app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
@st.cache_resource
def load_model_and_tokenizer():
    model_name = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        device_map="auto", 
        trust_remote_code=False, 
        revision="main"
    )
    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
    
    # Check if tokenizer has a pad token, if not add it
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token  # Use eos_token as padding token
    
    return model, tokenizer

model, tokenizer = load_model_and_tokenizer()

# Define the prompt template
def generate_prompt(comment):
    instructions = f"""Virtual Psychologist, communicates with empathy and understanding, focusing on mental health support and providing advice within its expertise. \
    It actively listens, acknowledges emotions, and avoids overly clinical or technical language unless specifically requested. \
    It reacts to feedback with warmth and adjusts its tone to match the individual's needs, offering encouragement and validation as appropriate. \
    Responses are tailored in length and tone to ensure a supportive and conversational experience.
    """
    return f"[INST] {instructions} \n{comment} \n[/INST]"

# Define the response generator
def get_response(comment):
    prompt = generate_prompt(comment)
    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
    
    # Check if CUDA is available, otherwise use CPU
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    outputs = model.generate(
        input_ids=inputs["input_ids"].to(device), 
        attention_mask=inputs["attention_mask"].to(device), 
        max_new_tokens=140, 
        pad_token_id=tokenizer.pad_token_id  # Ensure padding is handled properly
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("[/INST]")[-1].strip()

# Streamlit app layout
st.title("Virtual Psychologist")
st.markdown("This virtual psychologist offers empathetic responses to your comments or questions. Enter your message below.")

user_input = st.text_input("Your Comment/Question:", placeholder="Type here...")

if user_input:
    with st.spinner("Generating response..."):
        response = get_response(user_input)
    st.write("### Response:")
    st.write(response)

st.markdown("Built with ❤️ using [Hugging Face Transformers](https://huggingface.co/transformers/) and [Streamlit](https://streamlit.io/).")