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deleted commented out code
Browse files- pages/2_Context-based_chatbot.py +17 -129
pages/2_Context-based_chatbot.py
CHANGED
@@ -31,49 +31,16 @@ def remove_punctuation(word):
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return word_without_punctuation
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def return_top_k_context(sentence
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if sentence[-1] != ".":
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sentence = sentence + "."
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# if rels:
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# inputs = [f"Description : It is related to '{word}' but not '{word}'. Word : "]
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# else:
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# inputs = [f"Description : {sentence} Word : "]
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output = model_context(sentence)
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output = [output[i]['token_str'].strip() for i in range(len(output))]
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return output
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# JS
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# def get_related_words(word, num=5):
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# model.eval()
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# with torch.no_grad():
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# sentence = [f"Descripton : It is related to {word} but not {word}. Word : "]
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# #inputs = ["Description: It is something to cut stuff with. Word: "]
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# print(sentence)
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# inputs = tokenizer(sentence, padding=True, truncation=True, return_tensors="pt",)
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# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# model.to(device)
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# batch = {k: v.to(device) for k, v in inputs.items()}
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# beam_outputs = model.generate(
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# input_ids=batch['input_ids'], max_new_tokens=10, num_beams=num+2, num_return_sequences=num+2, early_stopping=True
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# )
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# #beam_preds = [tokenizer.decode(beam_output.detach().cpu().numpy(), skip_special_tokens=True) for beam_output in beam_outputs if ]
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# beam_preds = []
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# for beam_output in beam_outputs:
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# prediction = tokenizer.decode(beam_output.detach().cpu().numpy(), skip_special_tokens=True).strip()
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# if prediction not in " ".join(sentence):
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# beam_preds.append(prediction)
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# return ", ".join(beam_preds[:num])
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#if 'messages_context' not in st.session_state:
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def get_text():
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input_text = st.chat_input()
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return input_text
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@@ -112,22 +79,6 @@ def ask_if_helped_context():
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write_bot("Please give a sentence using a <mask> instead of the word you have in mind!")
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st.session_state.is_helpful_context['ask'] = False
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## removed: if st.session_state.actions_context[-1] == "result":
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# JS
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# def get_related_words_llama(relation, target, device, num=5):
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# prompt_context = 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"
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# inputs = tokenizer([prompt_context], return_tensors='pt').to(device)
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# output = model.generate(
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# **inputs, max_new_tokens=40, temperature=.75, early_stopping=True,
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# )
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# chatbot_response = tokenizer.decode(output[:, inputs['input_ids'].shape[-1]:][0], skip_special_tokens=True).strip()
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# postproc = [word for word in word_tokenize(chatbot_response) if len(word)>=3]
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# return postproc[-num:] if len(postproc)>=num else postproc
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def postproc_wn(related_words, syns=False):
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if syns:
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@@ -137,43 +88,7 @@ def postproc_wn(related_words, syns=False):
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related_words = [word.replace("_", " ") for word in related_words]
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return related_words
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# JS
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def get_available_cues(target):
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wn_nouns = [word.name() for word in wn.all_synsets(pos='n')]
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wn_nouns = [word.split('.')[0] if word[0] != "." else word.split('.')[1] for word in wn_nouns]
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if target in wn_nouns:
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available_cues = {}
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synset_target = wn.synsets(target, pos=wn.NOUN)[0]
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#if wn.synonyms(target)[0]:
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# available_cues['Synonyms'] = postproc_wn(wn.synonyms(target)[0], syns=True)
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#if synset_target.hypernyms():
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# available_cues['Hypernyms'] = postproc_wn(synset_target.hypernyms())
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#if synset_target.hyponyms():
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# available_cues['Hyponyms'] = postproc_wn(synset_target.hyponyms())
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if synset_target.examples():
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examples = []
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for example in synset_target.examples():
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examples.append(example.replace(target, "..."))
