intake_function.py

from input_parsing import start_ner, start_embedding, intake, define_tool_hash, tool_descriptions_values, second_intake, parse_input
from input_parsing import define_tool_reqs, smiles_regex
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
from PIL import Image
import io, json, pprint as pp
from scholarly import scholarly, ProxyGenerator
import numpy as np
import ast

# imports for HF Spaces
import torch
import gradio as gr
# end imports for HF Spaces

device = "cuda" if torch.cuda.is_available() else "cpu"

def start_models():
    '''
    Starts the necessary models for processing.
    Returns:
        parse_model: The NER model.
        document_embeddings: The encoded document embeddings.
        embed_model: The embedding model.
    '''
    parse_model = start_ner()
    document_embeddings, embed_model = start_embedding(tool_descriptions_values)
    return parse_model, document_embeddings, embed_model

class chat_manager():
  '''
  '''
  def __init__(self, model_id: str = 'google/gemma-3-1b-it', device: str = 'cuda'):
    '''
    '''
    self.model_id = model_id
    self.device = device
    self.chat_idx = 0

    #check to see if chat history file exists, if so load it
    try:
      with open('chat_session_history.txt', 'r') as f:
        data_string = f.read()
        self.chat_history = ast.literal_eval(data_string)
    except:
      self.chat_history = []

  def start_model_tokenizer(self):
    '''
      Downloads and loads the model and tokenizer.

      Args:
        None
      Returns:
        None
      Also defines:
        model: The model to use.
        tokenizer: The tokenizer to use.
    '''
    quantization_config = BitsAndBytesConfig(load_in_8bit=True)

    self.llm_model = AutoModelForCausalLM.from_pretrained(
        self.model_id, quantization_config=quantization_config).eval()

    self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)

    print(f"Model loaded on {self.device}")

  def start_support_models(self):
    '''
    Starts the supporting models for parsing and embedding.
    '''

    self.parse_model, self.document_embeddings, self.embed_model = start_models()
  
  def reset_chat(self):
    '''
    Resets the chat state.
    '''
    self.chat_idx = 0
    self.best_tools = []
    self.proteins_list = []
    self.names_list = []
    self.smiles_list = []
    self.uniprot_list = []
    self.pdb_list = []
    self.chembl_list = []
    self.query = ''
    self.present = []
  
  def hard_reset_chat(self):
    '''
    Resets the chat state.
    '''
    self.chat_idx = 0
    self.best_tools = []
    self.proteins_list = []
    self.names_list = []
    self.smiles_list = []
    self.uniprot_list = []
    self.pdb_list = []
    self.chembl_list = []
    self.query = ''
    self.present = []
    self.chat_history = []

  def chat(self, query: str, mode_flag: str = 'AI'):
    '''
      Chats with the model.

      Args:
        query: The prompt to send to the model.
        mode_flag: The mode to use (AI, Manual, Web Search, Chat).
      Returns:
        chat_history: The chat history.
    '''
    ''' ===============================================================================================
    Handle Web Search Mode  - 
    if user chooses web search mode, send query to websearch node and return results
    ==================================================================================================='''
    if mode_flag == 'Web Search':
      self.chat_idx = 0
      local_chat_history = []
      local_chat_history.append(query)

      top_hits, search_string, _ = websearch_node(query, self.embed_model)
      local_chat_history.append(search_string)
      self.chat_history.append(local_chat_history)
      with open('chat_session_history.txt', 'w') as f:
        pp.pp(self.chat_history, stream=f)

      return '', self.chat_history, None

    ''' =============================================================================================
    Handle Chat Mode  -
    if user chooses chat mode, send query to LLM and return response
    ================================================================================================='''
    if mode_flag == 'Chat':
      self.chat_idx  = 0
      local_chat_history = []
      local_chat_history.append(query)
      self.query = query

      context = 'Previous chat history: '
      for chat in self.chat_history:
          for turn in chat:
            context += '\n' + turn


      role_text = f"You are part of a drug design agent. Answer user questions to the best of your ability. \
If the user asks any innapropriate questions, respond with 'I'm sorry, I can't assist with that request.' \
If the user asks for general information, provide a concise and accurate answer. If the user asks about drug design, \
provide detailed and informative answers or refer them to the tools. They can access the tools by switching to AI or \
manual mode. Reference the previous conversation in the context if needed."

