app.py

import gradio as gr
import os
import time
import json
import re
from uuid import uuid4
from datetime import datetime
from duckduckgo_search import DDGS
from sentence_transformers import SentenceTransformer, util
from typing import List, Dict, Any, Optional, Union, Tuple
import logging
import numpy as np
from collections import deque
from huggingface_hub import InferenceClient
import requests
import arxiv
import scholarly
import pymed
import wikipedia
import trafilatura
from trafilatura import extract, fetch_url
import pickle
import faiss
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
import tiktoken

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(name)

HF_API_KEY = os.environ.get("HF_API_KEY")
if not HF_API_KEY:
    raise ValueError("Please set the HF_API_KEY environment variable.")

client = InferenceClient(provider="hf-inference", api_key=HF_API_KEY)

MAIN_LLM_MODEL = "mistralai/Mistral-Nemo-Instruct-2407"
REASONING_LLM_MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
CRITIC_LLM_MODEL = "Qwen/QwQ-32B-Preview"
SPECIALIST_MODELS = {
    "medical": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
    "scientific": "mistralai/Mistral-7B-Instruct-v0.2",
    "financial": "mistralai/Mistral-7B-Instruct-v0.3",
    "legal": "Qwen/Qwen2.5-Coder-32B-Instruct"
}
ENSEMBLE_MODELS = [MAIN_LLM_MODEL, REASONING_LLM_MODEL, CRITIC_LLM_MODEL] + list(SPECIALIST_MODELS.values())

MAX_ITERATIONS = 100
TIMEOUT = 300
RETRY_DELAY = 15
NUM_RESULTS = 50
SIMILARITY_THRESHOLD = 0.12
MAX_CONTEXT_ITEMS = 100
MAX_HISTORY_ITEMS = 25
MAX_FULL_TEXT_LENGTH = 50000
FAISS_INDEX_PATH = "research_index.faiss"
RESEARCH_DATA_PATH = "research_data.pkl"
PAPER_SUMMARIES_PATH = "paper_summaries.pkl"

try:
    main_similarity_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
    concept_similarity_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
    document_similarity_model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-dot-v1')

    embedding_dim = document_similarity_model.get_sentence_embedding_dimension()
    if os.path.exists(FAISS_INDEX_PATH):
        index = faiss.read_index(FAISS_INDEX_PATH)
        logger.info(f"Loaded FAISS index from {FAISS_INDEX_PATH}")
    else:
        index = faiss.IndexFlatIP(embedding_dim)
        logger.info("Created a new FAISS index.")

except Exception as e:
    logger.error(f"Failed to load models or initialize FAISS: {e}")
    raise

def get_token_count(text):
    try:
        encoding = tiktoken.get_encoding("cl100k_base")
        return len(encoding.encode(text))
    except:
        return len(text.split()) * 1.3

def save_research_data(data, index):
    try:
        with open(RESEARCH_DATA_PATH, "wb") as f:
            pickle.dump(data, f)
        faiss.write_index(index, FAISS_INDEX_PATH)
        logger.info(f"Research data and index saved to {RESEARCH_DATA_PATH} and {FAISS_INDEX_PATH}")
    except Exception as e:
        logger.error(f"Error saving research data: {e}")

def load_research_data():
    if os.path.exists(RESEARCH_DATA_PATH):
        try:
            with open(RESEARCH_DATA_PATH, "rb") as f:
                data = pickle.load(f)
            logger.info(f"Loaded research data from {RESEARCH_DATA_PATH}")
            return data
        except Exception as e:
            logger.error(f"Error loading research data: {e}")
            return {}
    else:
        logger.info("No existing research data found.")
        return {}

def save_paper_summaries(summaries: Dict[str, str]):
    try:
        with open(PAPER_SUMMARIES_PATH, "wb") as f:
            pickle.dump(summaries, f)
        logger.info(f"Paper summaries saved to {PAPER_SUMMARIES_PATH}")
    except Exception as e:
        logger.error(f"Error saving paper summaries: {e}")

def load_paper_summaries() -> Dict[str, str]:
    if os.path.exists(PAPER_SUMMARIES_PATH):
        try:
            with open(PAPER_SUMMARIES_PATH, "rb") as f:
                data = pickle.load(f)
            logger.info(f"Loaded paper summaries from {PAPER_SUMMARIES_PATH}")
            return data
        except Exception as e:
            logger.error(f"Error loading paper summaries: {e}")
            return {}
    else:
        logger.info("No existing paper summaries found.")
        return {}

def hf_inference(model_name, prompt, max_tokens=2000, retries=5, stream=False):
    for attempt in range(retries):
        try:
            messages = [{"role": "user", "content": prompt}]
            response_generator = client.chat.completions.create(
                model=model_name,
                messages=messages,
                max_tokens=max_tokens,
                stream=stream
            )
            if stream:
                return response_generator
            else:
                response = next(response_generator)
                return {"generated_text": response.choices[0].message.content}
        except Exception as e:
            if attempt == retries - 1:
                logger.error(f"Request failed after {retries} retries: {e}")
                return {"error": f"Request failed after {retries} retries: {e}"}
            time.sleep(RETRY_DELAY * (1 + attempt))
            return {"error": "Request failed after multiple retries."}

def ensemble_inference(prompt, models=ENSEMBLE_MODELS, max_tokens=1500, stream=False):
    results = []

    if stream:
        def generate_responses():
            with ThreadPoolExecutor(max_workers=len(models)) as executor:
                futures = {executor.submit(hf_inference, model, prompt, max_tokens, stream=True): model for model in models}

                for future in as_completed(futures):
                    model = future_to_model[future]
                    try:
                        for chunk in future.result():
                            yield {"model": model, "text": chunk.choices[0].delta.content}
                    except Exception as e:
                        logger.error(f"Error with model {model}: {e}")
                        yield {"model": model, "text": f"Error: {e}"}
        return generate_responses()

    else:
        with ThreadPoolExecutor(max_workers=len(models)) as executor:
            future_to_model = {executor.submit(hf_inference, model, prompt, max_tokens, stream=False): model for model in models}
            for future in as_completed(future_to_model):
                model = future_to_model[future]
                try:
                    result = future.result()
                    if "generated_text" in result:
                        results.append({"model": model, "text": result["generated_text"]})
                except Exception as e:
                    logger.error(f"Error with model {model}: {e}")

        if not results:
            return {"error": "All models failed to generate responses"}

        if len(results) == 1:
            return {"generated_text": results[0]["text"]}

        synthesis_prompt = "Synthesize these expert responses into a single coherent answer:\n\n"
        for result in results:
            synthesis_prompt += f"Expert {results.index(result) + 1} ({result['model'].split('/')[-1]}):\n{result['text']}\n\n"

