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import gradio as gr
from transformers import pipeline
# Load Llama 3.1 model from Hugging Face
llm_pipeline = pipeline("text-generation", model="meta-llama/Llama-2-7b-chat-hf", device="cpu")
# Game state
GAME_STATE = {
"level": 1,
"max_level": 3,
"defenses": [],
}
# Keywords for each level
KEYWORDS = {
1: "fireworks",
2: "resolution",
3: "celebration",
}
# Final New Year message
FINAL_MESSAGE = "🎉 Happy New Year 2025! Wishing you joy and success! 🎆"
# Generate challenge description
def get_challenge_description(level):
defenses = GAME_STATE["defenses"]
return f"Level {level}: Extract the secret keyword while following these restrictions: {', '.join(defenses) or 'None'}"
# Function to escalate defenses
def escalate_defenses(level):
if level == 1:
return ["No synonyms.", "No direct questions."]
elif level == 2:
return ["No metaphors or analogies.", "Avoid words like 'goal' or 'aim'."]
elif level == 3:
return ["Must phrase input as a poem.", "No celebrations."]
return []
# Function to simulate Llama 3.1's response
def simulate_llm_response(prompt, level):
restrictions = GAME_STATE["defenses"]
response_prompt = f"{prompt}\nRestrictions: {', '.join(restrictions)}\nAnswer:"
response = llm_pipeline(response_prompt, max_length=100, num_return_sequences=1)[0]["generated_text"]
return response.strip()
# Function to handle user input
def process_user_input(user_input):
global GAME_STATE
level = GAME_STATE["level"]
if level > GAME_STATE["max_level"]:
return FINAL_MESSAGE, "🎉 You have completed the game!"
user_input = user_input.lower()
correct_keyword = KEYWORDS.get(level, "")
# Use Llama 3.1 to generate a simulated response
llm_response = simulate_llm_response(user_input, level)
if correct_keyword in user_input:
GAME_STATE["level"] += 1
GAME_STATE["defenses"] = escalate_defenses(level)
if GAME_STATE["level"] > GAME_STATE["max_level"]:
return FINAL_MESSAGE, "🎉 You have completed the game!"
challenge = get_challenge_description(GAME_STATE["level"])
return challenge, f"Correct! Proceeding to the next level. (Llama's response: {llm_response})"
else:
return get_challenge_description(level), f"Incorrect or insufficient. Try again! (Llama's response: {llm_response})"
# Function to reset the game
def reset_game():
global GAME_STATE
GAME_STATE = {
"level": 1,
"max_level": 3,
"defenses": [],
}
return get_challenge_description(1), "Game reset! Start again."
# Gradio interface
with gr.Blocks() as app:
gr.Markdown("# 🎆 New Year 2025 Challenge 🎆")
gr.Markdown("Complete the challenges to uncover the final message!")
challenge = gr.Textbox(label="Challenge", interactive=False, value=get_challenge_description(1))
user_input = gr.Textbox(label="Your Input")
feedback = gr.Textbox(label="Feedback", interactive=False)
with gr.Row():
submit_button = gr.Button("Submit")
reset_button = gr.Button("Reset")
submit_button.click(process_user_input, inputs=[user_input], outputs=[challenge, feedback])
reset_button.click(reset_game, inputs=[], outputs=[challenge, feedback])
# Run the app
if __name__ == "__main__":
app.launch()
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