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available_cues['Examples'] = examples
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return available_cues
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else:
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return None
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# JS: moved the cue generation further down
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#def cue_generation():
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# if st.session_state.actions_context[-1] == 'cue':
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if 'messages_context' not in st.session_state:
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st.session_state.messages_context = []
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@@ -194,7 +109,6 @@ if 'descriptions_context' not in st.session_state:
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st.title("You name it! 🗣")
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# JS: would remove Simon by some neutral avatar
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with st.chat_message('user', avatar='julian.jpg'):
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st.write("Hey assistant!")
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@@ -213,28 +127,26 @@ for message in st.session_state.messages_context:
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#display user message in chat message container
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prompt_context = get_text()
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if prompt_context:
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#JS: would replace Simon by some neutral character
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with st.chat_message('user', avatar="julian.jpg"):
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st.markdown(prompt_context)
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#add to history
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st.session_state.messages_context.append({'role': 'user', 'content': prompt_context})
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#TODO: replace it with zero-shot classifier
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yes = ['yes', 'again', '
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if st.session_state.actions_context[-1] == "result":
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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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@@ -273,11 +185,6 @@ if st.session_state.actions_context[-1] == 'cue':
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with col4:
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b4 = st.button("All words", key="3")
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# JS
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#if get_available_cues(target):
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# avail_cues = get_available_cues(target)
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#cues_buttons = {cue_type: st.button(cue_type) for cue_type in avail_cues}
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b5 = st.button("I remembered the word!", key="4", type='primary')
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b6 = st.button("Exit", key="5", type='primary')
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new = st.button('Play again', key=64, type='primary')
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@@ -288,21 +195,17 @@ if st.session_state.actions_context[-1] == 'cue':
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if b1:
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st.session_state.counter_context["letter_count"] += 1
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#word_count = st.session_state.counter_context["word_count"]
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letter_count = st.session_state.counter_context["letter_count"]
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if letter_count < len(target):
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write_bot(f'The word starts with {st.session_state.results_context["results_context"][word_count][:letter_count]}.', remember=False)
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#ask_if_helped_context()
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st.session_state.is_helpful_context['ask'] = True
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else:
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write_bot(f'This is my predicted word: "{target}". Does this match your query?')
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#ask_if_helped_context()
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st.session_state.is_helpful_context['ask'] = True
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elif b2:
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rels = return_top_k_context(st.session_state.descriptions_context[-1], word=target, rels=True)
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write_bot(f'Here are words that are related to your word: {", ".join(rels)}.', remember=False)
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#ask_if_helped_context()
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st.session_state.is_helpful_context['ask'] = True
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elif b3:
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letter_count = st.session_state.counter_context["letter_count"]
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st.session_state.counter_context["word_count"] += 1
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word_count = st.session_state.counter_context["word_count"]
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#write_bot(f'The next word starts with {st.session_state.results_context["results_context"][word_count][:letter_count]}', remember=False)
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if letter_count < len(target):
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write_bot(f'The next word starts with {st.session_state.results_context["results_context"][word_count][:letter_count]}.', remember=False)
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#ask_if_helped_context()
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st.session_state.is_helpful_context['ask'] = True
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else:
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write_bot(f'This is my predicted word: "{target}". Does this match your query?')
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#ask_if_helped_context()
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st.session_state.is_helpful_context['ask'] = True
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#elif get_available_cues(target) and "Synonyms" in cues_buttons and cues_buttons['Synonyms']:
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#write_bot(f'Here are synonyms for the current word: {", ".join(avail_cues["Synonyms"])}', remember=False)
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#elif get_available_cues(target) and "Hypernyms" in cues_buttons and cues_buttons['Hypernyms']:
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#write_bot(f'Here are hypernyms for the current word: {", ".join(avail_cues["Hypernyms"])}', remember=False)
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#elif get_available_cues(target) and "Hyponyms" in cues_buttons and cues_buttons['Hyponyms']:
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#write_bot(f'Here are hyponyms for the current word: {", ".join(avail_cues["Hyponyms"])}', remember=False)
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#elif get_available_cues(target) and "Examples" in cues_buttons and cues_buttons['Examples']:
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#write_bot(f'Here are example contexts for the current word: {", ".join(avail_cues["Examples"])}', remember=False)
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elif b4:
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write_bot(f"Here are all my guesses about your word: {st.session_state.results_context['results_context_print']}")
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st.session_state.is_helpful_context['ask'] = True
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return word_without_punctuation
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def return_top_k_context(sentence):
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if sentence[-1] != ".":
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sentence = sentence + "."
