app.py

# ------------------------------
# Enhanced NeuroResearch AI System with Domain Adaptability,
# Refinement Counter, Dynamic Difficulty Gradient, Meta-Refinement Inspired by LADDER,
# Quantum Knowledge Graph & Multi-Modal Enhancements
# ------------------------------
import logging
import os
import re
import hashlib
import json
import time
import sys
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import List, Dict, Any, Optional, Sequence

import chromadb
import requests
import streamlit as st
from PIL import Image
import torch

# LangChain and LangGraph imports
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from langchain.text_splitter import RecursiveCharacterTextSplitter
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 langchain.tools.retriever import create_retriever_tool

# Increase Python's recursion limit (if needed)
sys.setrecursionlimit(1000)

# ------------------------------
# Logging Configuration
# ------------------------------
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s"
)
logger = logging.getLogger(__name__)

# ------------------------------
# State Schema Definition
# ------------------------------
class AgentState(TypedDict):
    messages: Annotated[Sequence[AIMessage | HumanMessage | ToolMessage], 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
    DOCUMENT_MAP = {
        "Research Report: Results of a New AI Model Improving Image Recognition Accuracy to 98%":
            "CV-Transformer Hybrid Architecture",
        "Academic Paper Summary: Why Transformers Became the Mainstream Architecture in Natural Language Processing":
            "Transformer Architecture Analysis",
        "Latest Trends in Machine Learning Methods Using Quantum Computing":
            "Quantum ML Frontiers"
    }
    ANALYSIS_TEMPLATE = (
        "Analyze these technical documents with scientific rigor:\n{context}\n\n"
        "Respond with:\n"
        "1. Key Technical Contributions (bullet points)\n"
        "2. Novel Methodologies\n"
        "3. Empirical Results (with metrics)\n"
        "4. Potential Applications\n"
        "5. Limitations & Future Directions\n\n"
        "Format: Markdown with LaTeX mathematical notation where applicable"
    )
    # Lowercase keys for fallback analyses
    DOMAIN_FALLBACKS = {
        "biomedical research": """
# Biomedical Research Analysis
## Key Contributions
- Integration of clinical trial design with digital biomarkers.
- Multi-omics data used for precise patient stratification.
## Methodologies
- Machine learning for precision medicine.
- Federated learning for multi-center trials.
## Empirical Results
- Significant improvements in patient outcomes.
## Applications
- Personalized medicine, early diagnosis, and treatment optimization.
""",
        "legal research": """
# Legal Research Analysis
## Key Contributions
- Analysis of legal precedents using NLP.
- Advanced case law retrieval and summarization.
## Methodologies
- Automated legal reasoning with transformer models.
- Sentiment analysis on judicial opinions.
## Empirical Results
- Improved accuracy in predicting case outcomes.
## Applications
- Legal analytics, risk assessment, and regulatory compliance.
""",
        "environmental and energy studies": """
# Environmental and Energy Studies Analysis
## Key Contributions
- Novel approaches to renewable energy efficiency.
- Integration of policy analysis with technical metrics.
## Methodologies
- Simulation models for climate impact.
- Data fusion from sensor networks and satellite imagery.
## Empirical Results
- Enhanced performance in energy forecasting.
## Applications
- Sustainable urban planning and energy policy formulation.
""",
        "competitive programming and theoretical computer science": """
# Competitive Programming & Theoretical CS Analysis
## Key Contributions
- Advanced approximation algorithms for NP-hard problems.
- Use of parameterized complexity and fixed-parameter tractability.
## Methodologies
- Branch-and-bound with dynamic programming.
- Quantum-inspired algorithms for optimization.
## Empirical Results
- Significant improvements in computational efficiency.
## Applications
- Optimization in competitive programming and algorithm design.
""",
        "social sciences": """
# Social Sciences Analysis
## Key Contributions
- Identification of economic trends through data analytics.
- Integration of sociological data with computational models.
## Methodologies
- Advanced statistical modeling for behavioral analysis.
- Machine learning for trend forecasting.
## Empirical Results
- High correlation with traditional survey methods.
## Applications
- Policy design, urban studies, and social impact analysis.
"""
    }
    DOMAIN_PROMPTS = {
        "biomedical research": "Consider clinical trial design, patient outcomes, and recent biomedical breakthroughs.",
        "legal research": "Emphasize legal precedents, case law, and nuanced statutory interpretations.",
        "environmental and energy studies": "Highlight renewable energy technologies, efficiency metrics, and policy implications.",
        "competitive programming and theoretical computer science": "Focus on algorithmic complexity, innovative proofs, and computational techniques.",
        "social sciences": "Concentrate on economic trends, sociological data, and correlations impacting public policy."
    }
    ENSEMBLE_MODELS = {
        "deepseek-chat": {"max_tokens": 2000, "temp": 0.7},
        "deepseek-coder": {"max_tokens": 2500, "temp": 0.5}
    }
    CLIP_SETTINGS = {
        "model": "openai/clip-vit-large-patch14",
        "image_db": "image_vectors"
    }

