Spaces:
Runtime error
Runtime error
Initial version of the app
Browse files- configs.yaml +7 -0
- requirements.txt +5 -0
- src/app.py +132 -0
- src/common.py +28 -0
- src/hangman.py +35 -0
- src/hf_utils.py +109 -0
configs.yaml
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generation_config:
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max_output_tokens: 256
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temperature: 1
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top_p: 1
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top_k: 32
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os_model: google/gemma-2b-it
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device: cpu
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requirements.txt
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streamlit
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python-dotenv
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torch
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transformers
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accelerate
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src/app.py
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import logging
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import os
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import streamlit as st
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import torch
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from dotenv import load_dotenv
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from common import CATEGORIES, MAX_TRIES, configs
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from hangman import guess_letter
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from hf_utils import query_hint, query_word
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@st.cache_resource()
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def setup(model_id: str, device: str) -> None:
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"""Initializes the model and tokenizer.
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Args:
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model_id (str): Model ID used to load the tokenizer and model.
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"""
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logger.info(f"Loading model and tokenizer from model: '{model_id}'")
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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token=os.environ["HF_ACCESS_TOKEN"],
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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token=os.environ["HF_ACCESS_TOKEN"],
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).to(device)
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logger.info("Setup finished")
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return {"tokenizer": tokenizer, "model": model}
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__file__)
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st.set_page_config(
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page_title="Gemma Hangman",
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page_icon="🧩",
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)
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load_dotenv()
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assets = setup(configs["os_model"], configs["device"])
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tokenizer = assets["tokenizer"]
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model = assets["model"]
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if not st.session_state:
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st.session_state["word"] = ""
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st.session_state["hint"] = ""
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st.session_state["hangman"] = ""
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st.session_state["missed_letters"] = []
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st.session_state["correct_letters"] = []
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st.title("Gemini Hangman")
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st.markdown("## Guess the word based on a hint")
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col1, col2 = st.columns(2)
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with col1:
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category = st.selectbox(
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"Choose a category",
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CATEGORIES,
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)
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with col2:
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start_btn = st.button("Start game")
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reset_btn = st.button("Reset game")
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if start_btn:
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st.session_state["word"] = query_word(
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category,
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model,
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tokenizer,
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configs["generation_config"],
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configs["device"],
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)
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st.session_state["hint"] = query_hint(
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st.session_state["word"],
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model,
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tokenizer,
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configs["generation_config"],
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configs["device"],
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)
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st.session_state["hangman"] = "_" * len(st.session_state["word"])
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st.session_state["missed_letters"] = []
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st.session_state["correct_letters"] = []
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if reset_btn:
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st.session_state["word"] = ""
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st.session_state["hint"] = ""
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st.session_state["hangman"] = ""
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st.session_state["missed_letters"] = []
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st.session_state["correct_letters"] = []
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st.markdown(
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"""
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## Guess the word based on a hint
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Note: you must input whitespaces and special characters.
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"""
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)
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st.markdown(f'### Hint:\n{st.session_state["hint"]}')
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col3, col4 = st.columns(2)
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with col3:
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guess = st.text_input(label="Enter letter")
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guess_btn = st.button("Guess letter")
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if guess_btn:
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st.session_state = guess_letter(guess, st.session_state)
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with col4:
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hangman = st.text_input(
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label="Hangman",
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value=st.session_state["hangman"],
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)
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st.text_input(
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label=f"Missed letters (max {MAX_TRIES} tries)",
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value=", ".join(st.session_state["missed_letters"]),
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)
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if st.session_state["word"] == st.session_state["hangman"] != "":
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st.success("You won!")
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st.balloons()
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if len(st.session_state["missed_letters"]) >= MAX_TRIES:
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st.error(f"""You lost, the correct word was '{st.session_state["word"]}'""")
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st.snow()
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src/common.py
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@@ -0,0 +1,28 @@
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import logging
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import pprint
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import yaml
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def parse_configs(configs_path: str) -> dict:
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"""Parse configs from the YAML file.
