Create app.py

app.py

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