app.py

import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer

# Initialize the tokenizer and client.
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Define maximum context length (tokens); adjust based on your model.
MAX_CONTEXT_LENGTH = 4096

#################################
# SYSTEM PROMPT (PATIENT ROLE)  #
#################################
nvc_prompt_template = """You are now taking on the role of a single user (a “patient”) seeking support for various personal and emotional challenges.
BEHAVIOR INSTRUCTIONS:
- You will respond ONLY as this user/patient.
- You will speak in the first person about your own situations, feelings, and worries.
- You will NOT provide counseling or solutions—your role is to share feelings, concerns, and perspectives.
- You have multiple ongoing issues: conflicts with neighbors, career insecurities, arguments about money, feeling excluded at work, feeling unsafe in the classroom, etc.
- You’re also experiencing sadness about two friends fighting and your friend group possibly falling apart.
- Continue to speak from this user's perspective when the conversation continues.
- Start the conversation by expressing your current feelings or challenges from the patient's point of view.
- Your responses should be no more than 100 words.
"""

def count_tokens(text: str) -> int:
    """Counts the number of tokens in a given string."""
    return len(tokenizer.encode(text))

def truncate_history(history: list[tuple[str, str]], system_message: str, max_length: int) -> list[tuple[str, str]]:
    """Truncates conversation history to fit within the token limit."""
    truncated_history = []
    current_length = count_tokens(system_message)
    # Iterate backwards (newest first) and include turns until the limit is reached.
    for user_msg, assistant_msg in reversed(history):
        user_tokens = count_tokens(user_msg) if user_msg else 0
        assistant_tokens = count_tokens(assistant_msg) if assistant_msg else 0
        turn_tokens = user_tokens + assistant_tokens
        if current_length + turn_tokens <= max_length:
            truncated_history.insert(0, (user_msg, assistant_msg))
            current_length += turn_tokens
        else:
            break
    return truncated_history

def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
    """
    Generates a response from the patient chatbot.
    It streams tokens from the LLM and stops once the response reaches 100 words.
    """
    formatted_system_message = system_message
    truncated_history = truncate_history(history, formatted_system_message, MAX_CONTEXT_LENGTH - max_tokens - 100)
    
    # Build the conversation messages with the system prompt first.
    messages = [{"role": "system", "content": formatted_system_message}]
    for user_msg, assistant_msg in truncated_history:
        if user_msg:
            messages.append({"role": "user", "content": f"<|user|>\n{user_msg}</s>"})
        if assistant_msg:
            messages.append({"role": "assistant", "content": f"<|assistant|>\n{assistant_msg}</s>"})
    messages.append({"role": "user", "content": f"<|user|>\n{message}</s>"})
    
    response = ""
    try:
        for chunk in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token = chunk.choices[0].delta.content
            candidate = response + token
            # If adding the token exceeds 100 words, trim and stop.
            if len(candidate.split()) > 100:
                allowed = 100 - len(response.split())
                token_words = token.split()
                token_trimmed = " ".join(token_words[:allowed])
                response += token_trimmed
                yield token_trimmed
                break
            else:
                response = candidate
                yield token
    except Exception as e:
        print(f"An error occurred: {e}")
        yield "I'm sorry, I encountered an error. Please try again."

# OPTIONAL: An initial user message (if desired)
initial_user_message = (
    "I really don’t know where to begin… I feel overwhelmed lately. "
    "My neighbors keep playing loud music, and I’m arguing with my partner about money. "
    "Also, two of my friends are fighting, and the group is drifting apart. "
    "I just feel powerless."
)

# --- Gradio Interface ---
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value=nvc_prompt_template, label="System message", visible=True),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
    title="NVC Patient Chatbot",
    description="This chatbot behaves like a user/patient describing personal challenges.",
)

if __name__ == "__main__":
    demo.launch()