2 modes added

pages/1_Descriptive_chatbot.py

@@ -0,0 +1,407 @@
+import streamlit as st
+import torch
+from tqdm import tqdm
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSeq2SeqLM, AutoModelForCausalLM
+from transformers import AutoTokenizer
+import numpy as np
+import time
+import string
+
+
+# JS
+import nltk
+nltk.download('wordnet')
+from nltk.corpus import wordnet as wn
+from nltk.tokenize import word_tokenize
+
+@st.cache_resource
+def get_models(llama=False):
+  st.write('Loading the model...')
+  # config = PeftConfig.from_pretrained("NursNurs/T5ForReverseDictionary")
+  # model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
+  # model = PeftModel.from_pretrained(model, "NursNurs/T5ForReverseDictionary")
+
+  config = PeftConfig.from_pretrained("YouNameIt/T5ForReverseDictionary_prefix_tuned")
+  model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
+  model = PeftModel.from_pretrained(model, "YouNameIt/T5ForReverseDictionary_prefix_tuned")
+
+  tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
+
+  # JS
+  if llama:
+    model_name = 'meta-llama/Llama-2-7b-chat-hf'
+    access_token = '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'
+    llama_tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=access_token, use_fast=True)#, use_fast=True)
+    llama_model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=access_token, device_map={'':0})#, load_in_4bit=True)
+    st.write("The assistant is loaded and ready to use!")
+    return model, tokenizer, llama_model, llama_tokenizer 
+  
+  else:
+    st.write("_The assistant is loaded and ready to use! :tada:_")
+    return model, tokenizer
+
+model, tokenizer = get_models()
+
+def remove_punctuation(word):
+    # Create a translation table that maps all punctuation characters to None
+    translator = str.maketrans('', '', string.punctuation)
+    
+    # Use the translate method to remove punctuation from the word
+    word_without_punctuation = word.translate(translator)
+    
+    return word_without_punctuation
+
+def return_top_k(sentence, k=10, word=None, rels=False):
+
+  if sentence[-1] != ".":
+    sentence = sentence + "."
+
+  if rels:
+    inputs = [f"Description : It is related to '{word}' but not '{word}'. Word : "]
+  else:
+    inputs = [f"Description : {sentence} Word : "]
+
+  inputs = tokenizer(
+      inputs,
+      padding=True, truncation=True,
+      return_tensors="pt",
+  )
+  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+  model.to(device)
+
+  with torch.no_grad():
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    output_sequences = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10, num_beams=k+5, num_return_sequences=k+5, #max_length=3,
+                                              top_p = 50, output_scores=True, return_dict_in_generate=True) #repetition_penalty=10000.0
+
+    logits = output_sequences['sequences_scores'].clone().detach()
+    decoded_probabilities = torch.softmax(logits, dim=0)
+
+
+    #all word predictions
+    predictions = [tokenizer.decode(tokens, skip_special_tokens=True) for tokens in output_sequences['sequences']]
+    probabilities = [round(float(prob), 2) for prob in decoded_probabilities]
+
+    stripped_sent = [remove_punctuation(word.lower()) for word in sentence.split()]
+    for pred in predictions:
+      if (len(pred) < 2) | (pred in stripped_sent):
+        predictions.pop(predictions.index(pred))  
+     
+  return predictions[:10] 
+
+# JS 
+def get_related_words(word, num=5):
+  model.eval()
+  with torch.no_grad():
+      sentence = [f"Descripton : It is related to {word} but not {word}. Word : "]
+      #inputs = ["Description: It is something to cut stuff with. Word: "]
+      print(sentence)
+      inputs = tokenizer(sentence, padding=True, truncation=True, return_tensors="pt",)
+
+      device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+      model.to(device)
+
+      batch = {k: v.to(device) for k, v in inputs.items()}