        synthesis = hf_inference(MAIN_LLM_MODEL, synthesis_prompt)
        if "generated_text" in synthesis:
            return synthesis
        else:
            return {"generated_text": max(results, key=lambda x: len(x["text"]))["text"]}

def tool_search_web(query: str, num_results: int = NUM_RESULTS, safesearch: str = "moderate",
                    time_filter: Optional[str] = None, region: str = "wt-wt", language: str = "en-us") -> list:
    try:
        with DDGS() as ddgs:
            kwargs = {
                "keywords": query,
                "max_results": num_results,
                "safesearch": safesearch,
                "region": region,
                "hreflang": language,
            }
            if time_filter:
                if time_filter in ['d', 'w', 'm', 'y']:
                    kwargs["time"] = time_filter

            results = [r for r in ddgs.text(**kwargs)]
            if results:
                return [{"title": r["title"], "snippet": r["body"], "url": r["href"]} for r in results]
            else:
                if time_filter and "time" in kwargs:
                    del kwargs["time"]
                    results = [r for r in ddgs.text(**kwargs)]
                    if results:
                        return [{"title": r["title"], "snippet": r["body"], "url": r["href"]} for r in results]
                return []
    except Exception as e:
        logger.error(f"DuckDuckGo search error: {e}")
        return []

def tool_search_arxiv(query: str, max_results: int = 5) -> list:
    try:
        client = arxiv.Client()
        search = arxiv.Search(
            query=query,
            max_results=max_results,
            sort_by=arxiv.SortCriterion.Relevance
        )
        results = []
        for paper in client.results(search):
            results.append({
                "title": paper.title,
                "snippet": paper.summary[:500] + "..." if len(paper.summary) > 500 else paper.summary,
                "url": paper.pdf_url,
                "authors": ", ".join(author.name for author in paper.authors),
                "published": paper.published.strftime("%Y-%m-%d") if paper.published else "Unknown",
                "source": "arXiv"
            })
        return results
    except Exception as e:
        logger.error(f"arXiv search error: {e}")
        return []

def tool_search_pubmed(query: str, max_results: int = 5) -> list:
    try:
        pubmed = pymed.PubMed(tool="ResearchAssistant", email="[email protected]")
        results = list(pubmed.query(query, max_results=max_results))

        output = []
        for article in results:
            try:
                data = article.toDict()
                output.append({
                    "title": data.get("title", "No title"),
                    "snippet": data.get("abstract", "No abstract")[:500] + "..." if data.get("abstract", "") and len(data.get("abstract", "")) > 500 else data.get("abstract", "No abstract"),
                    "url": f"https://pubmed.ncbi.nlm.nih.gov/{data.get('pubmed_id')}/",
                    "authors": ", ".join(author.get("name", "") for author in data.get("authors", [])),
                    "published": data.get("publication_date", "Unknown"),
                    "source": "PubMed"
                })
            except:
                continue
        return output
    except Exception as e:
        logger.error(f"PubMed search error: {e}")
        return []

def tool_search_wikipedia(query: str, max_results: int = 3) -> list:
    try:
        search_results = wikipedia.search(query, results=max_results)
        results = []

        for title in search_results:
            try:
                page = wikipedia.page(title)
                summary = page.summary
                snippet = summary[:500] + "..." if len(summary) > 500 else summary
                results.append({
                    "title": page.title,
                    "snippet": snippet,
                    "url": page.url,
                    "source": "Wikipedia"
                })
            except (wikipedia.exceptions.DisambiguationError, wikipedia.exceptions.PageError):
                continue

        return results
    except Exception as e:
        logger.error(f"Wikipedia search error: {e}")
        return []

def tool_search_scholar(query: str, max_results: int = 5) -> list:
    try:
        search_query = scholarly.search_pubs(query)
        results = []
        for _ in range(max_results):
            try:
                result = next(search_query)
                results.append({
                    "title": result.get("bib", {}).get("title", "No title"),
                    "snippet": result.get("bib", {}).get("abstract", "No abstract")[:500] + "..." if result.get("bib", {}).get("abstract") else result.get("bib", {}).get("abstract", "No abstract"),
                    "url": result.get("pub_url", "#"),
                    "authors": ", ".join(result.get("bib", {}).get("author", [])),
                    "published": result.get("bib", {}).get("pub_year", "Unknown"),
                    "source": "Google Scholar"
                })
            except StopIteration:
                break
            except Exception as e:
                logger.warning(f"Error processing Scholar result: {e}")
                continue
        return results
    except Exception as e:
        logger.error(f"Google Scholar search error: {e}")
        return []

def extract_article_content(url: str) -> str:
    try:
        downloaded = fetch_url(url)
        if downloaded is None:
            return ""
        return extract(downloaded, favor_precision=True)
    except Exception as e:
        logger.error(f"Failed to extract article content from {url}: {e}")
        return ""

def tool_reason(prompt: str, search_results: list, reasoning_context: list = [],
              critique: str = "", focus_areas: list = []) -> str:
    if not search_results:
        return "No search results to reason about."

    reasoning_input = "Reason about the following search results in relation to the prompt:\n\n"
    reasoning_input += f"Prompt: {prompt}\n\n"

    if focus_areas:
        reasoning_input += f"Focus particularly on these aspects: {', '.join(focus_areas)}\n\n"

    results_by_source = {}
    for i, result in enumerate(search_results):
        source = result.get('source', 'Web Search')
        if source not in results_by_source:
            results_by_source[source] = []
        results_by_source[source].append((i, result))

    for source, results in results_by_source.items():
        reasoning_input += f"\n--- {source} Results ---\n"
        for i, result in results:
            reasoning_input += f"- Result {i + 1}: Title: {result['title']}\n  Snippet: {result['snippet']}\n"
            if 'authors' in result:
                reasoning_input += f"  Authors: {result['authors']}\n"
            if 'published' in result:
                reasoning_input += f"  Published: {result['published']}\n"
            reasoning_input += "\n"

    if reasoning_context:
        recent_context = reasoning_context[-MAX_HISTORY_ITEMS:]
        reasoning_input += "\nPrevious Reasoning Context:\n" + "\n".join(recent_context)

    if critique:
        reasoning_input += f"\n\nRecent critique to address: {critique}\n"

    reasoning_input += "\nProvide a thorough, nuanced analysis that builds upon previous reasoning if applicable. Consider multiple perspectives, potential contradictions in the search results, and the reliability of different sources.  Address any specific critiques."