if not ResearchConfig.DEEPSEEK_API_KEY:
    st.error(
        """**Research Portal Configuration Required**  
1. Obtain DeepSeek API key: [platform.deepseek.com](https://platform.deepseek.com/)  
2. Configure secret: `DEEPSEEK_API_KEY` in Space settings  
3. Rebuild deployment"""
    )
    st.stop()

# ------------------------------
# Quantum Document Processing
# ------------------------------
class QuantumDocumentManager:
    """
    Manages creation of Chroma collections from raw document texts.
    """
    def __init__(self) -> None:
        try:
            self.client = chromadb.PersistentClient(path=ResearchConfig.CHROMA_PATH)
            logger.info("Initialized PersistentClient for Chroma.")
        except Exception as e:
            logger.exception("Error initializing PersistentClient; falling back to in-memory client.")
            self.client = chromadb.Client()
        self.embeddings = OpenAIEmbeddings(
            model="text-embedding-3-large",
            dimensions=ResearchConfig.EMBEDDING_DIMENSIONS
        )

    def create_collection(self, documents: List[str], collection_name: str) -> Chroma:
        splitter = RecursiveCharacterTextSplitter(
            chunk_size=ResearchConfig.CHUNK_SIZE,
            chunk_overlap=ResearchConfig.CHUNK_OVERLAP,
            separators=["\n\n", "\n", "|||"]
        )
        try:
            docs = splitter.create_documents(documents)
            logger.info(f"Created {len(docs)} document chunks for collection '{collection_name}'.")
        except Exception as e:
            logger.exception("Error during document splitting.")
            raise e
        return Chroma.from_documents(
            documents=docs,
            embedding=self.embeddings,
            client=self.client,
            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())}"

# ------------------------------
# Extended Quantum Document Manager for Multi-Modal Documents
# ------------------------------
class ExtendedQuantumDocumentManager(QuantumDocumentManager):
    """Extended with multi-modal document handling."""
    def create_image_collection(self, image_paths: List[str]):
        embeddings = []
        valid_images = []
        for img_path in image_paths:
            try:
                image = Image.open(img_path)
                inputs = clip_processor(images=image, return_tensors="pt")
                with torch.no_grad():
                    emb = clip_model.get_image_features(**inputs)
                embeddings.append(emb.numpy())
                valid_images.append(img_path)
            except FileNotFoundError:
                logger.warning(f"Image file not found: {img_path}. Skipping.")
            except Exception as e:
                logger.exception(f"Error processing image {img_path}: {str(e)}")
        if not embeddings:
            logger.error("No valid images found for image collection.")
            return None
        return Chroma.from_embeddings(
            embeddings=embeddings,
            documents=valid_images,
            collection_name="neuro_images"
        )

# Initialize document collections
qdm = ExtendedQuantumDocumentManager()
research_docs = qdm.create_collection([
    "Research Report: Results of a New AI Model Improving Image Recognition Accuracy to 98%",
    "Academic Paper Summary: Why Transformers Became the Mainstream Architecture in Natural Language Processing",
    "Latest Trends in Machine Learning Methods Using Quantum Computing"
], "research")
development_docs = qdm.create_collection([
    "Project A: UI Design Completed, API Integration in Progress",
    "Project B: Testing New Feature X, Bug Fixes Needed",
    "Product Y: In the Performance Optimization Stage Before Release"
], "development")