+      beam_outputs = model.generate(
+          input_ids=batch['input_ids'], max_new_tokens=10, num_beams=num+2, num_return_sequences=num+2, early_stopping=True
+      )
+      
+      #beam_preds = [tokenizer.decode(beam_output.detach().cpu().numpy(), skip_special_tokens=True) for beam_output in beam_outputs if ]
+      beam_preds = []
+      for beam_output in beam_outputs:
+          prediction = tokenizer.decode(beam_output.detach().cpu().numpy(), skip_special_tokens=True).strip()
+          if prediction not in " ".join(sentence):
+            beam_preds.append(prediction)
+      
+      return ", ".join(beam_preds[:num])
+
+#if 'messages' not in st.session_state:
+
+def get_text():
+  input_text = st.chat_input()
+  return input_text
+
+def write_bot(input, remember=True, blink=True):
+    with st.chat_message('assistant'):
+      message_placeholder = st.empty()
+      full_response = input
+      if blink == True:
+        response = ''
+        for chunk in full_response.split():
+          response += chunk + " "
+          time.sleep(0.05)
+          # Add a blinking cursor to simulate typing
+          message_placeholder.markdown(response + "▌") 
+        time.sleep(0.5)
+      message_placeholder.markdown(full_response)
+    if remember == True:
+      st.session_state.messages.append({'role': 'assistant', 'content': full_response})
+    
+def ask_if_helped():
+  y = st.button('Yes!', key=60)
+  n = st.button('No...', key=61)
+  new = st.button('I have a new word', key=62)
+  if y:
+    write_bot("I am happy to help!")
+    again = st.button('Play again')
+    if again:
+      write_bot("Please describe your word!")
+    st.session_state.is_helpful['ask'] = False
+  elif n:
+    st.session_state.actions.append('cue')
+    st.session_state.is_helpful['ask'] = False
+    #cue_generation()
+  elif new:
+    write_bot("Please describe your word!")
+    st.session_state.is_helpful['ask'] = False
+
+## removed: if st.session_state.actions[-1] == "result":
+
+# JS
+def get_related_words_llama(relation, target, device, num=5):
+    prompt = f"Provide {num} {relation}s for the word '{target}'. Your answer consists of these {num} words only. Do not include the word '{target}' itself in your answer"
+
+    inputs = tokenizer([prompt], return_tensors='pt').to(device)
+    output = model.generate(
+        **inputs, max_new_tokens=40, temperature=.75, early_stopping=True, 
+        )
+    chatbot_response = tokenizer.decode(output[:, inputs['input_ids'].shape[-1]:][0], skip_special_tokens=True).strip()
+
+    postproc = [word for word in word_tokenize(chatbot_response) if len(word)>=3]
+
+    return postproc[-num:] if len(postproc)>=num else postproc
+
+
+def postproc_wn(related_words, syns=False):
+    if syns:
+        related_words = [word.split('.')[0] if word[0] != "." else word.split('.')[1] for word in related_words]
+    else:
+        related_words = [word.name().split('.')[0] if word.name()[0] != "." else word.name().split('.')[1] for word in related_words]
+    related_words = [word.replace("_", " ") for word in related_words]
+
+    return related_words
+
+# JS
+def get_available_cues(target):
+    wn_nouns = [word.name() for word in wn.all_synsets(pos='n')]
+    wn_nouns = [word.split('.')[0] if word[0] != "." else word.split('.')[1] for word in wn_nouns]
+
+    if target in wn_nouns:
+      available_cues = {}
+      synset_target = wn.synsets(target, pos=wn.NOUN)[0]
+
+      #if wn.synonyms(target)[0]:
+       #   available_cues['Synonyms'] = postproc_wn(wn.synonyms(target)[0], syns=True)
+      
+      #if synset_target.hypernyms():
+       #   available_cues['Hypernyms'] = postproc_wn(synset_target.hypernyms())
+
+
+      #if synset_target.hyponyms():
+        #  available_cues['Hyponyms'] = postproc_wn(synset_target.hyponyms())
+
+      if synset_target.examples():
+          examples = []
+          
+          for example in synset_target.examples():
+              examples.append(example.replace(target, "..."))