      prompt = f'Query: {self.query}; CONTEXT: {context}.'

      messages = [[{
                  "role": "system",
                  "content": [{"type": "text", "text": role_text},]
              },{
                  "role": "user",
                  "content": [{"type": "text", "text": prompt},]
              }]]

      inputs = self.tokenizer.apply_chat_template(
          messages,
          add_generation_prompt=True,
          tokenize=True,
          return_dict=True,
          return_tensors="pt",
      ).to(self.llm_model.device) #.to(torch.bfloat16)

      with torch.inference_mode():
        outputs = self.llm_model.generate(**inputs, max_new_tokens=500, do_sample=True, temperature=0.5)

      outputs = self.tokenizer.batch_decode(outputs, )

      parts = outputs[0].split('<start_of_turn>model')
      response = parts[1].strip('\n').strip('<end_of_turn>')
      self.latest_response = response
      local_chat_history.append(response)
      self.chat_history.append(local_chat_history)
      with open('chat_session_history.txt', 'w') as f:
        pp.pp(self.chat_history, stream=f)
      
      return '', self.chat_history, None

    '''=============================================================================================
    AI Mode: not chat or web search - 
    First interaction: get best tools and parse entities
    ============================================================================================='''
    if self.chat_idx == 0:
      #self.chat_history = []
      local_chat_history = []
      local_chat_history.append(query)

      '''sends the query to the intake function to get best tools and parsed entities'''
      self.query = query
      self.best_tools, self.present, self.proteins_list, self.names_list, self.smiles_list, self.uniprot_list, self.pdb_list, self.chembl_list = \
      intake(self.query, self.parse_model, self.embed_model, self.document_embeddings)

      response = '## The tools chosen based on your query are:'
      for i,tool in enumerate(self.best_tools):
        response += '\n' + f'{i+1}. {tool} : {full_tool_descriptions[tool]}'

      response += ' \n\n ## And the following information was found in your query:\n'
      for (entity_type, entity_list) in zip(self.present, [self.proteins_list, self.names_list, self.smiles_list, self.uniprot_list, self.pdb_list, self.chembl_list]):
        if self.present[entity_type] > 0:
          response += f'**{entity_type}**: {self.present[entity_type]}\n'
          for ent_idx, entity in enumerate(entity_list):
            response += f'- **{ent_idx+1}. {entity_type}**: {entity}\n'
      response += '\n To accept the #1 tool choice, hit enter; to choose 2 or 3, enter that number.'
      response += '\n To edit the items in a list, enter "edit".'
      response += '\n To start over, click the clear button and enter a new query.' 
      self.chat_idx += 1

      matches = smiles_regex(response)
      for m in matches:
        if m in response:
          response = response.replace(m, f'```{m}```')

      local_chat_history.append(response)
      self.chat_history.append(local_chat_history)
      with open('chat_session_history.txt', 'w') as f:
        pp.pp(self.chat_history, stream=f)

      return '', self.chat_history, None

    elif self.chat_idx == 1:
      '''=============================================================================================
      AI Mode: not chat or web search - 
       Second interaction: get tool choice and call tool function, return results
       either directly (manual mode) or via LLM (AI mode)
      ============================================================================================='''
      local_chat_history = []
      local_chat_history.append(query)

      ''' ===============================================================================================
      if the user has chosen to edit a list, go to edit list step 501
      =============================================================================================='''
      if 'edit' in query.lower():
        self.chat_idx = 501
      
        list_list = ['smiles list', 'names list', 'proteins list', 'uniprot list', 'pdb list', 'chembl list']
        response = '## Enter the list to edit:\n'
        for i, list_name in enumerate(list_list):
          response += f'**{i+1}**. {list_name}\n'
        
        local_chat_history.append(response)
        self.chat_history.append(local_chat_history)
        with open('chat_session_history.txt', 'w') as f:
          pp.pp(self.chat_history, stream=f)
        return '', self.chat_history, None
      