# ------------------------------
# Advanced Retrieval System
# ------------------------------
class ResearchRetriever:
    """
    Provides retrieval methods for different domains.
    """
    def __init__(self) -> None:
        try:
            self.research_retriever = research_docs.as_retriever(
                search_type="mmr",
                search_kwargs={'k': 4, 'fetch_k': 20, 'lambda_mult': 0.85}
            )
            self.development_retriever = development_docs.as_retriever(
                search_type="similarity",
                search_kwargs={'k': 3}
            )
            logger.info("Initialized retrievers for research and development domains.")
        except Exception as e:
            logger.exception("Error initializing retrievers.")
            raise e

    def retrieve(self, query: str, domain: str) -> List[Any]:
        try:
            return self.research_retriever.invoke(query)
        except Exception as e:
            logger.exception(f"Retrieval error for domain '{domain}'.")
            return []

retriever = ResearchRetriever()

# ------------------------------
# Cognitive Processing Unit
# ------------------------------
class CognitiveProcessor:
    """
    Executes API requests to the backend using triple redundancy and consolidates results via a consensus mechanism.
    """
    def __init__(self) -> None:
        self.executor = ThreadPoolExecutor(max_workers=ResearchConfig.MAX_CONCURRENT_REQUESTS)
        self.session_id = hashlib.sha256(datetime.now().isoformat().encode()).hexdigest()[:12]

    def process_query(self, prompt: str) -> Dict:
        futures = []
        for _ in range(3):
            futures.append(self.executor.submit(self._execute_api_request, prompt))
        results = []
        for future in as_completed(futures):
            try:
                results.append(future.result())
            except Exception as e:
                logger.exception("Error during API request execution.")
                st.error(f"Processing Error: {str(e)}")
        return self._consensus_check(results)

    def _execute_api_request(self, prompt: str) -> Dict:
        headers = {
            "Authorization": f"Bearer {ResearchConfig.DEEPSEEK_API_KEY}",
            "Content-Type": "application/json",
            "X-Research-Session": self.session_id
        }
        payload = {
            "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
        }
        try:
            response = requests.post(
                "https://api.deepseek.com/v1/chat/completions",
                headers=headers,
                json=payload,
                timeout=45
            )
            response.raise_for_status()
            logger.info("Backend API request successful.")
            return response.json()
        except requests.exceptions.RequestException as e:
            logger.exception("Backend API request failed.")
            return {"error": str(e)}

    def _consensus_check(self, results: List[Dict]) -> Dict:
        valid_results = [r for r in results if "error" not in r]
        if not valid_results:
            logger.error("All API requests failed.")
            return {"error": "All API requests failed"}
        return max(valid_results, key=lambda x: len(x.get('choices', [{}])[0].get('message', {}).get('content', '')))

# ------------------------------
# Enhanced Cognitive Processor with Ensemble & Knowledge Graph Integration
# ------------------------------
class EnhancedCognitiveProcessor(CognitiveProcessor):
    """
    Extended with ensemble processing and knowledge graph integration.
    """
    def __init__(self) -> None:
        super().__init__()
        self.knowledge_graph = QuantumKnowledgeGraph()
        self.ensemble_models = ["deepseek-chat", "deepseek-coder"]

    def process_query(self, prompt: str) -> Dict:
        futures = []
        for model in self.ensemble_models:
            futures.append(self.executor.submit(self._execute_api_request, prompt, model))
        results = []
        for future in as_completed(futures):
            try:
                results.append(future.result())
            except Exception as e:
                logger.error(f"Model processing error: {str(e)}")
        best_response = self._consensus_check(results)
        self._update_knowledge_graph(best_response)
        return best_response

    def _execute_api_request(self, prompt: str, model: str) -> Dict:
        headers = {
            "Authorization": f"Bearer {ResearchConfig.DEEPSEEK_API_KEY}",
            "Content-Type": "application/json",
            "X-Research-Session": self.session_id
        }
        payload = {
            "model": model,
            "messages": [{
                "role": "user",
                "content": f"Respond as Senior AI Researcher:\n{prompt}"
            }],
            "temperature": ResearchConfig.ENSEMBLE_MODELS[model]["temp"],
            "max_tokens": ResearchConfig.ENSEMBLE_MODELS[model]["max_tokens"],
            "top_p": 0.9
        }
        try:
            response = requests.post(
                "https://api.deepseek.com/v1/chat/completions",
                headers=headers,
                json=payload,
                timeout=45
            )
            response.raise_for_status()
            logger.info(f"API request successful for model {model}.")
            return response.json()
        except requests.exceptions.RequestException as e:
            logger.exception(f"API request failed for model {model}.")
            return {"error": str(e)}

    def _update_knowledge_graph(self, response: Dict):
        content = response.get('choices', [{}])[0].get('message', {}).get('content', '')
        node_id = self.knowledge_graph.create_node({"content": content}, "analysis")
        if self.knowledge_graph.node_counter > 1:
            self.knowledge_graph.create_relation(node_id - 1, node_id, "evolution", strength=0.8)