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output = model_context(sentence)
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output = [output[i]['token_str'].strip() for i in range(len(output))]
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return output
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def get_text():
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input_text = st.chat_input()
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return input_text
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write_bot("Please give a sentence using a <mask> instead of the word you have in mind!")
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st.session_state.is_helpful_context['ask'] = False
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def postproc_wn(related_words, syns=False):
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if syns:
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related_words = [word.replace("_", " ") for word in related_words]
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return related_words
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if 'messages_context' not in st.session_state:
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st.session_state.messages_context = []
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st.title("You name it! 🗣")
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with st.chat_message('user', avatar='julian.jpg'):
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st.write("Hey assistant!")
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#display user message in chat message container
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prompt_context = get_text()
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if prompt_context:
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with st.chat_message('user', avatar="julian.jpg"):
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st.markdown(prompt_context)
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#add to history
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st.session_state.messages_context.append({'role': 'user', 'content': prompt_context})
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#TODO: replace it with zero-shot classifier
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yes = ['yes', 'again', 'sure', 'new word', 'yes!', 'yep', 'yeah']
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try:
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if prompt_context.lower() in yes:
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write_bot("Please give a sentence using a <mask> instead of the word you have in mind!")
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#if previously we asked to give a prompt_context
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elif (st.session_state.messages_context[-2]['content'] == "Please give a sentence using a <mask> instead of the word you have in mind!") & (st.session_state.messages_context[-1]['content'] != "no"):
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write_bot("Great! Let me think what it could be...")
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st.session_state.descriptions_context.append(prompt_context)
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st.session_state.results_context['results_context'] = return_top_k_context(st.session_state.descriptions_context[-1])
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st.session_state.results_context['results_context_print'] = dict(zip(range(1, len(st.session_state.results_context['results_context'])+1), st.session_state.results_context['results_context']))
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write_bot("I think I have some ideas. Do you want to see my guesses or do you want a cue?")
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st.session_state.actions_context.append("result")
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except:
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write_bot("Sorry, I didn't understand you... I am still learning :sob: For now, could you respond with 'yes' or 'no'? ")
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if st.session_state.actions_context[-1] == "result":
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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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with col4:
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b4 = st.button("All words", key="3")
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b5 = st.button("I remembered the word!", key="4", type='primary')
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b6 = st.button("Exit", key="5", type='primary')
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new = st.button('Play again', key=64, type='primary')
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if b1:
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st.session_state.counter_context["letter_count"] += 1
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letter_count = st.session_state.counter_context["letter_count"]
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if letter_count < len(target):
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write_bot(f'The word starts with {st.session_state.results_context["results_context"][word_count][:letter_count]}.', remember=False)
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st.session_state.is_helpful_context['ask'] = True
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else:
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write_bot(f'This is my predicted word: "{target}". Does this match your query?')
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st.session_state.is_helpful_context['ask'] = True
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elif b2:
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rels = return_top_k_context(st.session_state.descriptions_context[-1], word=target, rels=True)
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write_bot(f'Here are words that are related to your word: {", ".join(rels)}.', remember=False)
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st.session_state.is_helpful_context['ask'] = True
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elif b3:
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letter_count = st.session_state.counter_context["letter_count"]
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st.session_state.counter_context["word_count"] += 1
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word_count = st.session_state.counter_context["word_count"]
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if letter_count < len(target):
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write_bot(f'The next word starts with {st.session_state.results_context["results_context"][word_count][:letter_count]}.', remember=False)
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st.session_state.is_helpful_context['ask'] = True
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else:
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write_bot(f'This is my predicted word: "{target}". Does this match your query?')
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st.session_state.is_helpful_context['ask'] = True
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elif b4:
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write_bot(f"Here are all my guesses about your word: {st.session_state.results_context['results_context_print']}")
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st.session_state.is_helpful_context['ask'] = True
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