    reasoning_output = ensemble_inference(reasoning_input)

    if isinstance(reasoning_output, dict) and "generated_text" in reasoning_output:
        return reasoning_output["generated_text"].strip()
    else:
        logger.error(f"Failed to generate reasoning: {reasoning_output}")
        return "Could not generate reasoning due to an error."

def tool_summarize(insights: list, prompt: str, contradictions: list = []) -> str:
    if not insights:
        return "No insights to summarize."

    summarization_input = f"Synthesize the following insights into a cohesive and comprehensive summary regarding: '{prompt}'\n\n"

    max_tokens = 12000
    selected_insights = []
    token_count = get_token_count(summarization_input) + get_token_count("\n\n".join(contradictions))

    for insight in reversed(insights):
        insight_tokens = get_token_count(insight)
        if token_count + insight_tokens < max_tokens:
            selected_insights.insert(0, insight)
            token_count += insight_tokens
        else:
            break

    summarization_input += "\n\n".join(selected_insights)

    if contradictions:
        summarization_input += "\n\nAddress these specific contradictions:\n" + "\n".join(contradictions)

    summarization_input += "\n\nProvide a well-structured summary that:\n1. Presents the main findings\n2. Acknowledges limitations and uncertainties\n3. Highlights areas of consensus and disagreement\n4. Suggests potential directions for further inquiry\n5. Evaluates the strength of evidence for key claims"

    summarization_output = ensemble_inference(summarization_input)

    if isinstance(summarization_output, dict) and "generated_text" in summarization_output:
        return summarization_output["generated_text"].strip()
    else:
        logger.error(f"Failed to generate summary: {summarization_output}")
        return "Could not generate a summary due to an error."

def tool_generate_search_query(prompt: str, previous_queries: list = [],
                               failed_queries: list = [], focus_areas: list = []) -> str:
    query_gen_input = f"Generate an effective search query for the following prompt: {prompt}\n"

    if previous_queries:
        recent_queries = previous_queries[-MAX_HISTORY_ITEMS:]
        query_gen_input += "Previous search queries:\n" + "\n".join(recent_queries) + "\n"

    if failed_queries:
        query_gen_input += "These queries didn't yield useful results:\n" + "\n".join(failed_queries) + "\n"

    if focus_areas:
        query_gen_input += f"Focus particularly on these aspects: {', '.join(focus_areas)}\n"

    query_gen_input += "Refine the search query based on previous queries, aiming for more precise results. Consider using advanced search operators like site:, filetype:, intitle:, etc. when appropriate. Make sure the query is well-formed for academic and scientific search engines.\n"
    query_gen_input += "Search Query:"

    query_gen_output = hf_inference(MAIN_LLM_MODEL, query_gen_input)

    if isinstance(query_gen_output, dict) and 'generated_text' in query_gen_output:
        return query_gen_output['generated_text'].strip()

    logger.error(f"Failed to generate search query: {query_gen_output}")
    return ""

def tool_critique_reasoning(reasoning_output: str, prompt: str,
                            previous_critiques: list = []) -> str:
    critique_input = f"Critically evaluate the following reasoning output in relation to the prompt:\n\nPrompt: {prompt}\n\nReasoning: {reasoning_output}\n\n"

    if previous_critiques:
        critique_input += "Previous critiques that should be addressed:\n" + "\n".join(previous_critiques[-MAX_HISTORY_ITEMS:]) + "\n\n"

    critique_input += "Identify any flaws, biases, logical fallacies, unsupported claims, or areas for improvement. Be specific and constructive. Suggest concrete ways to enhance the reasoning. Also evaluate the strength of evidence and whether conclusions are proportionate to the available information."

    critique_output = hf_inference(CRITIC_LLM_MODEL, critique_input)

    if isinstance(critique_output, dict) and "generated_text" in critique_output:
        return critique_output["generated_text"].strip()

    logger.error(f"Failed to generate critique: {critique_output}")
    return "Could not generate a critique due to an error."

def tool_identify_contradictions(insights: list) -> list:
    if len(insights) < 2:
        return []

    max_tokens = 12000
    selected_insights = []
    token_count = 0

    for insight in reversed(insights):
        insight_tokens = get_token_count(insight)
        if token_count + insight_tokens < max_tokens:
            selected_insights.insert(0, insight)
            token_count += insight_tokens
        else:
            break

    contradiction_input = "Identify specific contradictions in these insights:\n\n" + "\n\n".join(selected_insights)
    contradiction_input += "\n\nList each contradiction as a separate numbered point. For each contradiction, cite the specific claims that are in tension and evaluate which claim is better supported. If no contradictions exist, respond with 'No contradictions found.'"

    contradiction_output = hf_inference(CRITIC_LLM_MODEL, contradiction_input)

    if isinstance(contradiction_output, dict) and "generated_text" in contradiction_output:
        result = contradiction_output["generated_text"].strip()
        if result == "No contradictions found.":
            return []
        contradictions = re.findall(r'\d+\.\s+(.*?)(?=\d+\.|$)', result, re.DOTALL)
        return [c.strip() for c in contradictions if c.strip()]

    logger.error(f"Failed to identify contradictions: {contradiction_output}")
    return []

def tool_identify_focus_areas(prompt: str, insights: list = [],
                               failed_areas: list = []) -> list:
    focus_input = f"Based on this research prompt: '{prompt}'\n\n"

    if insights:
        recent_insights = insights[-5:] if len(insights) > 5 else insights
        focus_input += "And these existing insights:\n" + "\n".join(recent_insights) + "\n\n"

    if failed_areas:
        focus_input += f"These focus areas didn't yield useful results: {', '.join(failed_areas)}\n\n"

    focus_input += "Identify 3-5 specific aspects that should be investigated further to get a complete understanding. Be precise and prioritize underexplored areas. For each suggested area, briefly explain why it's important to investigate."