      elif query == '':
        self.tool_choice = 0
      elif query in ['1','2','3']:
        self.tool_choice = int(query) - 1
      else:
        ''' ===============================================================================================
        In the case that the user enters an invalid tool choice, return to tool choice step
        ==================================================================================================='''
        response = 'Invalid input. Please enter 1, 2, or 3 to choose one of the tools above, or hit enter to accept the #1 tool choice. \
or enter "edit" to edit a list.'
        local_chat_history.append(response)
        self.chat_history.append(local_chat_history)
        with open('chat_session_history.txt', 'w') as f:
          pp.pp(self.chat_history, stream=f)

        self.chat_idx = 1
        return '', self.chat_history, None
      
      ''' ==============================================================================================
      Check that the necessary data is present for the chosen tool
      ask user for missing data if not
      ================================================================================================='''
      tool_function_reqs = define_tool_reqs(self.best_tools[self.tool_choice], self.proteins_list,
                                            self.names_list, self.smiles_list, self.uniprot_list, self.pdb_list, self.chembl_list)
      data_request = f'The necessary data was not found for tool {self.best_tools[self.tool_choice]}.\n'
      missing_data = False
      reqs_list = tool_function_reqs[self.best_tools[self.tool_choice]][0]
      list_names = tool_function_reqs[self.best_tools[self.tool_choice]][1]

      for sub_list, list_name in zip(reqs_list, list_names):
        if len(sub_list) == 0:
          data_request += f'Missing information for: *{list_name}*.\n'
          missing_data = True
      data_request += 'Please provide the necessary information to proceed.'
      if missing_data:
        local_chat_history.append(data_request)
        self.chat_history.append(local_chat_history)
        with open('chat_session_history.txt', 'w') as f:
          pp.pp(self.chat_history, stream=f)
        self.chat_idx = 999
        return '', self.chat_history, None
      ''' ====================================================================================================
      End data check: if not missing data, call tool function 
      ====================================================================================================='''

      ''' Get the chosen tool function and args, call it, and get results'''
      tool_function_hash = define_tool_hash(self.best_tools[self.tool_choice], self.proteins_list,
                                            self.names_list, self.smiles_list, self.uniprot_list, self.pdb_list, self.chembl_list)

      args_list = tool_function_hash[self.best_tools[self.tool_choice]][1]
      results_tuple  = tool_function_hash[self.best_tools[self.tool_choice]][0](*args_list)

      results_list, self.results_string, self.results_images = results_tuple
      print(self.results_string)

      '''=============================================================================================
      If manual mode, return results directly; if AI mode, send results to LLM for response generation
      ============================================================================================='''
      if mode_flag == 'Manual':
        
        matches = smiles_regex(self.results_string)

        for m in matches:
          if m in self.results_string:
            self.results_string = self.results_string.replace(m, f'```{m}```')

        local_chat_history.append(self.results_string)
        self.chat_history.append(local_chat_history)
        with open('chat_session_history.txt', 'w') as f:
          pp.pp(self.chat_history, stream=f)
        try:
          img = Image.open(io.BytesIO(self.results_images[0].data))
        except:
          img = None
        
        self.reset_chat()

        return '', self.chat_history, img

      ''' ============================================================================================= 
      AI mode: send results to LLM for response generation
      ============================================================================================='''
      role_text = "Answer the query using the information in the context. Add explanations \
or enriching information where appropriate."

      prompt = f'Query: {self.query}.\n Context: {self.results_string}'

      messages = [[{
                  "role": "system",
                  "content": [{"type": "text", "text": role_text},]
              },{
                  "role": "user",
                  "content": [{"type": "text", "text": prompt},]
              }]]

      inputs = self.tokenizer.apply_chat_template(
          messages,
          add_generation_prompt=True,
          tokenize=True,
          return_dict=True,
          return_tensors="pt",
      ).to(self.llm_model.device) #.to(torch.bfloat16)

      with torch.inference_mode():
          outputs = self.llm_model.generate(**inputs, max_new_tokens=500, do_sample=True, temperature=0.5)

      outputs = self.tokenizer.batch_decode(outputs, )

      parts = outputs[0].split('<start_of_turn>model')
      response = parts[1].strip('\n').strip('<end_of_turn>')
      self.latest_response = response

      matches = smiles_regex(response)
      for m in matches:
          if m in response:
              response = response.replace(m, f'```{m}```')

      local_chat_history.append(response)
      self.chat_history.append(local_chat_history)
      with open('chat_session_history.txt', 'w') as f:
        pp.pp(self.chat_history, stream=f)
      self.chat_idx += 1

