app.py

"""
Enhanced NeuroResearch AI System
---------------------------------
This application integrates domain-adaptive multi-modal retrieval, ensemble cognitive processing,
and dynamic knowledge graph construction. It is designed for advanced technical research,
analysis, and reporting, employing triple-redundant API requests and a structured state workflow.
"""

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]

# ------------------------------
# Application Configuration
# ------------------------------
class ResearchConfig:
    # Environment & API configuration
    DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY")
    CHROMA_PATH = "chroma_db"
    
    # Document processing settings
    CHUNK_SIZE = 512
    CHUNK_OVERLAP = 64
    MAX_CONCURRENT_REQUESTS = 5
    EMBEDDING_DIMENSIONS = 1536
    
    # Mapping of documents to research topics
    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"
    }
    
    # Template for detailed analysis using Markdown and LaTeX formatting
    ANALYSIS_TEMPLATE = (
        "Let's think step by step. Synthesize a comprehensive technical report based on the following documents. "
        "Focus on identifying the key innovations, empirical results, and potential limitations. Explicitly state any assumptions made during your analysis. "
        "The report MUST be valid Markdown, and all mathematical notation MUST be correctly formatted LaTeX (e.g., `E=mc^2`).\n\n"
        "Documents:\n{context}\n\n"
        "Respond with the following structure:\n"
        "# Technical Analysis Report\n\n"
        "1. **Key Technical Contributions:** (Bullet points highlighting the main innovations)\n"
        "2. **Novel Methodologies:** (Detailed explanation of the new methods used)\n"
        "3. **Empirical Results:** (Quantitative results with specific metrics, e.g., accuracy, precision, recall, F1-score. Include confidence intervals where appropriate.)\n"
        "4. **Potential Applications:** (Real-world applications of the technology)\n"
        "5. **Limitations and Future Directions:** (Current limitations and suggestions for future research)\n\n"
        "Format: Markdown with LaTeX mathematical notation where applicable."
    )
    
    # Domain-specific fallback analyses and prompts
    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, 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 combined 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.
"""
    }
    DOMAIN_PROMPTS = {
        "biomedical research": """
Consider clinical trial design, patient outcomes, and recent biomedical breakthroughs. For example, discuss how a new drug might impact patient survival rates or how a new diagnostic technique might improve early detection of a disease. Discuss specific clinical studies if available.
""",
        "legal research": """
Emphasize legal precedents, case law, and nuanced statutory interpretations. For example, when analyzing a case, identify the key holdings, explain the legal reasoning behind the decision, and compare it to other relevant cases. If a statute is involved, discuss how the court interpreted the statute and whether there are any ambiguities or conflicts with other laws.
""",
        "environmental and energy studies": """
Highlight renewable energy technologies, efficiency metrics, and policy implications. Provide specific data points on energy consumption and environmental impact. For instance, compare the energy efficiency of solar panels from different manufacturers, or discuss the impact of a specific environmental regulation on air quality.
""",
        "competitive programming and theoretical computer science": """
Focus on algorithmic complexity, innovative proofs, and computational techniques. For example, analyze the time and space complexity of a new algorithm, or explain the key steps in a mathematical proof. Include pseudocode or code snippets where appropriate.
""",
        "social sciences": """
Concentrate on economic trends, sociological data, and correlations impacting public policy. For example, analyze the impact of a new social program on poverty rates, or discuss the relationship between education levels and income inequality. Cite specific studies and statistical data to support your claims.
"""
    }
    
    # Ensemble model settings
    ENSEMBLE_MODELS = {
        "deepseek-chat": {"max_tokens": 2000, "temp": 0.7},
        "deepseek-coder": {"max_tokens": 2500, "temp": 0.5}
    }
    
    # CLIP model settings for image embeddings
    CLIP_SETTINGS = {
        "model": "openai/clip-vit-large-patch14",
        "image_db": "image_vectors"
    }