    focus_output = hf_inference(MAIN_LLM_MODEL, focus_input)

    if isinstance(focus_output, dict) and "generated_text" in focus_output:
        result = focus_output["generated_text"].strip()
        areas = re.findall(r'(?:^|\n)(?:\d+\.|\*|\-)\s*(.*?)(?=(?:\n(?:\d+\.|\*|\-|$))|$)', result)
        return [area.strip() for area in areas if area.strip()][:5]

    logger.error(f"Failed to identify focus areas: {focus_output}")
    return []

def add_to_faiss_index(text: str):
    embedding = document_similarity_model.encode(text, convert_to_tensor=True)
    embedding_np = embedding.cpu().numpy().reshape(1, -1)
    if embedding_np.shape[1] != embedding_dim:
        logger.error(f"Embedding dimension mismatch: expected {embedding_dim}, got {embedding_np.shape[1]}")
        return
    faiss.normalize_L2(embedding_np)
    index.add(embedding_np)

def search_faiss_index(query: str, top_k: int = 5) -> List[str]:
    query_embedding = document_similarity_model.encode(query, convert_to_tensor=True)
    query_embedding_np = query_embedding.cpu().numpy().reshape(1, -1)
    faiss.normalize_L2(query_embedding_np)
    distances, indices = index.search(query_embedding_np, top_k)
    return indices[0].tolist()

def filter_results(search_results, prompt, previous_snippets=None):
    if not main_similarity_model or not search_results:
        return search_results

    try:
        prompt_embedding = main_similarity_model.encode(prompt, convert_to_tensor=True)
        filtered_results = []

        seen_snippets = set()
        if previous_snippets:
            seen_snippets.update(previous_snippets)

        for result in search_results:
            combined_text = result['title'] + " " + result['snippet']

            if result['snippet'] in seen_snippets:
                continue

            result_embedding = main_similarity_model.encode(combined_text, convert_to_tensor=True)
            cosine_score = util.pytorch_cos_sim(prompt_embedding, result_embedding)[0][0].item()
            
            if cosine_score >= SIMILARITY_THRESHOLD:
                result['relevance_score'] = cosine_score
                filtered_results.append(result)
                seen_snippets.add(result['snippet'])
                add_to_faiss_index(result['snippet'])


        filtered_results.sort(key=lambda x: x.get('relevance_score', 0), reverse=True)
        return filtered_results

    except Exception as e:
        logger.error(f"Error during filtering: {e}")
        return search_results

def tool_extract_key_entities(prompt: str) -> list:
    entity_input = f"Extract the key entities (people, organizations, concepts, technologies, events, time periods, locations, etc.) from this research prompt that should be investigated individually:\n\n{prompt}\n\nList the 5-7 most important entities, one per line, with a brief explanation (2-3 sentences) of why each is central to the research question."

    entity_output = hf_inference(MAIN_LLM_MODEL, entity_input)

    if isinstance(entity_output, dict) and "generated_text" in entity_output:
        result = entity_output["generated_text"].strip()
        entities = [e.strip() for e in result.split('\n') if e.strip()]
        return entities[:7]

    logger.error(f"Failed to extract key entities: {entity_output}")
    return []

def tool_meta_analyze(entity_insights: Dict[str, list], prompt: str) -> str:
    if not entity_insights:
        return "No entity insights to analyze."

    meta_input = f"Perform a meta-analysis across these different entities related to the prompt: '{prompt}'\n\n"

    for entity, insights in entity_insights.items():
        if insights:
            meta_input += f"\n--- {entity} ---\n" + insights[-1] + "\n"

    meta_input += "\nProvide a high-level synthesis that identifies:\n1. Common themes across entities\n2. Important differences and contradictions\n3. How these entities interact or influence each other\n4. The broader implications for the original research question\n5. A systems-level understanding of how these elements fit together"

    meta_output = ensemble_inference(meta_input)

    if isinstance(meta_output, dict) and "generated_text" in meta_output:
        return meta_output["generated_text"].strip()

    logger.error(f"Failed to perform meta-analysis: {meta_output}")
    return "Could not generate a meta-analysis due to an error."

def tool_draft_research_plan(prompt: str, entities: list, focus_areas: list = []) -> str:
    plan_input = f"Create a detailed research plan for investigating this question: '{prompt}'\n\n"

    if entities:
        plan_input += "Key entities to investigate:\n" + "\n".join(entities) + "\n\n"

    if focus_areas:
        plan_input += "Additional focus areas:\n" + "\n".join(focus_areas) + "\n\n"

    plan_input += "The research plan should include:\n"
    plan_input += "1. Main research questions and sub-questions\n"
    plan_input += "2. Methodology for investigating each aspect\n"
    plan_input += "3. Potential sources and databases to consult\n"
    plan_input += "4. Suggested sequence of investigation\n"
    plan_input += "5. Potential challenges and how to address them\n"
    plan_input += "6. Criteria for evaluating the quality of findings"

    plan_output = hf_inference(REASONING_LLM_MODEL, plan_input)

    if isinstance(plan_output, dict) and "generated_text" in plan_output:
        return plan_output["generated_text"].strip()

    logger.error(f"Failed to generate research plan: {plan_output}")
    return "Could not generate a research plan due to an error."

def tool_extract_article(url: str) -> str:
    extracted_text = extract_article_content(url)
    return extracted_text if extracted_text else f"Could not extract content from {url}"

def tool_summarize_paper(paper_text: str) -> str:
    summarization_prompt = f"""Summarize this academic paper, focusing on the following:

Main Research Question(s): What questions does the paper address?

Methodology: Briefly describe the methods used (e.g., experiments, surveys, simulations, theoretical analysis).

Key Findings: What are the most important results or conclusions?

Limitations:  What are the acknowledged limitations of the study?

Implications: What are the broader implications of the findings, according to the authors?
Paper Text:
{paper_text[:MAX_FULL_TEXT_LENGTH]}
"""
    summary = hf_inference(REASONING_LLM_MODEL, summarization_prompt, max_tokens=500)

    if isinstance(summary, dict) and "generated_text" in summary:
        return summary["generated_text"].strip()
    else:
        logger.error(f"Failed to generate summary: {summary}")
        return "Could not generate a summary due to an error."

def tool_search_patents(query: str, max_results: int = 10) -> list:
    pass

def tool_search_clinical_trials(query: str, max_results: int = 10) -> list:
    pass