+                  
+          available_cues['Examples'] = examples
+
+      return available_cues
+
+    else:
+      return None 
+
+# JS: moved the cue generation further down
+#def cue_generation():
+ # if st.session_state.actions[-1] == 'cue':
+
+if 'messages' not in st.session_state:
+  st.session_state.messages = []
+  
+if 'results' not in st.session_state:
+  st.session_state.results = {'results': False, 'results_print': False}
+  
+if 'actions' not in st.session_state:
+  st.session_state.actions = [""]
+  
+if 'counters' not in st.session_state:
+  st.session_state.counters = {"letter_count": 0, "word_count": 0}
+  
+if 'is_helpful' not in st.session_state:
+  st.session_state.is_helpful = {'ask':False}
+  
+if 'descriptions' not in st.session_state:
+  st.session_state.descriptions = []
+  
+st.title("You name it! 🗣")
+
+# JS: would remove Simon by some neutral avatar
+with st.chat_message('user'):
+  st.write("Hey assistant!")
+  
+bot = st.chat_message('assistant')
+bot.write("Hello human! Wanna practice naming some words?")
+
+#for showing history of messages
+for message in st.session_state.messages:
+  if message['role'] == 'user':
+    with st.chat_message(message['role']):
+      st.markdown(message['content'])
+  else:
+    with st.chat_message(message['role']):
+      st.markdown(message['content'])
+  
+#display user message in chat message container
+prompt = get_text() 
+if prompt:
+  #JS: would replace Simon by some neutral character 
+  with st.chat_message('user'):
+    st.markdown(prompt)
+  #add to history
+  st.session_state.messages.append({'role': 'user', 'content': prompt})
+  #TODO: replace it with zero-shot classifier
+  yes = ['yes', 'again', 'Yes', 'sure', 'new word', 'yes!', 'yep', 'yeah']
+  if prompt in yes:
+    write_bot("Please describe your word!")
+  elif prompt == 'it is similar to the best place on earth':
+    write_bot("Great! Let me think what it could be...")
+    time.sleep(3)
+    write_bot("Do you mean Saarland?")
+  #if previously we asked to give a prompt
+  elif (st.session_state.messages[-2]['content'] == "Please describe your word!") & (st.session_state.messages[-1]['content'] != "no"):
+    write_bot("Great! Let me think what it could be...")
+    st.session_state.descriptions.append(prompt)
+    st.session_state.results['results'] = return_top_k(st.session_state.descriptions[-1])
+    st.session_state.results['results_print'] = dict(zip(range(1, 11), st.session_state.results['results']))
+    write_bot("I think I have some ideas. Do you want to see my guesses or do you want a cue?")
+    st.session_state.actions.append("result")
+  
+if st.session_state.actions[-1] == "result":
+  col1, col2, col3, col4, col5 = st.columns(5)
+  with col1:
+    a1 = st.button('Results', key=10)
+  with col2:
+    a2 = st.button('Cue', key=11)
+  if a1:
+    write_bot("Here are my guesses about your word:")
+    st.write(st.session_state.results['results_print'])
+    time.sleep(1)
+    write_bot('Does it help you remember the word?', remember=False)
+    st.session_state.is_helpful['ask'] = True
+  elif a2:
+    #write_bot(f'The first letter is {st.session_state.results["results"][0][0]}.')