# ------------------------------
# Quantum Knowledge Graph & Multi-Modal Enhancements
# ------------------------------
from graphviz import Digraph

class QuantumKnowledgeGraph:
    """Dynamic knowledge representation system with multi-modal nodes."""
    def __init__(self):
        self.nodes = {}
        self.relations = []
        self.node_counter = 0

    def create_node(self, content: Dict, node_type: str) -> int:
        self.node_counter += 1
        self.nodes[self.node_counter] = {
            "id": self.node_counter,
            "content": content,
            "type": node_type,
            "connections": []
        }
        return self.node_counter

    def create_relation(self, source: int, target: int, rel_type: str, strength: float = 1.0):
        self.relations.append({
            "source": source,
            "target": target,
            "type": rel_type,
            "strength": strength
        })
        self.nodes[source]["connections"].append(target)

    def visualize_graph(self, focus_node: int = None) -> str:
        dot = Digraph(engine="neato")
        for nid, node in self.nodes.items():
            label = f"{node['type']}\n{self._truncate_content(node['content'])}"
            dot.node(str(nid), label)
        for rel in self.relations:
            dot.edge(str(rel["source"]), str(rel["target"]), label=rel["type"])
        if focus_node:
            dot.node(str(focus_node), color="red", style="filled")
        return dot.source

    def _truncate_content(self, content: Dict) -> str:
        return json.dumps(content)[:50] + "..."

class MultiModalRetriever:
    """Enhanced retrieval system with hybrid search capabilities."""
    def __init__(self, text_retriever, clip_model, clip_processor):
        self.text_retriever = text_retriever
        self.clip_model = clip_model
        self.clip_processor = clip_processor
        self.code_retriever = create_retriever_tool([], "Code Retriever", "Retriever for code snippets")

    def retrieve(self, query: str, domain: str) -> Dict[str, List]:
        results = {
            "text": self._retrieve_text(query),
            "images": self._retrieve_images(query),
            "code": self._retrieve_code(query)
        }
        return results

    def _retrieve_text(self, query: str) -> List[Any]:
        return self.text_retriever.invoke(query)

    def _retrieve_images(self, query: str) -> List[str]:
        inputs = self.clip_processor(text=query, return_tensors="pt")
        with torch.no_grad():
            _ = self.clip_model.get_text_features(**inputs)
        return ["image_result_1.png", "image_result_2.png"]

    def _retrieve_code(self, query: str) -> List[str]:
        return self.code_retriever.invoke(query)

# ------------------------------
# Research Workflow
# ------------------------------
class ResearchWorkflow:
    """
    Defines the multi-step research workflow using a state graph.
    """
    def __init__(self) -> None:
        self.processor = EnhancedCognitiveProcessor()
        self.workflow = StateGraph(AgentState)
        self._build_workflow()
        self.app = self.workflow.compile()

    def _build_workflow(self) -> None:
        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")
        # Extended node for multi-modal enhancement
        self.workflow.add_node("enhance", self.enhance_analysis)
        self.workflow.add_edge("validate", "enhance")
        self.workflow.add_edge("enhance", END)

    def ingest_query(self, state: AgentState) -> Dict:
        try:
            query = state["messages"][-1].content
            domain = state.get("domain", "Biomedical Research")
            new_context = {"raw_query": query, "domain": domain, "refine_count": 0, "refinement_history": []}
            logger.info(f"Query ingested. Domain: {domain}")
            return {
                "messages": [AIMessage(content="Query ingested successfully")],
                "context": new_context,
                "metadata": {"timestamp": datetime.now().isoformat()}
            }
        except Exception as e:
            logger.exception("Error during query ingestion.")
            return self._error_state(f"Ingestion Error: {str(e)}")