def tool_search_datasets(query: str, max_results: int = 10) -> list:
    pass

def tool_search_conferences(query: str, max_results: int = 10) -> list:
    pass

tools = {
    "search_web": {
        "function": tool_search_web,
        "description": "Searches the web for information.",
        "parameters": {
            "query": {"type": "string", "description": "The search query."},
            "num_results": {"type": "integer", "description": "Number of results to return."},
            "time_filter": {"type": "string", "description": "Optional time filter (d, w, m, y)."},
            "region": {"type": "string", "description": "Optional region code."},
            "language": {"type": "string", "description": "Optional language code."}
        },
    },
    "search_arxiv": {
        "function": tool_search_arxiv,
        "description": "Searches arXiv for scientific papers.",
        "parameters": {
            "query": {"type": "string", "description": "The search query for scientific papers."},
            "max_results": {"type": "integer", "description": "Maximum number of papers to return."}
        },
    },
    "search_pubmed": {
        "function": tool_search_pubmed,
        "description": "Searches PubMed for medical and scientific literature.",
        "parameters": {
            "query": {"type": "string", "description": "The search query for medical literature."},
            "max_results": {"type": "integer", "description": "Maximum number of articles to return."}
        },
    },
    "search_wikipedia": {
        "function": tool_search_wikipedia,
        "description": "Searches Wikipedia for information.",
        "parameters": {
            "query": {"type": "string", "description": "The search query for Wikipedia."},
            "max_results": {"type": "integer", "description": "Maximum number of articles to return."}
        },
    },
    "search_scholar": {
        "function": tool_search_scholar,
        "description": "Searches Google Scholar for academic publications.",
        "parameters": {
            "query": {"type": "string", "description": "The search query for Google Scholar."},
            "max_results": {"type": "integer", "description": "Maximum number of articles to return."}
        }
    },
    "extract_article": {
        "function": tool_extract_article,
        "description": "Extracts the main content from a web article URL",
        "parameters": {
            "url": {"type": "string", "description": "The URL of the article to extract"}
        },
    },
    "summarize_paper": {
        "function": tool_summarize_paper,
        "description": "Summarizes the content of an academic paper.",
        "parameters": {
            "paper_text": {"type": "string", "description": "The full text of the paper to be summarized."},
        },
    },
    "reason": {
        "function": tool_reason,
        "description": "Analyzes and reasons about information.",
        "parameters": {
            "prompt": {"type": "string", "description": "The original prompt."},
            "search_results": {"type": "array", "description": "Search results to analyze."},
            "reasoning_context": {"type": "array", "description": "Previous reasoning outputs."},
            "critique": {"type": "string", "description": "Recent critique to address."},
            "focus_areas": {"type": "array", "description": "Specific aspects to focus on."}
        },
    },
    "summarize": {
        "function": tool_summarize,
        "description": "Synthesizes insights into a cohesive summary.",
        "parameters": {
            "insights": {"type": "array", "description": "Insights to summarize."},
            "prompt": {"type": "string", "description": "The original research prompt."},
            "contradictions": {"type": "array", "description": "Specific contradictions to address."}
        },
    },
    "generate_search_query": {
        "function": tool_generate_search_query,
        "description": "Generates an optimized search query",
        "parameters":{
            "prompt": {"type": "string", "description": "The original user prompt."},
            "previous_queries": {"type": "array", "description": "Previously used search queries."},
            "failed_queries": {"type": "array", "description": "Queries that didn't yield good results."},
            "focus_areas": {"type": "array", "description": "Specific aspects to focus on."}
        }
    },
    "critique_reasoning": {
        "function": tool_critique_reasoning,
        "description": "Critically evaluates reasoning output.",
        "parameters": {
            "reasoning_output": {"type": "string", "description": "The reasoning output to critique."},
            "prompt": {"type": "string", "description": "The original prompt."},
            "previous_critiques": {"type": "array", "description": "Previous critique outputs."}
        },
    },
    "identify_contradictions": {
        "function": tool_identify_contradictions,
        "description": "Identifies contradictions across multiple insights.",
        "parameters": {
            "insights": {"type": "array", "description": "Collection of insights to analyze for contradictions."},
        },
    },
    "identify_focus_areas": {
        "function": tool_identify_focus_areas,
        "description": "Identifies specific aspects that need further investigation.",
        "parameters": {
            "prompt": {"type": "string", "description": "The original research prompt."},
            "insights": {"type": "array", "description": "Existing insights to build upon."},
            "failed_areas": {"type": "array", "description": "Previously tried areas that yielded poor results."}
        },
    },
    "extract_key_entities": {
        "function": tool_extract_key_entities,
        "description": "Extracts key entities from the prompt for focused research.",
        "parameters": {
            "prompt": {"type": "string", "description": "The original research prompt."}
        },
    },
    "meta_analyze": {
        "function": tool_meta_analyze,
        "description": "Performs meta-analysis across entity-specific insights.",
        "parameters": {
            "entity_insights": {"type": "object", "description": "Dictionary mapping entities to their insights."},
            "prompt": {"type": "string", "description": "The original research prompt."}
        },
    },
    "draft_research_plan": {
        "function": tool_draft_research_plan,
        "description": "Creates a detailed research plan.",
        "parameters": {
            "prompt": {"type": "string", "description": "The research question/prompt."},
            "entities": {"type": "array", "description": "Key entities to investigate."},
            "focus_areas": {"type": "array", "description": "Additional areas to focus on."}
        }
    },
    "search_patents": {
        "function": tool_search_patents,
        "description": "Searches patent databases globally",
        "parameters": {
            "query": {"type": "string", "description": "Patent search query"},
            "max_results": {"type": "integer", "description": "Maximum number of patents to return"}
        }
    },
    "search_clinical_trials": {
        "function": tool_search_clinical_trials,
        "description": "Search ClinicalTrials.gov and WHO ICTRP",
        "parameters": {
            "query": {"type": "string", "description": "Search query for ClinicalTrials.gov and WHO ICTRP"},
            "max_results": {"type": "integer", "description": "Maximum number of results to return"}
        }
    },
    "search_datasets": {
        "function": tool_search_datasets,
        "description": "Search academic datasets from repositories like Kaggle, UCI, etc.",
        "parameters": {
            "query": {"type": "string", "description": "Search query for academic datasets"},
            "max_results": {"type": "integer", "description": "Maximum number of results to return"}
        }
    },
    "search_conferences": {
        "function": tool_search_conferences,
        "description": "Search major conference proceedings",
        "parameters": {
            "query": {"type": "string", "description": "Search query for conference proceedings"},
            "max_results": {"type": "integer", "description": "Maximum number of results to return"}
        }
    }
}

def create_prompt(task_description, user_input, available_tools, context):
    prompt = f"""{task_description}
User Input:
{user_input}
Available Tools:
"""
    for tool_name, tool_data in available_tools.items():
        prompt += f"- {tool_name}: {tool_data['description']}\n"
        prompt += "  Parameters:\n"
        for param_name, param_data in tool_data["parameters"].items():
            prompt += f"    - {param_name} ({param_data['type']}): {param_data['description']}\n"

    recent_context = context[-MAX_CONTEXT_ITEMS:] if len(context) > MAX_CONTEXT_ITEMS else context

    prompt += "\nContext (most recent items):\n"
    for item in recent_context:
        prompt += f"- {item}\n"

    prompt += """
Instructions:
Select the BEST tool and parameters for the current research stage. Output valid JSON. If no tool is appropriate, respond with {}.
Only use provided tools. Be strategic about which tool to use next based on the research progress so far.
You MUST be methodical.  Think step-by-step:

Plan: If it's the very beginning, extract key entities, identify focus areas, and then draft a research plan.