+    #time.sleep(1)
+    st.session_state.actions.append('cue')
+    #cue_generation()
+    #write_bot('Does it help you remember the word?', remember=False)
+    #st.session_state.is_helpful['ask'] = True
+    
+if st.session_state.is_helpful['ask']:
+    ask_if_helped()
+
+if st.session_state.actions[-1] == 'cue':
+  guessed = False
+  write_bot('What do you want to see?', remember=False, blink=False)
+
+  while guessed == False:
+    # JS
+    word_count = st.session_state.counters["word_count"]
+    target = st.session_state.results["results"][word_count]
+    
+    col1, col2, col3, col4, col5 = st.columns(5)
+  
+
+    with col1:
+      b1 = st.button("Next letter", key="1")
+    with col2:
+      b2 = st.button("Related words")
+    with col3:
+      b3 = st.button("Next word", key="2")
+    with col4:
+      b4 = st.button("All words", key="3")
+
+    # JS
+    #if get_available_cues(target):
+     # avail_cues = get_available_cues(target)
+      #cues_buttons = {cue_type: st.button(cue_type) for cue_type in avail_cues}
+
+    b5 = st.button("I remembered the word!", key="4", type='primary')
+    b6 = st.button("Exit", key="5", type='primary')
+    new = st.button('Play again', key=64, type='primary')
+
+    if b1:
+      st.session_state.counters["letter_count"] += 1
+      #word_count = st.session_state.counters["word_count"]
+      letter_count = st.session_state.counters["letter_count"]
+      if letter_count < len(target):
+        write_bot(f'The word starts with {st.session_state.results["results"][word_count][:letter_count]}. \n Does this help you remember the word?', remember=False)
+        #ask_if_helped()
+        st.session_state.is_helpful['ask'] = True
+      else:
+        write_bot(f'This is my predicted word: "{target}". Does this match your query?')
+        #ask_if_helped()
+        st.session_state.is_helpful['ask'] = True
+
+    elif b2:
+      rels = return_top_k(st.session_state.descriptions[-1], word=target, rels=True)
+      write_bot(f'Here are words that are related to your word: {", ".join(rels)}. \n Does this help you remember the word?', remember=False)
+      #ask_if_helped()
+      st.session_state.is_helpful['ask'] = True
+
+    elif b3:
+      st.session_state.counters["letter_count"] = 1
+      letter_count = st.session_state.counters["letter_count"]
+      st.session_state.counters["word_count"] += 1
+      word_count = st.session_state.counters["word_count"]
+      #write_bot(f'The next word starts with {st.session_state.results["results"][word_count][:letter_count]}', remember=False)
+      if letter_count < len(target):
+        write_bot(f'The next word starts with {st.session_state.results["results"][word_count][:letter_count]}. \n Does this help you remember the word?', remember=False)
+        #ask_if_helped()
+        st.session_state.is_helpful['ask'] = True
+      else:
+        write_bot(f'This is my predicted word: "{target}". Does this match your query?')
+        #ask_if_helped()
+        st.session_state.is_helpful['ask'] = True
+
+    #elif get_available_cues(target) and "Synonyms" in cues_buttons and cues_buttons['Synonyms']:
+      #write_bot(f'Here are synonyms for the current word: {", ".join(avail_cues["Synonyms"])}', remember=False)
+
+    #elif get_available_cues(target) and "Hypernyms" in cues_buttons and cues_buttons['Hypernyms']:
+      #write_bot(f'Here are hypernyms for the current word: {", ".join(avail_cues["Hypernyms"])}', remember=False)
+
+    #elif get_available_cues(target) and "Hyponyms" in cues_buttons and cues_buttons['Hyponyms']:
+      #write_bot(f'Here are hyponyms for the current word: {", ".join(avail_cues["Hyponyms"])}', remember=False)
+
+    #elif get_available_cues(target) and "Examples" in cues_buttons and cues_buttons['Examples']:   
+      #write_bot(f'Here are example contexts for the current word: {", ".join(avail_cues["Examples"])}', remember=False)
+
+    elif b4:
+      write_bot(f"Here are all my guesses about your word: {st.session_state.results['results_print']}")
+
+    elif b5:
+      write_bot("Yay! I am happy I could be of help!")
+      st.session_state.counters["word_count"] = 0
+      st.session_state.counters["letter_count"] = 0
+      new = st.button('Play again', key=63)
+      if new:
+        write_bot("Please describe your word!")