# Ensure required API keys are configured
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):
    """
    Extends QuantumDocumentManager with multi-modal (image) document handling.
    Uses dependency injection for CLIP components.
    """
    def __init__(self, clip_model: Any, clip_processor: Any) -> None:
        super().__init__()
        self.clip_model = clip_model
        self.clip_processor = clip_processor

    def create_image_collection(self, image_paths: List[str]) -> Optional[Chroma]:
        embeddings = []
        valid_images = []
        for img_path in image_paths:
            try:
                image = Image.open(img_path)
                inputs = self.clip_processor(images=image, return_tensors="pt")
                with torch.no_grad():
                    emb = self.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(clip_model=None, clip_processor=None)  # clip_model/processor to be set later
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 research and development 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 = [self.executor.submit(self._execute_api_request, prompt) for _ in range(3)]
        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 a Senior AI Researcher and Technical Writer:\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"}
        # Choose the result with the longest response content as a simple consensus metric
        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):
    """
    Extends CognitiveProcessor 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 = [self.executor.submit(self._execute_api_request, prompt, model) for model in self.ensemble_models]
        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 a Senior AI Researcher and Technical Writer:\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) -> None:
        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:
    """
    Represents a dynamic, multi-modal knowledge graph.
    """
    def __init__(self):
        self.nodes: Dict[int, Dict[str, Any]] = {}
        self.relations: List[Dict[str, Any]] = []
        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) -> None:
        self.relations.append({
            "source": source,
            "target": target,
            "type": rel_type,
            "strength": strength
        })
        self.nodes[source]["connections"].append(target)

    def visualize_graph(self, focus_node: Optional[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] + "..."

# ------------------------------
# Multi-Modal Retriever
# ------------------------------
class MultiModalRetriever:
    """
    Enhanced retrieval system that integrates text, image, and code snippet search.
    """
    def __init__(self, text_retriever: Any, clip_model: Any, clip_processor: Any) -> None:
        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]:
        return {
            "text": self._retrieve_text(query),
            "images": self._retrieve_images(query),
            "code": self._retrieve_code(query)
        }

    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 a 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
            # Retrieve the domain from the state's context (defaulting to Biomedical Research)
            domain = state.get("context", {}).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. If a domain-specific fallback is available, it is used;
        otherwise, the system synthesizes a comprehensive analysis via the cognitive processor.
        """
        try:
            domain = state["context"].get("domain", "Biomedical Research").strip().lower()
            fallback_analyses = ResearchConfig.DOMAIN_FALLBACKS
            if domain in fallback_analyses:
                logger.info(f"Using fallback analysis for domain: {state['context'].get('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 the following research analysis:\n{analysis}\n\n"
                "Check for:\n"
                "1. Technical accuracy\n"
                "2. Citation support (are claims backed by evidence?)\n"
                "3. Logical consistency\n"
                "4. Methodological soundness\n\n"
                "Respond with 'VALID: [brief justification]' or 'INVALID: [brief justification]'."
            )
            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\nSynthesize the above into a final, concise, and high-quality technical analysis report. "
                    "Focus on the key findings and improvements made across the iterations. Do not introduce new ideas; just synthesize the improvements. Ensure the report is well-structured and easy to understand."
                )
                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"
                    "First, critically evaluate the analysis and identify its weaknesses, such as inaccuracies, unsupported claims, or lack of clarity. Summarize these weaknesses in a short paragraph.\n\n"
                    "Then, improve the following aspects:\n"
                    "1. Technical precision\n"
                    "2. Empirical grounding\n"
                    "3. Theoretical coherence\n\n"
                    "Use a structured difficulty gradient approach (similar to LADDER) to produce a simpler yet more accurate variant, addressing the weaknesses identified."
                )
                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

# Load CLIP components
clip_model = CLIPModel.from_pretrained(ResearchConfig.CLIP_SETTINGS["model"])
clip_processor = CLIPProcessor.from_pretrained(ResearchConfig.CLIP_SETTINGS["model"])

# Update the ExtendedQuantumDocumentManager with the loaded CLIP components
qdm.clip_model = clip_model
qdm.clip_processor = clip_processor

multi_retriever = MultiModalRetriever(retriever.research_retriever, clip_model, clip_processor)

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

if __name__ == "__main__":
    ResearchInterfaceExtended()