_html=True)
+            headers = {
+                "Authorization": f"Bearer {Config.API_KEY}",
+                "Content-Type": "application/json"
+            }
             
-            with st.expander("🚀 Development Hub", expanded=True):
-                for text in development_texts:
-                    st.markdown(f'<div class="document-card">{text}</div>', 
-                              unsafe_allow_html=True)
+            response = requests.post(
+                "https://api.deepseek.com/v1/chat/completions",
+                headers=headers,
+                json={
+                    "model": "deepseek-chat",
+                    "messages": [{
+                        "role": "user",
+                        "content": f"""Synthesize this information:
+                        {context}
+                        
+                        Include:
+                        1. Key findings
+                        2. Supporting evidence
+                        3. Technical details
+                        4. Potential applications"""
+                    }],
+                    "temperature": 0.5
+                },
+                timeout=20
+            )
             
-            st.divider()
-            self._render_analytics()
-    
-    def _render_analytics(self):
-        st.subheader("📊 Cognitive Metrics")
-        col1, col2 = st.columns(2)
-        col1.metric("Processing Speed", "42ms", "-3ms")
-        col2.metric("Accuracy Confidence", "98.7%", "+0.5%")
-        st.progress(0.87, text="Knowledge Coverage")
-    
-    def _render_main_interface(self):
-        col1, col2 = st.columns([1, 2])
+            response.raise_for_status()
+            return {"messages": [AIMessage(
+                content=response.json()["choices"][0]["message"]["content"]
+            )]}
         
-        with col1:
-            self._render_chat_interface()
-        
-        with col2:
-            self._render_analysis_panel()
+        except Exception as e:
+            return {"messages": [AIMessage(
+                content=f"Synthesis Error: {str(e)}. Please try again later."
+            )]}
+
+# ------------------------------
+# Professional UI Interface
+# ------------------------------
+def main():
+    st.set_page_config(
+        page_title="Research Assistant Pro",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
     
-    def _render_chat_interface(self):
-        with st.container(height=600, border=False):
-            st.subheader("💬 NeuroDialogue Interface")
-            query = st.chat_input("Query the knowledge universe...")
-            
-            if query:
-                self._handle_query(query)
-                
-            for msg in st.session_state.conversation:
-                self._render_message(msg)
+    # Dark theme implementation
+    st.markdown("""
+    <style>
+    .stApp {
+        background-color: #0f1114;
+        color: #ffffff;
+    }
+    .stTextInput input, .stTextArea textarea {
+        background-color: #1e1e24 !important;
+        color: #ffffff !important;
+    }
+    .stButton>button {
+        background: #2563eb;
+        transition: all 0.2s;
+    }
+    .stButton>button:hover {
+        background: #1d4ed8;
+        transform: scale(1.02);
+    }
+    .result-card {
+        background: #1a1a1f;
+        border-radius: 8px;
+        padding: 1.5rem;
+        margin: 1rem 0;
+    }
+    </style>
+    """, unsafe_allow_html=True)
     
-    def _render_analysis_panel(self):
-        with st.container(height=600, border=False):
-            st.subheader("🔍 Deep Analysis Matrix")
-            # Implement advanced visualization components
+    st.title("🔍 Research Assistant Pro")
+    st.write("Advanced AI-Powered Research Analysis")
     
-    def _handle_query(self, query: str):
-        # Implement enterprise query handling with rate limiting
-        pass
+    col1, col2 = st.columns([1, 2])
     
-    def _render_message(self, msg: dict):
-        # Implement holographic message rendering
-        pass
+    with col1:
+        with st.form("query_form"):
+            query = st.text_area("Research Query:", height=150,
+                                placeholder="Enter your research question...")
+            submitted = st.form_submit_button("Analyze")
+            
+            if submitted and query:
+                with st.spinner("Processing..."):
+                    try:
+                        assistant = ResearchAssistant()
+                        result = assistant.workflow.invoke({"messages": [
+                            HumanMessage(content=query)
+                        ]})
+                        
+                        with st.expander("Analysis Details", expanded=True):
+                            st.markdown(f"""
+                            <div class="result-card">
+                                {result['messages'][-1].content}
+                            </div>
+                            """, unsafe_allow_html=True)
+                    except Exception as e:
+                        st.error(f"Processing Error: {str(e)}")
+
+    with col2:
+        st.subheader("Knowledge Base")
+        with st.expander("Research Documents"):
+            st.info("""
+            - Advanced Image Recognition Systems
+            - Transformer Architecture Analysis
+            - Quantum ML Research
+            """)
+        
+        with st.expander("Development Updates"):
+            st.info("""
+            - Project A: API Integration Phase
+            - Project B: Feature Testing
+            - Product Y: Optimization Stage
+            """)
 
-# ------------------------------
-# Quantum Execution Core
-# ------------------------------
 if __name__ == "__main__":
-    interface = HolographicInterface()
-    interface.render()
-    engine = EnterpriseWorkflowEngine()
+    if not Config.API_KEY:
+        st.error("""
+        🔑 Configuration Required:
+        Set DEEPSEEK_API_KEY environment variable
+        """)
+        st.stop()
+    main()