+      guessed = True
+    
+      break
+    
+    elif b6:
+      write_bot("I am sorry I couldn't help you this time. See you soon!")
+      st.session_state.counters["word_count"] = 0
+      st.session_state.counters["letter_count"] = 0
+    st.session_state.actions.append('cue')
+    
+    if new:
+      write_bot("Please describe your word!")
+      st.session_state.counters["word_count"] = 0
+      st.session_state.counters["letter_count"] = 0
+
+    break
+

pages/2_Context-based_chatbot.py

@@ -0,0 +1,363 @@
+import streamlit as st
+import torch
+from tqdm import tqdm
+from transformers import pipeline
+import numpy as np
+import time
+import string
+
+
+# JS
+import nltk
+nltk.download('wordnet')
+from nltk.corpus import wordnet as wn
+from nltk.tokenize import word_tokenize
+
+@st.cache_resource
+def get_models(llama=False):
+  st.write('Loading the model...')
+  model = pipeline("fill-mask")
+  st.write("_The assistant is loaded and ready to use! :tada:_")
+  return model 
+
+model = get_models()
+
+def remove_punctuation(word):
+    # Create a translation table that maps all punctuation characters to None
+    translator = str.maketrans('', '', string.punctuation)
+    
+    # Use the translate method to remove punctuation from the word
+    word_without_punctuation = word.translate(translator)
+    
+    return word_without_punctuation
+
+def return_top_k(sentence, word=None, rels=False):
+
+  if sentence[-1] != ".":
+    sentence = sentence + "."
+
+#   if rels:
+#     inputs = [f"Description : It is related to '{word}' but not '{word}'. Word : "]
+#   else:
+#     inputs = [f"Description : {sentence} Word : "]
+    
+  output = model(sentence) 
+  output = [output[i]['token_str'] for i in output.keys()]
+  return output
+
+
+# JS 
+# def get_related_words(word, num=5):
+#   model.eval()
+#   with torch.no_grad():
+#       sentence = [f"Descripton : It is related to {word} but not {word}. Word : "]
+#       #inputs = ["Description: It is something to cut stuff with. Word: "]
+#       print(sentence)
+#       inputs = tokenizer(sentence, padding=True, truncation=True, return_tensors="pt",)
+
+#       device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#       model.to(device)
+
+#       batch = {k: v.to(device) for k, v in inputs.items()}
+#       beam_outputs = model.generate(
+#           input_ids=batch['input_ids'], max_new_tokens=10, num_beams=num+2, num_return_sequences=num+2, early_stopping=True
+#       )
+      
+    #   #beam_preds = [tokenizer.decode(beam_output.detach().cpu().numpy(), skip_special_tokens=True) for beam_output in beam_outputs if ]
+    #   beam_preds = []
+    #   for beam_output in beam_outputs:
+    #       prediction = tokenizer.decode(beam_output.detach().cpu().numpy(), skip_special_tokens=True).strip()
+    #       if prediction not in " ".join(sentence):
+    #         beam_preds.append(prediction)
+      
+    #   return ", ".join(beam_preds[:num])
+
+#if 'messages' not in st.session_state:
+
+def get_text():
+  input_text = st.chat_input()
+  return input_text
+
+def write_bot(input, remember=True, blink=True):
+    with st.chat_message('assistant'):
+      message_placeholder = st.empty()
+      full_response = input
+      if blink == True:
+        response = ''
+        for chunk in full_response.split():
+          response += chunk + " "
+          time.sleep(0.05)
+          # Add a blinking cursor to simulate typing
+          message_placeholder.markdown(response + "▌") 
+        time.sleep(0.5)
+      message_placeholder.markdown(full_response)
+    if remember == True:
+      st.session_state.messages.append({'role': 'assistant', 'content': full_response})
+    
+def ask_if_helped():
+  y = st.button('Yes!', key=60)
+  n = st.button('No...', key=61)
+  new = st.button('I have a new word', key=62)
+  if y:
+    write_bot("I am happy to help!")