      # self.reset_chat()

      #convert self.results_images[0] from ipython display to an image for gradio
      try:
        img = Image.open(io.BytesIO(self.results_images[0].data))
      except:
        img = None

      if img != None:
        return '', self.chat_history, img
      else:
        return '', self.chat_history, None
    
    elif self.chat_idx == 2:
      '''=============================================================================================
      for every turn after the first tool call:
      if chat_idx  = 2, call second intake to get new tools based on latest response, last results,
      and the new query
      calls chat_idx = 1 again to get tool choice from user
      ============================================================================================='''
      local_chat_history = []
      local_chat_history.append(query)
      self.query = query

      ''' =============================================================================================
      if user wants to review history, set context to full chat history, else set context to latest response,
      last results, and the new query
      ============================================================================================'''
      if mode_flag == 'Review History':
        context = self.query
        for chat in self.chat_history:
          for turn in chat:
            context += '\n' + turn
      else:
        context = self.latest_response + '\n' + self.results_string + '\n' + self.query

      self.best_tools, self.present, self.proteins_list, self.names_list, self.smiles_list, self.uniprot_list, self.pdb_list, self.chembl_list = \
      second_intake(self.query, context, self.parse_model, self.embed_model, self.document_embeddings)

      response = f'## Your new query is: {self.query}\n'
      response += '## The tools chosen based on your query are:'
      for i,tool in enumerate(self.best_tools):
        response += '\n' + f'{i+1}. {tool} : {full_tool_descriptions[tool]}'

      response += ' \n\n ## And the following information was found in your query:\n'
      for (entity_type, entity_list) in zip(self.present, [self.proteins_list, self.names_list, self.smiles_list, self.uniprot_list, self.pdb_list, self.chembl_list]):
        if self.present[entity_type] > 0:
          response += f'**{entity_type}**: {self.present[entity_type]}\n'
          for ent_idx, entity in enumerate(entity_list):
            response += f'- **{ent_idx+1}. {entity_type}**: {entity}\n'
      response += '\n To accept the #1 tool choice, hit enter; to choose 2 or 3, enter that number.'
      response += '\n To edit the items in a list, enter "edit".'
      response += '\n To start over, click the clear button and enter a new query.' 
      self.chat_idx = 1

      matches = smiles_regex(response)
      for m in matches:
          if m in response:
              response = response.replace(m, f'```{m}```')

      local_chat_history.append(response)
      self.chat_history.append(local_chat_history)
      with open('chat_session_history.txt', 'w') as f:
        pp.pp(self.chat_history, stream=f)

      return '', self.chat_history, None
    
    elif self.chat_idx == 999:
      ''' ============================================================================================== 
      condition for missing data after tool choice; if user was prompted for missing data, parse new input,
      then return to tool choice step (chat_idx = 1)
      ============================================================================================='''
      local_chat_history = []
      local_chat_history.append(query)

      ''' Parse the new input to get missing data '''
      present, proteins_list, names_list, smiles_list, uniprot_list, pdb_list, chembl_list = parse_input(query, self.parse_model)