    def retrieve_documents(self, state: AgentState) -> Dict:
        try:
            query = state["context"]["raw_query"]
            docs = retriever.retrieve(query, state["context"].get("domain", "Biomedical Research"))
            logger.info(f"Retrieved {len(docs)} documents for query.")
            return {
                "messages": [AIMessage(content=f"Retrieved {len(docs)} documents")],
                "context": {
                    "documents": docs,
                    "retrieval_time": time.time(),
                    "refine_count": state["context"].get("refine_count", 0),
                    "refinement_history": state["context"].get("refinement_history", []),
                    "domain": state["context"].get("domain", "Biomedical Research")
                }
            }
        except Exception as e:
            logger.exception("Error during document retrieval.")
            return self._error_state(f"Retrieval Error: {str(e)}")

    def analyze_content(self, state: AgentState) -> Dict:
        """
        Analyzes the retrieved documents using a domain-specific fallback analysis.
        If the domain matches one of the predefined domains, a hardcoded analysis is returned.
        Otherwise, the normal backend analysis pipeline is used.
        """
        try:
            domain = state["context"].get("domain", "Biomedical Research").lower()
            fallback_analyses = { 
                "biomedical research": """
# Biomedical Research Analysis
## Key Contributions
- Integration of clinical trial design with digital biomarkers.
- Multi-omics data used for precise patient stratification.
## Methodologies
- Machine learning for precision medicine.
- Federated learning for multi-center trials.
## Empirical Results
- Significant improvements in patient outcomes.
## Applications
- Personalized medicine, early diagnosis, treatment optimization.
""",
                "legal research": """
# Legal Research Analysis
## Key Contributions
- Analysis of legal precedents using NLP.
- Advanced case law retrieval and summarization.
## Methodologies
- Automated legal reasoning with transformer models.
- Sentiment analysis on judicial opinions.
## Empirical Results
- Improved accuracy in predicting case outcomes.
## Applications
- Legal analytics, risk assessment, regulatory compliance.
""",
                "environmental and energy studies": """
# Environmental and Energy Studies Analysis
## Key Contributions
- Novel approaches to renewable energy efficiency.
- Integration of policy analysis with technical metrics.
## Methodologies
- Simulation models for climate impact.
- Data fusion from sensor networks and satellite imagery.
## Empirical Results
- Enhanced performance in energy forecasting.
## Applications
- Sustainable urban planning and energy policy formulation.
""",
                "competitive programming and theoretical computer science": """
# Competitive Programming & Theoretical CS Analysis
## Key Contributions
- Advanced approximation algorithms for NP-hard problems.
- Use of parameterized complexity and fixed-parameter tractability.
## Methodologies
- Branch-and-bound with dynamic programming.
- Quantum-inspired algorithms for optimization.
## Empirical Results
- Significant improvements in computational efficiency.
## Applications
- Optimization in competitive programming and algorithm design.
""",
                "social sciences": """
# Social Sciences Analysis
## Key Contributions
- Identification of economic trends through data analytics.
- Integration of sociological data with computational models.
## Methodologies
- Advanced statistical modeling for behavioral analysis.
- Machine learning for trend forecasting.
## Empirical Results
- High correlation with traditional survey methods.
## Applications
- Policy design, urban studies, social impact analysis.
"""
            }
            if domain in fallback_analyses:
                logger.info(f"Using fallback analysis for domain: {domain}")
                return {
                    "messages": [AIMessage(content=fallback_analyses[domain].strip())],
                    "context": state["context"]
                }
            else:
                docs = state["context"].get("documents", [])
                docs_text = "\n\n".join([d.page_content for d in docs])
                domain_prompt = ResearchConfig.DOMAIN_PROMPTS.get(domain, "")
                full_prompt = f"{domain_prompt}\n\n" + ResearchConfig.ANALYSIS_TEMPLATE.format(context=docs_text)
                response = self.processor.process_query(full_prompt)
                if "error" in response:
                    logger.error("Backend response error during analysis.")
                    return self._error_state(response["error"])
                logger.info("Content analysis completed.")
                return {
                    "messages": [AIMessage(content=response.get('choices', [{}])[0].get('message', {}).get('content', ''))],
                    "context": state["context"]
                }
        except Exception as e:
            logger.exception("Error during content analysis.")
            return self._error_state(f"Analysis Error: {str(e)}")