+    again = st.button('Play again')
+    if again:
+      write_bot("Please describe your word!")
+    st.session_state.is_helpful['ask'] = False
+  elif n:
+    st.session_state.actions.append('cue')
+    st.session_state.is_helpful['ask'] = False
+    #cue_generation()
+  elif new:
+    write_bot("Please describe your word!")
+    st.session_state.is_helpful['ask'] = False
+
+## removed: if st.session_state.actions[-1] == "result":
+
+# JS
+# def get_related_words_llama(relation, target, device, num=5):
+#     prompt = f"Provide {num} {relation}s for the word '{target}'. Your answer consists of these {num} words only. Do not include the word '{target}' itself in your answer"
+
+#     inputs = tokenizer([prompt], return_tensors='pt').to(device)
+#     output = model.generate(
+#         **inputs, max_new_tokens=40, temperature=.75, early_stopping=True, 
+#         )
+#     chatbot_response = tokenizer.decode(output[:, inputs['input_ids'].shape[-1]:][0], skip_special_tokens=True).strip()
+
+#     postproc = [word for word in word_tokenize(chatbot_response) if len(word)>=3]
+
+#     return postproc[-num:] if len(postproc)>=num else postproc
+
+
+def postproc_wn(related_words, syns=False):
+    if syns:
+        related_words = [word.split('.')[0] if word[0] != "." else word.split('.')[1] for word in related_words]
+    else:
+        related_words = [word.name().split('.')[0] if word.name()[0] != "." else word.name().split('.')[1] for word in related_words]
+    related_words = [word.replace("_", " ") for word in related_words]
+
+    return related_words
+
+# JS
+def get_available_cues(target):
+    wn_nouns = [word.name() for word in wn.all_synsets(pos='n')]
+    wn_nouns = [word.split('.')[0] if word[0] != "." else word.split('.')[1] for word in wn_nouns]
+
+    if target in wn_nouns:
+      available_cues = {}
+      synset_target = wn.synsets(target, pos=wn.NOUN)[0]
+
+      #if wn.synonyms(target)[0]:
+       #   available_cues['Synonyms'] = postproc_wn(wn.synonyms(target)[0], syns=True)
+      
+      #if synset_target.hypernyms():
+       #   available_cues['Hypernyms'] = postproc_wn(synset_target.hypernyms())
+
+
+      #if synset_target.hyponyms():
+        #  available_cues['Hyponyms'] = postproc_wn(synset_target.hyponyms())
+
+      if synset_target.examples():
+          examples = []
+          
+          for example in synset_target.examples():
+              examples.append(example.replace(target, "..."))
+                  
+          available_cues['Examples'] = examples
+
+      return available_cues
+
+    else:
+      return None 
+
+# JS: moved the cue generation further down
+#def cue_generation():
+ # if st.session_state.actions[-1] == 'cue':
+
+if 'messages' not in st.session_state:
+  st.session_state.messages = []
+  
+if 'results' not in st.session_state:
+  st.session_state.results = {'results': False, 'results_print': False}
+  
+if 'actions' not in st.session_state:
+  st.session_state.actions = [""]
+  
+if 'counters' not in st.session_state:
+  st.session_state.counters = {"letter_count": 0, "word_count": 0}
+  
+if 'is_helpful' not in st.session_state:
+  st.session_state.is_helpful = {'ask':False}
+  
+if 'descriptions' not in st.session_state:
+  st.session_state.descriptions = []
+  
+st.title("You name it! 🗣")
+
+# JS: would remove Simon by some neutral avatar
+with st.chat_message('user'):
+  st.write("Hey assistant!")
+  
+bot = st.chat_message('assistant')
+bot.write("Hello human! Wanna practice naming some words?")