      ''' Update the existing lists with any new data only if the existing lists are empty'''
      if len(self.proteins_list) == 0 and len(proteins_list) > 0:
        self.proteins_list = proteins_list
      if len(self.names_list) == 0 and len(names_list) > 0:
        self.names_list = names_list
      if len(self.smiles_list) == 0 and len(smiles_list) > 0:
        self.smiles_list = smiles_list
      if len(self.uniprot_list) == 0 and len(uniprot_list) > 0:
        self.uniprot_list = uniprot_list
      if len(self.pdb_list) == 0 and len(pdb_list) > 0:
        self.pdb_list = pdb_list
      if len(self.chembl_list) == 0 and len(chembl_list) > 0:
        self.chembl_list = chembl_list

      for item in present:
        self.present[item] += present[item]
      
      response = f'## Your new query is: {self.query}\n'
      response += '## The tools chosen based on your query are:'
      for i,tool in enumerate(self.best_tools):
        response += '\n' + f'{i+1}. {tool} : {full_tool_descriptions[tool]}'

      response += ' \n\n ## And the following information was found in your query:\n'
      for (entity_type, entity_list) in zip(self.present, [self.proteins_list, self.names_list, self.smiles_list, self.uniprot_list, self.pdb_list, self.chembl_list]):
        if self.present[entity_type] > 0:
          response += f'**{entity_type}**: {self.present[entity_type]}\n'
          for ent_idx, entity in enumerate(entity_list):
            response += f'- **{ent_idx+1}. {entity_type}**: {entity}\n'
      response += '\n To accept the #1 tool choice, hit enter; to choose 2 or 3, enter that number.'
      response += '\n To edit the items in a list, enter "edit".'
      response += '\n To start over, click the clear button and enter a new query.' 
      self.chat_idx = 1

      matches = smiles_regex(response)
      for m in matches:
          if m in response:
              response = response.replace(m, f'```{m}```')

      local_chat_history.append(response)
      self.chat_history.append(local_chat_history)
      with open('chat_session_history.txt', 'w') as f:
        pp.pp(self.chat_history, stream=f)

      return '', self.chat_history, None
    
    elif self.chat_idx == 501:
      ''' ==============================================================================================
      condition for editing a list; get which list to edit from user
      ============================================================================================='''
      local_chat_history = []
      local_chat_history.append(query)
      self.chat_idx = 502

      list_list = ['smiles list', 'names list', 'proteins list', 'uniprot list', 'pdb list', 'chembl list']
      try:
        choice_idx = int(query) - 1
        self.list_to_edit = list_list[choice_idx]

        response = f'## You have chosen to edit the {self.list_to_edit}.\n'
        response += 'Enter the numbers for the *items to keep* in the list.'

      except:
        response = 'Invalid input. Please enter the number corresponding to the list you wish to edit.'
        self.chat_idx = 501

      local_chat_history.append(response)
      self.chat_history.append(local_chat_history)
      with open('chat_session_history.txt', 'w') as f:
        pp.pp(self.chat_history, stream=f)
      return '', self.chat_history, None  

    if self.chat_idx == 502:
      ''' ==============================================================================================
      condition for editing a list; get which items to keep from user
      ============================================================================================='''
      local_chat_history = []
      local_chat_history.append(query)
      self.chat_idx = 503

      if ',' in query:
        items_to_keep = query.split(',')
      elif ';' in query:
        items_to_keep = query.split(';')
      else: 
        items_to_keep = query.split()
      
      for item in items_to_keep:
        if not item.isdigit():
          response = 'Invalid input. Please enter the numbers corresponding to the items you wish to keep.'
          self.chat_idx = 502
          local_chat_history.append(response)
          self.chat_history.append(local_chat_history)
          with open('chat_session_history.txt', 'w') as f:
            pp.pp(self.chat_history, stream=f)
          return '', self.chat_history, None
      
      try:
        if self.list_to_edit == 'smiles list':
          current_list = self.smiles_list
        elif self.list_to_edit == 'names list':
          current_list = self.names_list
        elif self.list_to_edit == 'proteins list':
          current_list = self.proteins_list
        elif self.list_to_edit == 'uniprot list':
          current_list = self.uniprot_list
        elif self.list_to_edit == 'pdb list':
          current_list = self.pdb_list
        elif self.list_to_edit == 'chembl list':
          current_list = self.chembl_list
        
        new_list = []
        for item in items_to_keep:
          idx = int(item) - 1
          new_list.append(current_list[idx])
        