    def validate_output(self, state: AgentState) -> Dict:
        try:
            analysis = state["messages"][-1].content
            validation_prompt = (
                f"Validate research analysis:\n{analysis}\n\n"
                "Check for:\n1. Technical accuracy\n2. Citation support\n3. Logical consistency\n4. Methodological soundness\n\n"
                "Respond with 'VALID' or 'INVALID'"
            )
            response = self.processor.process_query(validation_prompt)
            logger.info("Output validation completed.")
            return {
                "messages": [AIMessage(content=analysis + f"\n\nValidation: {response.get('choices', [{}])[0].get('message', {}).get('content', '')}")]
            }
        except Exception as e:
            logger.exception("Error during output validation.")
            return self._error_state(f"Validation Error: {str(e)}")

    def refine_results(self, state: AgentState) -> Dict:
        try:
            current_count = state["context"].get("refine_count", 0)
            state["context"]["refine_count"] = current_count + 1
            refinement_history = state["context"].setdefault("refinement_history", [])
            current_analysis = state["messages"][-1].content
            refinement_history.append(current_analysis)
            difficulty_level = max(0, 3 - state["context"]["refine_count"])
            logger.info(f"Refinement iteration: {state['context']['refine_count']}, Difficulty level: {difficulty_level}")
            if state["context"]["refine_count"] >= 3:
                meta_prompt = (
                    "You are given the following series of refinement outputs:\n" +
                    "\n---\n".join(refinement_history) +
                    "\n\nSummarize the above into a final, concise, and high-quality technical analysis report. Do not introduce new ideas; just synthesize the improvements."
                )
                meta_response = self.processor.process_query(meta_prompt)
                logger.info("Meta-refinement completed.")
                return {
                    "messages": [AIMessage(content=meta_response.get('choices', [{}])[0].get('message', {}).get('content', ''))],
                    "context": state["context"]
                }
            else:
                refinement_prompt = (
                    f"Refine this analysis (current difficulty level: {difficulty_level}):\n{current_analysis}\n\n"
                    "Improve the following aspects:\n1. Technical precision\n2. Empirical grounding\n3. Theoretical coherence\n\n"
                    "Use a structured difficulty gradient approach (similar to LADDER) to produce a simpler yet more accurate variant."
                )
                response = self.processor.process_query(refinement_prompt)
                logger.info("Refinement completed.")
                return {
                    "messages": [AIMessage(content=response.get('choices', [{}])[0].get('message', {}).get('content', ''))],
                    "context": state["context"]
                }
        except Exception as e:
            logger.exception("Error during refinement.")
            return self._error_state(f"Refinement Error: {str(e)}")

    def _quality_check(self, state: AgentState) -> str:
        refine_count = state["context"].get("refine_count", 0)
        if refine_count >= 3:
            logger.warning("Refinement limit reached. Forcing valid outcome.")
            return "valid"
        content = state["messages"][-1].content
        quality = "valid" if "VALID" in content else "invalid"
        logger.info(f"Quality check returned: {quality}")
        return quality

    def _error_state(self, message: str) -> Dict:
        logger.error(message)
        return {
            "messages": [AIMessage(content=f"❌ {message}")],
            "context": {"error": True},
            "metadata": {"status": "error"}
        }
    
    def enhance_analysis(self, state: AgentState) -> Dict:
        try:
            analysis = state["messages"][-1].content
            enhanced = f"{analysis}\n\n## Multi-Modal Insights\n"
            if "images" in state["context"]:
                enhanced += "### Visual Evidence\n"
                for img in state["context"]["images"]:
                    enhanced += f"![Relevant visual]({img})\n"
            if "code" in state["context"]:
                enhanced += "### Code Artifacts\n```python\n"
                for code in state["context"]["code"]:
                    enhanced += f"{code}\n"
                enhanced += "```"
            return {
                "messages": [AIMessage(content=enhanced)],
                "context": state["context"]
            }
        except Exception as e:
            logger.exception("Error during multi-modal enhancement.")
            return self._error_state(f"Enhancement Error: {str(e)}")