+
+#for showing history of messages
+for message in st.session_state.messages:
+  if message['role'] == 'user':
+    with st.chat_message(message['role']):
+      st.markdown(message['content'])
+  else:
+    with st.chat_message(message['role']):
+      st.markdown(message['content'])
+  
+#display user message in chat message container
+prompt = get_text() 
+if prompt:
+  #JS: would replace Simon by some neutral character 
+  with st.chat_message('user'):
+    st.markdown(prompt)
+  #add to history
+  st.session_state.messages.append({'role': 'user', 'content': prompt})
+  #TODO: replace it with zero-shot classifier
+  yes = ['yes', 'again', 'Yes', 'sure', 'new word', 'yes!', 'yep', 'yeah']
+  if prompt in yes:
+    write_bot("Please give a sentence using a <mask> instead of the word you have in mind!")
+  elif prompt == 'it is similar to the best place on earth':
+    write_bot("Great! Let me think what it could be...")
+    time.sleep(3)
+    write_bot("Do you mean Saarland?")
+  #if previously we asked to give a prompt
+  elif (st.session_state.messages[-2]['content'] == "Please give a sentence using a <mask> instead of the word you have in mind!") & (st.session_state.messages[-1]['content'] != "no"):
+    write_bot("Great! Let me think what it could be...")
+    st.session_state.descriptions.append(prompt)
+    st.session_state.results['results'] = return_top_k(st.session_state.descriptions[-1])
+    st.session_state.results['results_print'] = dict(zip(range(1, 11), st.session_state.results['results']))
+    write_bot("I think I have some ideas. Do you want to see my guesses or do you want a cue?")
+    st.session_state.actions.append("result")
+  
+if st.session_state.actions[-1] == "result":
+  col1, col2, col3, col4, col5 = st.columns(5)
+  with col1:
+    a1 = st.button('Results', key=10)
+  with col2:
+    a2 = st.button('Cue', key=11)
+  if a1:
+    write_bot("Here are my guesses about your word:")
+    st.write(st.session_state.results['results_print'])
+    time.sleep(1)
+    write_bot('Does it help you remember the word?', remember=False)
+    st.session_state.is_helpful['ask'] = True
+  elif a2:
+    #write_bot(f'The first letter is {st.session_state.results["results"][0][0]}.')
+    #time.sleep(1)
+    st.session_state.actions.append('cue')
+    #cue_generation()
+    #write_bot('Does it help you remember the word?', remember=False)
+    #st.session_state.is_helpful['ask'] = True
+    
+if st.session_state.is_helpful['ask']:
+    ask_if_helped()
+
+if st.session_state.actions[-1] == 'cue':
+  guessed = False
+  write_bot('What do you want to see?', remember=False, blink=False)
+
+  while guessed == False:
+    # JS
+    word_count = st.session_state.counters["word_count"]
+    target = st.session_state.results["results"][word_count]
+    
+    col1, col2, col3, col4, col5 = st.columns(5)
+  
+
+    with col1:
+      b1 = st.button("Next letter", key="1")
+    with col2:
+      b2 = st.button("Related words")
+    with col3:
+      b3 = st.button("Next word", key="2")
+    with col4:
+      b4 = st.button("All words", key="3")
+
+    # JS
+    #if get_available_cues(target):
+     # avail_cues = get_available_cues(target)
+      #cues_buttons = {cue_type: st.button(cue_type) for cue_type in avail_cues}
+
+    b5 = st.button("I remembered the word!", key="4", type='primary')
+    b6 = st.button("Exit", key="5", type='primary')
+    new = st.button('Play again', key=64, type='primary')
+
+    if b1:
+      st.session_state.counters["letter_count"] += 1
+      #word_count = st.session_state.counters["word_count"]
+      letter_count = st.session_state.counters["letter_count"]
+      if letter_count < len(target):
+        write_bot(f'The word starts with {st.session_state.results["results"][word_count][:letter_count]}. \n Does this help you remember the word?', remember=False)
+        #ask_if_helped()
+        st.session_state.is_helpful['ask'] = True
+      else:
+        write_bot(f'This is my predicted word: "{target}". Does this match your query?')