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Args:
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configs_path (str): Path to the YAML file
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Returns:
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dict: Parsed configs
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"""
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configs = yaml.safe_load(open(configs_path, "r"))
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logger.info(f"Configs: {pprint.pformat(configs)}")
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return configs
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CONFIGS_PATH = "configs.yaml"
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MAX_TRIES = 6
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CATEGORIES = ["Country", "Animal", "Food", "Movie"]
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__file__)
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configs = parse_configs(CONFIGS_PATH)
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src/hangman.py
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import logging
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from streamlit import session_state
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def guess_letter(letter: str, session: session_state) -> session_state:
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"""Take a letter and evaluate if it is part of the hangman puzzle
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then updates the session object accordingly.
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Args:Chosen letter
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letter (str): Streamlit session object
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session (session_state): _description_
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Returns:
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session_state: Updated session
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"""
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logger.info(f"Letter '{letter}' picked")
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if letter in session["word"]:
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session["correct_letters"].append(letter)
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else:
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session["missed_letters"].append(letter)
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hangman = "".join(
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[
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(letter if letter in session["correct_letters"] else "_")
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for letter in session["word"]
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]
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)
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session["hangman"] = hangman
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logger.info("Session state updated")
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return session
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__file__)
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src/hf_utils.py
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import logging
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import re
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import string
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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GEMMA_WORD_PATTERNS = [
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"(?<=\*)(.*?)(?=\*)",
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'(?<=")(.*?)(?=")',
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]
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def query_hf(
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query: str,
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model: AutoModelForCausalLM,
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tokenizer: AutoTokenizer,
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generation_config: dict,
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device: str,
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) -> str:
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"""Queries an LLM model using the Vertex AI API.
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Args:
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query (str): Query sent to the Vertex API
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model (str): Model target by Vertex
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generation_config (dict): Configurations used by the model
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Returns:
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str: Vertex AI text response
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"""
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generation_config = GenerationConfig(
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do_sample=True,
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max_new_tokens=generation_config["max_output_tokens"],
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top_k=generation_config["top_k"],
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top_p=generation_config["top_p"],
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temperature=generation_config["temperature"],
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)
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input_ids = tokenizer(query, return_tensors="pt").to(device)
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outputs = model.generate(**input_ids, generation_config=generation_config)
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outputs = tokenizer.decode(outputs[0], skip_special_tokens=True)
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outputs = outputs.replace(query, "")
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return outputs
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def query_word(
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category: str,
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model: AutoModelForCausalLM,
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tokenizer: AutoTokenizer,
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generation_config: dict,
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device: str,
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) -> str:
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"""Queries a word to be used for the hangman game.
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Args:
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category (str): Category used as source sample a word
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model (str): Model target by Vertex
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generation_config (dict): Configurations used by the model
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Returns:
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str: Queried word
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"""
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logger.info(f"Quering word for category: '{category}'...")
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query = f"Name a single existing {category}."
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matched_word = ""
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while not matched_word:
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word = query_hf(query, model, tokenizer, generation_config, device)
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# Extract word of interest from Gemma's output
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for pattern in GEMMA_WORD_PATTERNS:
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matched_words = re.findall(rf"{pattern}", word)
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matched_words = [x for x in matched_words if x != ""]
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if matched_words:
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matched_word = matched_words[-1]
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matched_word = matched_word.translate(str.maketrans("", "", string.punctuation))
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matched_word = matched_word.lower()
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logger.info("Word queried successful")
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return matched_word
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def query_hint(
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word: str,
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model: AutoModelForCausalLM,
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tokenizer: AutoTokenizer,
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generation_config: dict,
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device: str,
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) -> str:
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"""Queries a hint for the hangman game.
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Args:
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word (str): Word used as source to create the hint
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model (str): Model target by Vertex
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generation_config (dict): Configurations used by the model
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Returns:
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str: Queried hint
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"""
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logger.info(f"Quering hint for word: '{word}'...")
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query = f"Describe the word '{word}' without mentioning it."
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hint = query_hf(query, model, tokenizer, generation_config, device)
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hint = re.sub(re.escape(word), "***", hint, flags=re.IGNORECASE)
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logger.info("Hint queried successful")
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return hint
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__file__)
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