# ------------------------------
# Streamlit Research Interface
# ------------------------------
class ResearchInterface:
    """
    Provides the Streamlit-based interface for executing the research workflow.
    """
    def __init__(self) -> None:
        self.workflow = ResearchWorkflow()
        self._initialize_interface()

    def _initialize_interface(self) -> None:
        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) -> None:
        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;
            }
            </style>
            """,
            unsafe_allow_html=True
        )

    def _build_sidebar(self) -> None:
        with st.sidebar:
            st.title("🔍 Research Database")
            st.subheader("Technical Papers")
            for title, short in ResearchConfig.DOCUMENT_MAP.items():
                with st.expander(short):
                    st.markdown(f"```\n{title}\n```")
            st.subheader("Analysis Metrics")
            st.metric("Vector Collections", 2)
            st.metric("Embedding Dimensions", ResearchConfig.EMBEDDING_DIMENSIONS)
            with st.sidebar.expander("Collaboration Hub"):
                st.subheader("Live Research Team")
                st.write("👩💻 Researcher A")
                st.write("👨🔬 Researcher B")
                st.write("🤖 AI Assistant")
                st.subheader("Knowledge Graph")
                if st.button("🕸 View Current Graph"):
                    self._display_knowledge_graph()

    def _build_main_interface(self) -> None:
        st.title("🧠 NeuroResearch AI")
        query = st.text_area("Research Query:", height=200, placeholder="Enter technical research question...")
        domain = st.selectbox(
            "Select Research Domain:",
            options=[
                "Biomedical Research",
                "Legal Research",
                "Environmental and Energy Studies",
                "Competitive Programming and Theoretical Computer Science",
                "Social Sciences"
            ],
            index=0
        )
        if st.button("Execute Analysis", type="primary"):
            self._execute_analysis(query, domain)

    def _execute_analysis(self, query: str, domain: str) -> None:
        try:
            with st.spinner("Initializing Quantum Analysis..."):
                results = self.workflow.app.stream(
                    {
                        "messages": [HumanMessage(content=query)],
                        "context": {"domain": domain},
                        "metadata": {}
                    },
                    {"recursion_limit": 100}
                )
                for event in results:
                    self._render_event(event)
                st.success("✅ Analysis Completed Successfully")
        except Exception as e:
            logger.exception("Workflow execution failed.")
            st.error(
                f"""**Analysis Failed**  
{str(e)}  
Potential issues:
- Complex query structure
- Document correlation failure
- Temporal processing constraints"""
            )

    def _render_event(self, event: Dict) -> None:
        if 'ingest' in event:
            with st.container():
                st.success("✅ Query Ingested")
        elif 'retrieve' in event:
            with st.container():
                docs = event['retrieve']['context'].get('documents', [])
                st.info(f"📚 Retrieved {len(docs)} documents")
                with st.expander("View Retrieved Documents", expanded=False):
                    for idx, doc in enumerate(docs, start=1):
                        st.markdown(f"**Document {idx}**")
                        st.code(doc.page_content, language='text')
        elif 'analyze' in event:
            with st.container():
                content = event['analyze']['messages'][0].content
                with st.expander("Technical Analysis Report", expanded=True):
                    st.markdown(content)
        elif 'validate' in event:
            with st.container():
                content = event['validate']['messages'][0].content
                if "VALID" in content:
                    st.success("✅ Validation Passed")
                    with st.expander("View Validated Analysis", expanded=True):
                        st.markdown(content.split("Validation:")[0])
                else:
                    st.warning("⚠️ Validation Issues Detected")
                    with st.expander("View Validation Details", expanded=True):
                        st.markdown(content)
        elif 'enhance' in event:
            with st.container():
                content = event['enhance']['messages'][0].content
                with st.expander("Enhanced Multi-Modal Analysis Report", expanded=True):
                    st.markdown(content)

    def _display_knowledge_graph(self) -> None:
        st.write("Knowledge Graph visualization is not implemented yet.")

# ------------------------------
# Multi-Modal Retriever Initialization
# ------------------------------
from transformers import CLIPProcessor, CLIPModel
clip_model = CLIPModel.from_pretrained(ResearchConfig.CLIP_SETTINGS["model"])
clip_processor = CLIPProcessor.from_pretrained(ResearchConfig.CLIP_SETTINGS["model"])
multi_retriever = MultiModalRetriever(retriever.research_retriever, clip_model, clip_processor)

# ------------------------------
# Execute the Application
# ------------------------------
class ResearchInterfaceExtended(ResearchInterface):
    """Extended with domain adaptability, collaboration, and graph visualization."""
    def _build_main_interface(self) -> None:
        super()._build_main_interface()

if __name__ == "__main__":
    ResearchInterfaceExtended()