+        #ask_if_helped()
+        st.session_state.is_helpful['ask'] = True
+
+    elif b2:
+      rels = return_top_k(st.session_state.descriptions[-1], word=target, rels=True)
+      write_bot(f'Here are words that are related to your word: {", ".join(rels)}. \n Does this help you remember the word?', remember=False)
+      #ask_if_helped()
+      st.session_state.is_helpful['ask'] = True
+
+    elif b3:
+      st.session_state.counters["letter_count"] = 1
+      letter_count = st.session_state.counters["letter_count"]
+      st.session_state.counters["word_count"] += 1
+      word_count = st.session_state.counters["word_count"]
+      #write_bot(f'The next word starts with {st.session_state.results["results"][word_count][:letter_count]}', remember=False)
+      if letter_count < len(target):
+        write_bot(f'The next word starts with {st.session_state.results["results"][word_count][:letter_count]}. \n Does this help you remember the word?', remember=False)
+        #ask_if_helped()
+        st.session_state.is_helpful['ask'] = True
+      else:
+        write_bot(f'This is my predicted word: "{target}". Does this match your query?')
+        #ask_if_helped()
+        st.session_state.is_helpful['ask'] = True
+
+    #elif get_available_cues(target) and "Synonyms" in cues_buttons and cues_buttons['Synonyms']:
+      #write_bot(f'Here are synonyms for the current word: {", ".join(avail_cues["Synonyms"])}', remember=False)
+
+    #elif get_available_cues(target) and "Hypernyms" in cues_buttons and cues_buttons['Hypernyms']:
+      #write_bot(f'Here are hypernyms for the current word: {", ".join(avail_cues["Hypernyms"])}', remember=False)
+
+    #elif get_available_cues(target) and "Hyponyms" in cues_buttons and cues_buttons['Hyponyms']:
+      #write_bot(f'Here are hyponyms for the current word: {", ".join(avail_cues["Hyponyms"])}', remember=False)
+
+    #elif get_available_cues(target) and "Examples" in cues_buttons and cues_buttons['Examples']:   
+      #write_bot(f'Here are example contexts for the current word: {", ".join(avail_cues["Examples"])}', remember=False)
+
+    elif b4:
+      write_bot(f"Here are all my guesses about your word: {st.session_state.results['results_print']}")
+
+    elif b5:
+      write_bot("Yay! I am happy I could be of help!")
+      st.session_state.counters["word_count"] = 0
+      st.session_state.counters["letter_count"] = 0
+      new = st.button('Play again', key=63)
+      if new:
+        write_bot("Please describe your word!")
+      guessed = True
+    
+      break
+    
+    elif b6:
+      write_bot("I am sorry I couldn't help you this time. See you soon!")
+      st.session_state.counters["word_count"] = 0
+      st.session_state.counters["letter_count"] = 0
+    st.session_state.actions.append('cue')
+    
+    if new:
+      write_bot("Please describe your word!")
+      st.session_state.counters["word_count"] = 0
+      st.session_state.counters["letter_count"] = 0
+
+    break
+

pages/App.py

@@ -0,0 +1,25 @@
+import streamlit as st
+
+st.set_page_config(
+    page_title="You Name It!",
+    page_icon="👋",
+)
+
+st.write("# Welcome to YouNameIt chatbot! 👋")
+
+st.sidebar.success("Select a chatbot mode above.")
+
+st.markdown(
+    """
+    YouNameIt is a project helping people with aphasia practice their word retrieval skill and assisting them to remember words on a daily basis.
+    **👈 Select a chatbot mode from the sidebar** to test our app!
+    ### What new features are planned?
+    - Adaptation to German language and more;
+    - Speech-to-text suppport;
+    - Android & IOS mobile apps.
+    ### For any suggestions or ideas please contact us.
+    - Julian []()
+    - Nursulu []()
+"""
+)
+