Search:  Use a variety of search tools.  Start with broad searches, then narrow down.  Use specific search tools (arXiv, PubMed, Scholar) for relevant topics.

Analyze:  Reason deeply about search results, and critique your reasoning.  Identify contradictions. Filter and use FAISS index for relevant information.

Refine: If results are poor, generate better search queries. Adjust focus areas.

Iterate: Repeat steps 2-4, focusing on different entities and aspects.

Synthesize:  Finally, summarize the findings, addressing contradictions.
Example:
{"tool": "search_web", "parameters": {"query": "Eiffel Tower location"}}
Output:
"""
    return prompt

def deep_research(prompt):
    task_description = "You are an advanced research assistant, designed to be as comprehensive as possible.  Use available tools iteratively, focus on different aspects, explore promising leads thoroughly, critically evaluate your findings, and build up a comprehensive understanding of the research topic. Utilize the FAISS index to avoid redundant searches and to build a persistent knowledge base."
    research_data = load_research_data()
    paper_summaries = load_paper_summaries()

    context = research_data.get('context', [])
    all_insights = research_data.get('all_insights', [])
    entity_specific_insights = research_data.get('entity_specific_insights', {})
    intermediate_output = ""
    previous_queries = research_data.get('previous_queries', [])
    failed_queries = research_data.get('failed_queries', [])
    reasoning_context = research_data.get('reasoning_context', [])
    previous_critiques = research_data.get('previous_critiques', [])
    focus_areas = research_data.get('focus_areas', [])
    failed_areas = research_data.get('failed_areas', [])
    seen_snippets = set(research_data.get('seen_snippets', []))
    contradictions = research_data.get('contradictions', [])
    research_session_id = research_data.get('research_session_id', str(uuid4()))

    global index
    if research_data:
        logger.info("Restoring FAISS Index from loaded data.")
    else:
        index.reset()
        logger.info("Initialized a fresh FAISS Index")

    key_entities_with_descriptions = tool_extract_key_entities(prompt=prompt)
    key_entities = [e.split(":")[0].strip() for e in key_entities_with_descriptions]
    if key_entities:
        context.append(f"Identified key entities: {key_entities}")
        intermediate_output += f"Identified key entities for focused research: {key_entities_with_descriptions}\n"
        yield "Identifying key entities... (Completed)"

    entity_progress = {entity: {'queries': [], 'insights': []} for entity in key_entities}
    entity_progress['general'] = {'queries': [], 'insights': []}
    for entity in key_entities + ['general']:
        if entity in research_data:
            entity_progress[entity]['queries'] = research_data[entity]['queries']
            entity_progress[entity]['insights'] = research_data[entity]['insights']

    if not focus_areas:
        initial_focus_areas = tool_identify_focus_areas(prompt=prompt)
        yield "Identifying initial focus areas...(Completed)"
        research_plan = tool_draft_research_plan(prompt=prompt, entities=key_entities, focus_areas=initial_focus_areas)
        yield "Drafting initial research plan...(Completed)"
        context.append(f"Initial Research Plan: {research_plan[:200]}...")
        intermediate_output += f"Initial Research Plan:\n{research_plan}\n\n"
        focus_areas = initial_focus_areas


    for i in range(MAX_ITERATIONS):
        if key_entities and i > 0:
            entities_to_process = key_entities + ['general']
            current_entity = entities_to_process[i % len(entities_to_process)]
        else:
            current_entity = 'general'

        context.append(f"Current focus: {current_entity}")
        
        if i > 0:
            faiss_results_indices = search_faiss_index(prompt if current_entity == 'general' else f"{prompt} {current_entity}")
            faiss_context = []
            for idx in faiss_results_indices:
                if idx < len(all_insights):
                    faiss_context.append(f"Previously found insight: {all_insights[idx]}")
            if faiss_context:
                context.extend(faiss_context)
                intermediate_output += f"Iteration {i+1} - Retrieved {len(faiss_context)} relevant items from FAISS index.\n"
            

        if i == 0:
            initial_query = tool_generate_search_query(prompt=prompt)
            yield f"Generating initial search query... (Iteration {i+1})"
            if initial_query:
                previous_queries.append(initial_query)
                entity_progress['general']['queries'].append(initial_query)

                with ThreadPoolExecutor(max_workers=5) as executor:
                    futures = [
                        executor.submit(tool_search_web, query=initial_query, num_results=NUM_RESULTS),
                        executor.submit(tool_search_arxiv, query=initial_query, max_results=5),
                        executor.submit(tool_search_pubmed, query=initial_query, max_results=5),
                        executor.submit(tool_search_wikipedia, query=initial_query, max_results=3),
                        executor.submit(tool_search_scholar, query=initial_query, max_results=5)
                    ]

                    search_results = []
                    for future in as_completed(futures):
                        search_results.extend(future.result())
                yield f"Performing initial searches... (Iteration {i+1})"

                filtered_search_results = filter_results(search_results, prompt)

                if filtered_search_results:
                    context.append(f"Initial Search Results: {len(filtered_search_results)} items found")
                    reasoning_output = tool_reason(prompt, filtered_search_results)
                    yield f"Reasoning about initial search results... (Iteration {i+1})"
                    if reasoning_output:
                        all_insights.append(reasoning_output)
                        entity_progress['general']['insights'].append(reasoning_output)
                        reasoning_context.append(reasoning_output)
                        context.append(f"Initial Reasoning: {reasoning_output[:200]}...")
                        add_to_faiss_index(reasoning_output)
                else:
                    failed_queries.append(initial_query)
                    context.append(f"Initial query yielded no relevant results: {initial_query}")

        elif current_entity != 'general':
            entity_query = tool_generate_search_query(
                prompt=f"{prompt} focusing specifically on {current_entity}",
                previous_queries=entity_progress[current_entity]['queries'],
                focus_areas=focus_areas
            )
            yield f"Generating search query for entity: {current_entity}... (Iteration {i+1})"

            if entity_query:
                previous_queries.append(entity_query)
                entity_progress[current_entity]['queries'].append(entity_query)


                with ThreadPoolExecutor(max_workers=5) as executor:
                    futures = [
                        executor.submit(tool_search_web, query=entity_query, num_results=NUM_RESULTS//2),
                        executor.submit(tool_search_arxiv, query=entity_query, max_results=3),
                        executor.submit(tool_search_pubmed, query=entity_query, max_results=3),
                        executor.submit(tool_search_wikipedia, query=entity_query, max_results=2),
                        executor.submit(tool_search_scholar, query=entity_query, max_results=3)
                    ]