        if self.list_to_edit == 'smiles list':
          self.smiles_list = new_list
        elif self.list_to_edit == 'names list': 
          self.names_list = new_list
        elif self.list_to_edit == 'proteins list':
          self.proteins_list = new_list
        elif self.list_to_edit == 'uniprot list':
          self.uniprot_list = new_list
        elif self.list_to_edit == 'pdb list':
          self.pdb_list = new_list
        elif self.list_to_edit == 'chembl list':
          self.chembl_list = new_list
        
        self.present = {
          'proteins': len(self.proteins_list),
          'molecules': len(self.names_list),
          'smiles': len(self.smiles_list),
          'uniprot': len(self.uniprot_list),
          'pdb': len(self.pdb_list),
          'chembl': len(self.chembl_list)
        }

        response = '## The tools chosen based on your query are:'
        for i,tool in enumerate(self.best_tools):
          response += '\n' + f'{i+1}. {tool} : {full_tool_descriptions[tool]}'

        response += ' \n\n ## And the following information was found in your query:\n'
        for (entity_type, entity_list) in zip(self.present, [self.proteins_list, self.names_list, self.smiles_list, self.uniprot_list, self.pdb_list, self.chembl_list]):
          if self.present[entity_type] > 0:
            response += f'**{entity_type}**: {self.present[entity_type]}\n'
            for ent_idx, entity in enumerate(entity_list):
              response += f'- **{ent_idx+1}. {entity_type}**: {entity}\n'
        response += '\n To accept the #1 tool choice, hit enter; to choose 2 or 3, enter that number.'
        response += '\n To edit the items in a list, enter "edit".'
        response += '\n To start over, click the clear button and enter a new query.' 
        self.chat_idx = 1

        matches = smiles_regex(response)
        for m in matches:
          if m in response:
            response = response.replace(m, f'```{m}```')

      except:
        response = 'An error occurred while processing your input. Please try again.'
        self.chat_idx = 502

      local_chat_history.append(response)
      self.chat_history.append(local_chat_history)
      with open('chat_session_history.txt', 'w') as f:
        pp.pp(self.chat_history, stream=f)
        
      return '', self.chat_history, None
        


full_tool_descriptions = {
  'smiles_node' : 'Queries Pubchem for the smiles string of the molecule based on the name.',
  'name_node' : 'Queries Pubchem for the name of the molecule based on the smiles string.',
  'related_node' : 'Queries Pubchem for similar molecules based on the smiles string or name.',
  'substitution_node' : 'A simple substitution routine that looks for a substituent on a phenyl ring and\
substitutes different fragments in that location. Returns a list of novel molecules and their\
QED score (1 is most drug-like, 0 is least drug-like).',
  'lipinski_node' : 'A tool to calculate QED and other lipinski properties of a molecule.',
  'pharmfeature_node': 'A tool to compare the pharmacophore features of a query molecule against\
those of a reference molecule and report the pharmacophore features of both and the feature\
score of the query molecule.',
  'uniprot_node' : 'This tool takes in the user requested protein and searches UNIPROT for matches.\
It returns a string scontaining the protein ID, gene name, organism, and protein name.',
  'listbioactives_node' : 'Accepts a UNIPROT ID and searches for bioactive molecules. Returns counts of\
the bioactives found and their ChEMBL IDs.',
  'getbioactives_node' : 'Accepts a Chembl ID and get all bioactives molecule SMILES and IC50s for that ID.',
  'predict_node' : 'uses the current_bioactives.csv file from the get_bioactives node to fit the\
Light GBM model and predict the IC50 for the current smiles.',
  'gpt_node' : 'Uses a Chembl dataset, previously stored in a CSV file by the get_bioactives node, to\
finetune a GPT model to generate novel molecules for the target protein.',
  'pdb_node' : 'Accepts a PDB ID and queires the protein databank for the sequence of the protein, \
as well as other information such as ligands.',
  'find_node': 'Accepts a protein name and searches the protein databack for PDB IDs that match along \
with the entry titles.',
  'docking_node' : 'Docking tool: uses dockstring to dock the molecule into the protein binding site and returns \
the docking score and the binding pose.',
  'get_actives_for_protein' : 'Finds Bioactive molecules for a give protein. Uses Uniprot to find chembl IDs \
for the protein, and then queries chembl for bioactive molecules.',
  'get_predictions_for_protein' : 'Uses Uniprot to find chembl IDs for the protein, and then queries chembl \
for bioactive molecules to train a model and predict the activity of the given smiles.',
  'dock_from_names' : 'Accepts names of molecules and docks them in a given protein.'
}

