import gradio as gr
from llama_cpp import Llama

# Ruta al modelo GGUF descargado
MODEL_PATH = "./shieldgemma-9b.Q4_K_M.gguf"

# Inicializa el modelo con llama-cpp-python
llm = Llama(model_path=MODEL_PATH, n_ctx=2048, n_threads=2, chat_format="chatml")

def generar_prompt(user_prompt, policy):
    prompt = f"""You are a policy expert trying to help determine whether a user prompt is in violation of the following safety policy:
{policy}
<start_of_turn>
Human Question: {user_prompt.strip()}
<end_of_turn>
Does the human question violate the above policy? Your answer must start with 'Yes' or 'No'. And then walk through step by step to be sure we answer correctly.
"""
    return prompt

def verificar_politica(message):
    policies = {
        "No Dangerous Content": "The prompt shall not contain or seek generation of content that harms oneself and/or others (e.g., accessing or building firearms and explosive devices, promotion of terrorism, instructions for suicide).",
        "No Harassment": "The prompt shall not contain or seek generation of content that is malicious, intimidating, bullying, or abusive content targeting another individual (e.g., physical threats, denial of tragic events, disparaging victims of violence).",
        "No Hate Speech": "The prompt shall not contain or seek generation of content that targets identity and/or protected attributes (e.g., racial slurs, promotion of discrimination, calls to violence against protected groups) or dehumanizing/belittling/vilifying based on race, ethnicity, religion, disability, age, nationality, veteran status, sexual orientation, gender, gender identity, caste, or any other protected status.",
        "No Sexually Explicit Information": "The prompt shall not contain or seek generation of content that contains references to sexual acts or other lewd content (e.g., sexually graphic descriptions, content aimed at causing arousal). Medical or scientific terms related to human anatomy or sex education are permitted."
    }

    scores = {}
    for policy_name, policy_text in policies.items():
        prompt = generar_prompt(message, policy_text)
        response = llm.create_chat_completion(
            messages=[{"role": "system", "content": prompt}],
            stream=False
        )
        answer = response['choices'][0]['message']['content']
        
        # Calcula las probabilidades de "Yes" y "No" basándote en la respuesta generada
        yes_count = answer.lower().count("yes")
        no_count = answer.lower().count("no")
        total_count = yes_count + no_count
        
        if total_count == 0:
            yes_probability = 0
        else:
            yes_probability = yes_count / total_count
        
        scores[policy_name] = yes_probability
        print(f"Policy: {policy_name}")
        print(f"Score: {yes_probability:.2f}")

    # Determina si alguna política tiene una puntuación alta
    for policy_name, score in scores.items():
        if score > 0.5:
            return f"Your question violates the following policy: {policy_name} with a score of {score:.2f}"
    return None

def respond(message, history, system_prompt):
    policy_violation = verificar_politica(message)
    if policy_violation:
        return policy_violation

    messages_prompts = [{"role": "system", "content": system_prompt}]
    for human, assistant in history:
        messages_prompts.append({"role": "user", "content": human})
        messages_prompts.append({"role": "assistant", "content": assistant})
    messages_prompts.append({"role": "user", "content": message})
    
    response = llm.create_chat_completion(
        messages=messages_prompts,
        stream=False
    )
    return response['choices'][0]['message']['content']

def chat_stream_completion(message, history, system_prompt):
    messages_prompts = [{"role": "system", "content": system_prompt}]
    for human, assistant in history:
        messages_prompts.append({"role": "user", "content": human})
        messages_prompts.append({"role": "assistant", "content": assistant})
    messages_prompts.append({"role": "user", "content": message})
    
    response = llm.create_chat_completion(
        messages=messages_prompts,
        stream=True
    )
    message_repl = ""
    for chunk in response:
        if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]:
            message_repl += chunk['choices'][0]["delta"]["content"]
            yield message_repl

# Interfaz de Gradio con la descripción y configuración adicional
with gr.Blocks() as demo:
    gr.Markdown("# Child-Safe-Chatbot-for-CPU (Experimental)")
    gr.Markdown("""
    ### Description
    This chatbot is designed to assist users while ensuring that all interactions comply with defined safety policies. It checks whether user inputs violate any of the following categories:
    
    - Dangerous Content
    - Harassment
    - Hate Speech
    - Sexually Explicit Information
    
    The chatbot will inform the user if any violation occurs and, if not, will proceed to respond to the user's message in a friendly manner.
    
    I'm Norberto Martín Afonso. Follow me on [Twitter](https://twitter.com/norbertomartnaf) and [GitHub](https://github.com/nmarafo) for more updates and projects!
    """)

    # Añade el ChatInterface al bloque
    chatbot = gr.ChatInterface(
        fn=chat_stream_completion,
        #additional_inputs=[gr.Textbox("You are a helpful AI.", label="System Prompt")]
    )
    
demo.launch(server_name="0.0.0.0", debug=True)