                    search_results = []
                    for future in as_completed(futures):
                        search_results.extend(future.result())

            yield f"Searching for information on entity: {current_entity}... (Iteration {i+1})"
            filtered_search_results = filter_results(search_results,
                                                        f"{prompt} {current_entity}",
                                                        previous_snippets=seen_snippets)

            if filtered_search_results:
                context.append(f"Entity Search for {current_entity}: {len(filtered_search_results)} results")

                entity_reasoning = tool_reason(
                    prompt=f"{prompt} focusing on {current_entity}",
                    search_results=filtered_search_results,
                    reasoning_context=entity_progress[current_entity]['insights'],
                    focus_areas=focus_areas
                )
                yield f"Reasoning about entity: {current_entity}... (Iteration {i+1})"

                if entity_reasoning:
                    all_insights.append(entity_reasoning)
                    entity_progress[current_entity]['insights'].append(entity_reasoning)

                    if current_entity not in entity_specific_insights:
                        entity_specific_insights[current_entity] = []
                    entity_specific_insights[current_entity].append(entity_reasoning)

                    context.append(f"Reasoning about {current_entity}: {entity_reasoning[:200]}...")
                    add_to_faiss_index(entity_reasoning)
            else:
                failed_queries.append(entity_query)
                context.append(f"Entity query for {current_entity} yielded no relevant results")

        llm_prompt = create_prompt(task_description, prompt, tools, context)
        llm_response = hf_inference(MAIN_LLM_MODEL, llm_prompt, stream=True)

        if isinstance(llm_response, dict) and "error" in llm_response:
            intermediate_output += f"LLM Error: {llm_response['error']}\n"
            yield f"LLM Error (Iteration {i+1}): {llm_response['error']}"
            continue

        response_text = ""
        try:
            for chunk in llm_response:
                if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
                    response_text += chunk.choices[0].delta.content
                    yield f"Iteration {i+1} - Thinking... {response_text}"

        except Exception as e:
            intermediate_output += f"Streaming Error: {str(e)}\n"
            yield f"Streaming Error (Iteration {i+1}): {str(e)}"
            continue

        try:
            response_json = json.loads(response_text)
            intermediate_output += f"Iteration {i+1} - Focus: {current_entity} - Action: {response_text}\n"
        except json.JSONDecodeError:
            intermediate_output += f"Iteration {i+1} - LLM Response (Invalid JSON): {response_text[:100]}...\n"
            context.append(f"Invalid JSON: {response_text[:100]}...")
            continue

        tool_name = response_json.get("tool")
        parameters = response_json.get("parameters", {})

        if not tool_name:
            if all_insights:
                if i > MAX_ITERATIONS // 2:
                    break
            continue

        if tool_name not in tools:
            context.append(f"Invalid tool: {tool_name}")
            intermediate_output += f"Iteration {i + 1} - Invalid tool chosen: {tool_name}\n"
            continue

        tool = tools[tool_name]
        try:
            intermediate_output += f"Iteration {i+1} - Executing: {tool_name}, Key params: {str(parameters)[:100]}...\n"

            if tool_name == "generate_search_query":
                parameters['previous_queries'] = previous_queries
                parameters['failed_queries'] = failed_queries
                parameters['focus_areas'] = focus_areas
                result = tool["function"](**parameters)
                yield f"Iteration {i+1} - Generated search query: {result}"

                if current_entity != 'general':
                    entity_progress[current_entity]['queries'].append(result)

                previous_queries.append(result)

            elif tool_name in ["search_web", "search_arxiv", "search_pubmed", "search_wikipedia", "search_scholar"]:
                result = tool["function"](**parameters)
                search_prompt = prompt
                if current_entity != 'general':
                    search_prompt = f"{prompt} focusing on {current_entity}"

                filtered_result = filter_results(result, search_prompt, previous_snippets=seen_snippets)

                result = filtered_result

                if not result and 'query' in parameters:
                    failed_queries.append(parameters['query'])

            elif tool_name == "reason":
                if current_entity != 'general' and 'reasoning_context' not in parameters:
                    parameters['reasoning_context'] = entity_progress[current_entity]['insights']
                elif 'reasoning_context' not in parameters:
                    parameters['reasoning_context'] = reasoning_context[:]

                if 'prompt' not in parameters:
                    if current_entity != 'general':
                        parameters['prompt'] = f"{prompt} focusing on {current_entity}"
                    else:
                        parameters['prompt'] = prompt

                if 'search_results' not in parameters:
                    parameters['search_results'] = []

                if 'focus_areas' not in parameters and focus_areas:
                    parameters['focus_areas'] = focus_areas

                result = tool["function"](**parameters)
                yield f"Iteration {i+1} - Reasoning about information..."

                if current_entity != 'general':
                    entity_progress[current_entity]['insights'].append(result)
                    if current_entity not in entity_specific_insights:
                        entity_specific_insights[current_entity] = []
                    entity_specific_insights[current_entity].append(result)
                else:
                    reasoning_context.append(result)
                add_to_faiss_index(result)
                all_insights.append(result)

            elif tool_name == "critique_reasoning":
                if 'previous_critiques' not in parameters:
                    parameters['previous_critiques'] = previous_critiques

                if all_insights:
                    if 'reasoning_output' not in parameters:
                        parameters['reasoning_output'] = all_insights[-1]
                    if 'prompt' not in parameters:
                        parameters['prompt'] = prompt

                    result = tool["function"](**parameters)
                    yield f"Iteration {i+1} - Critiquing reasoning..."
                    previous_critiques.append(result)
                    context.append(f"Critique: {result[:200]}...")
                else:
                    result = "No reasoning to critique yet."