def websearch_node(query: str, embed_model, proxy_flag: bool = True) -> (list[str], str, list):
  '''
  Performs a web search using scholarly and ranks results based on similarity to the query.
  Args:
      query (str): The input query string.
      embed_model: The embedding model.
  Returns:  
      top_hits (list[str]): List of top hit titles and links.
      search_string (str): String representation of the top hits.
      None: Placeholder for images (not used here).
  '''
  try:
    if proxy_flag:
      pg = ProxyGenerator() 
      success = pg.FreeProxies()
      if success:
        pg.FreeProxies()
        scholarly.use_proxy(pg)

    scholarly.set_timeout(15) 

    search_query = scholarly.search_pubs(query)
    print(f'Search generator created for query: {query}')

    titles = []
    links = []
    abstracts = []

    for i in range(10):
      item = next(search_query)
      res_string = json.dumps(item)
      res_dict = json.loads(res_string)
      links.append(res_dict['pub_url'])
      titles.append(res_dict['bib']['title'])
      abstracts.append(res_dict['bib']['abstract'])
      print(f'Found result {i+1}')

    assert(len(titles) == len(links) == len(abstracts))
    print(f'Found {len(titles)} results')
    
    abstract_embeddings = embed_model.encode_document(abstracts)
    query_embeddings = embed_model.encode_query(query)

    scores = embed_model.similarity(query_embeddings, abstract_embeddings)

    max_hits = 10
    if len(scores) < max_hits:
      max_hits = len(scores)
    top_hits = []
    hits_idx = 0
    while hits_idx < 10:
      current_hit_idx = np.argmax(scores[0])
      current_score = scores[0][current_hit_idx].item()
      top_hits.append((titles[current_hit_idx], links[current_hit_idx], current_score))
      scores[0][current_hit_idx] = -1
      hits_idx += 1

    search_string = f'The top 10 hits for your query are:\n'
    i = 0
    for title, link, score in top_hits:
      search_string += f'{i}. {title}\nLink: {link}\nScore: {score:.3f}\n\n'
      i += 1
    print('Web search completed successfully.')
  except:
    top_hits = []
    search_string = 'Web search failed. Please try again later.'
    print('Web search failed due to an exception.')

  return top_hits, search_string, None

'''================================================================================================
Functions for use on Hugging Face Spaces
===================================================================================================='''


''' ======================================================================================================
older functions retained for compatibility
======================================================================================================'''


def query_to_context(query: str, parse_model, embed_model, document_embeddings):
    '''
    Processes a query to extract relevant context and information.
    Args:
        query (str): The input query string.
        parse_model: The NER model.
        embed_model: The embedding model.
        document_embeddings: The encoded document embeddings.
    Returns:
        results_list: List of results.
        results_string: String representation of results.
        results_images: Any associated images with the results.
    '''

    best_tools, present, proteins_list, names_list, smiles_list, uniprot_list, pdb_list, chembl_list = intake(query, parse_model, embed_model, document_embeddings)
    tool_function_hash = define_tool_hash(best_tools[0], proteins_list, names_list, smiles_list, uniprot_list, pdb_list, chembl_list)

    args_list = tool_function_hash[best_tools[0]][1]
    results_tuple  = tool_function_hash[best_tools[0]][0](*args_list)

    i=1
    while results_tuple[0] == [] :
      tool_function_hash = define_tool_hash(best_tools[i], proteins_list, names_list, smiles_list, uniprot_list, pdb_list, chembl_list)
      args_list = tool_function_hash[best_tools[i]][1]
      results_tuple  = tool_function_hash[best_tools[i]][0](*args_list)
      i+=1
      if i == 3:
        break

    results_list, results_string, results_images = results_tuple
    
    return results_list, results_string, results_images