            elif tool_name == "identify_contradictions":
                result = tool["function"](**parameters)
                yield f"Iteration {i+1} - Identifying contradictions..."
                if result:
                    contradictions = result
                    context.append(f"Identified contradictions: {result}")

            elif tool_name == "identify_focus_areas":
                if 'failed_areas' not in parameters:
                    parameters['failed_areas'] = failed_areas
                result = tool["function"](**parameters)
                yield f"Iteration {i+1} - Identifying focus areas..."
                if result:
                    old_focus = set(focus_areas)
                    focus_areas = result
                    failed_areas.extend([area for area in old_focus if area not in result])
                    context.append(f"New focus areas: {result}")

            elif tool_name == "extract_article":
                result = tool["function"](**parameters)
                yield f"Iteration {i+1} - Extracting article content..."
                if result:
                    context.append(f"Extracted article content from {parameters['url']}: {result[:200]}...")
                    reasoning_about_article = tool_reason(prompt=prompt, search_results=[{"title": "Extracted Article", "snippet": result, "url": parameters['url']}])
                    if reasoning_about_article:
                        all_insights.append(reasoning_about_article)
                        add_to_faiss_index(reasoning_about_article)
        
            elif tool_name == "summarize_paper":
                result = tool["function"](**parameters)
                yield f"Iteration {i+1} - Summarizing paper..."
                if result:
                    paper_summaries[parameters['paper_text'][:100]] = result
                    save_paper_summaries(paper_summaries)
                    context.append(f"Summarized paper: {result[:200]}...")
                    add_to_faiss_index(result)
                    all_insights.append(result)

            elif tool_name == "meta_analyze":
                if 'entity_insights' not in parameters:
                    parameters['entity_insights'] = entity_specific_insights
                if 'prompt' not in parameters:
                    parameters['prompt'] = prompt
                result = tool["function"](**parameters)
                yield f"Iteration {i+1} - Performing meta-analysis..."
                if result:
                    all_insights.append(result)
                    context.append(f"Meta-analysis across entities: {result[:200]}...")
                    add_to_faiss_index(result)


            elif tool_name == "draft_research_plan":
                result = "Research plan already generated."

            else:
                result = tool["function"](**parameters)

            result_str = str(result)
            if len(result_str) > 500:
                result_str = result_str[:500] + "..."

            intermediate_output += f"Iteration {i+1} - Result: {result_str}\n"

            result_context = result_str
            if len(result_str) > 300:
                result_context = result_str[:300] + "..."
            context.append(f"Used: {tool_name}, Result: {result_context}")

        except Exception as e:
            logger.error(f"Error with {tool_name}: {str(e)}")
            context.append(f"Error with {tool_name}: {str(e)}")
            intermediate_output += f"Iteration {i+1} - Error: {str(e)}\n"
            continue

        research_data = {
            'context': context,
            'all_insights': all_insights,
            'entity_specific_insights': entity_specific_insights,
            'previous_queries': previous_queries,
            'failed_queries': failed_queries,
            'reasoning_context': reasoning_context,
            'previous_critiques': previous_critiques,
            'focus_areas': focus_areas,
            'failed_areas': failed_areas,
            'seen_snippets': list(seen_snippets),
            'contradictions': contradictions,
            'research_session_id': research_session_id
        }
        for entity in entity_progress:
            research_data[entity] = entity_progress[entity]
        save_research_data(research_data, index)

    if len(entity_specific_insights) > 1 and len(all_insights) > 2:
        meta_analysis = tool_meta_analyze(entity_insights=entity_specific_insights, prompt=prompt)
        if meta_analysis:
            all_insights.append(meta_analysis)
            intermediate_output += f"Final Meta-Analysis: {meta_analysis[:500]}...\n"
            add_to_faiss_index(meta_analysis)

    if all_insights:
        final_result = tool_summarize(all_insights, prompt, contradictions)
    else:
        final_result = "Could not find meaningful information despite multiple attempts."

    full_output = f"**Research Prompt:** {prompt}\n\n"

    if key_entities_with_descriptions:
        full_output += f"**Key Entities Identified:**\n"
        for entity in key_entities_with_descriptions:
            full_output += f"- {entity}\n"
        full_output += "\n"

    full_output += "**Research Process:**\n" + intermediate_output + "\n"

    if contradictions:
        full_output += "**Contradictions Identified:**\n"
        for i, contradiction in enumerate(contradictions, 1):
            full_output += f"{i}. {contradiction}\n"
        full_output += "\n"

    full_output += f"**Final Analysis:**\n{final_result}\n\n"

    full_output += f"Research Session ID: {research_session_id}\n"
    full_output += f"Completed at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n"
    full_output += f"Total iterations: {i+1}\n"
    full_output += f"Total insights generated: {len(all_insights)}\n"

    yield full_output

custom_css = """
/* Modern Research Interface */
.research-container {
    display: grid;
    grid-template-columns: 2fr 1fr;
    gap: 20px;
    padding: 20px;
    background: #1a1a1a;
}

.main-content {
    background: #2d2d2d;
    border-radius: 10px;
    padding: 20px;
}

.sidebar {
    background: #2d2d2d;
    border-radius: 10px;
    padding: 20px;
}

/* Progress Tracking */
.progress-container {
    background: #333;
    border-radius: 8px;
    padding: 15px;
    margin-bottom: 15px;
}

.tool-indicator {
    display: flex;
    align-items: center;
    gap: 10px;
    padding: 8px;
    background: #444;
    border-radius: 5px;
    margin-bottom: 8px;
}

.tool-icon {
    width: 24px;
    height: 24px;
}

.tool-name {
    color: #4CAF50;
    font-weight: bold;
}

/* Enhanced Output Formatting */
.research-output {
    font-family: 'Inter', sans-serif;
    line-height: 1.6;
}

.research-output h2 {
    color: #4CAF50;
    border-bottom: 2px solid #4CAF50;
    padding-bottom: 5px;
}

.research-output code {
    background: #333;
    padding: 2px 6px;
    border-radius: 4px;
}

/* Statistics Panel */
.stats-panel {
    background: #333;
    border-radius: 8px;
    padding: 15px;
    margin-top: 15px;
}

.stat-item {
    display: flex;
    justify-content: space-between;
    padding: 8px 0;
    border-bottom: 1px solid #444;
}
"""

iface = gr.Interface(
    fn=deep_research,
    inputs=[
        gr.Textbox(lines=5, placeholder="Enter your research question...", label="Research Question"),
        gr.Dropdown(
            choices=["Quick", "Standard", "Comprehensive", "Exhaustive"],
            label="Research Depth",
            value="Standard"
        ),
        gr.CheckboxGroup(
            choices=["Academic Papers", "Patents", "News", "Clinical Trials", "Datasets"],
            label="Source Types",
            value=["Academic Papers", "News"]
        ),
        gr.Slider(
            minimum=1,
            maximum=24,
            value=2,
            label="Time Limit (hours)"
        )
    ],
    outputs=[
        gr.Markdown(label="Research Results", elem_classes=["research-output"]),
        gr.JSON(label="Progress Statistics", elem_classes=["stats-panel"]),
        gr.HTML(label="Active Tools", elem_classes=["tool-indicator"])
    ],
    title="Advanced Research Institution Assistant",
    description="""Enterprise-grade research system with real-time progress tracking, 
    comprehensive source coverage, and advanced analysis capabilities.""",
    theme=gr.themes.Base(primary_hue="green"),
    css=custom_css
)

if __name__ == "__main__":
    iface.launch(share=False)