app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
#from transformers import AutoTokenizer
#from llama_cpp import Llama
from datasets import load_dataset

# Replace with the direct image URL
flower_image_url = "https://i.postimg.cc/hG2FG85D/2.png"

# Inject custom CSS for the background with a centered and blurred image
st.markdown(
    f"""
    <style>
    /* Container for background */
    html, body {{
        margin: 0;
        padding: 0;
        overflow: hidden;
    }}
    [data-testid="stAppViewContainer"] {{
        position: relative;
        z-index: 1; /* Ensure UI elements are above the background */
    }}
    /* Blurred background image */
    .blurred-background {{
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        z-index: -1; /* Send background image behind all UI elements */
        background-image: url("{flower_image_url}");
        background-size: cover;
        background-position: center;
        filter: blur(10px); /* Adjust blur ratio here */
        opacity: 0.8; /* Optional: Add slight transparency for a subtle effect */
    }}
    </style>
    """,
    unsafe_allow_html=True
)

# Add the blurred background div
st.markdown('<div class="blurred-background"></div>', unsafe_allow_html=True)

#"""""""""""""""""""""""""   Application Code Starts here   """""""""""""""""""""""""""""""""""""""""""""

# Hugging Face access token
HF_TOKEN = "HF_TOKEN"  # Replace with your actual token or set it as an environment variable

# Load the text generation pipeline with model and tokenizer
@st.cache_resource
def load_text_generation_pipeline():
    model_name = "google/gemma-2-9b-it"
    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=HF_TOKEN)
    model = AutoModelForCausalLM.from_pretrained(
        model_name, 
        load_in_8bit=True, 
        device_map="auto", 
        use_auth_token=HF_TOKEN
    )
    return pipeline("text-generation", model=model, tokenizer=tokenizer)

text_generator = load_text_generation_pipeline()

# Load the counseling dataset
@st.cache_resource
def load_counseling_dataset():
    return load_dataset("Amod/mental_health_counseling_conversations")

dataset = load_counseling_dataset()

# Streamlit App
st.title("Mental Health Counseling Chat")
st.markdown("""
Welcome to the **Mental Health Counseling Chat Application**.  
This platform is designed to provide **supportive, positive, and encouraging responses** using a fast and efficient language model.
""")

# Display example dataset entries
if st.checkbox("Show Example Questions and Answers from Dataset"):
    sample = dataset["train"].shuffle(seed=42).select(range(3))  # Display 3 random samples
    for example in sample:
        st.markdown(f"**Question:** {example.get('context', 'N/A')}")
        st.markdown(f"**Answer:** {example.get('response', 'N/A')}")
        st.markdown("---")

# User input for mental health concerns
user_input = st.text_area("Your question or concern:", placeholder="Type your question here...")

if st.button("Get Supportive Response"):
    if user_input.strip():
        try:
            # Generate response using the text generation pipeline
            prompt = f"User: {user_input}\nCounselor:"
            response = text_generator(prompt, max_length=100, num_return_sequences=1)
            
            # Extract and display the response
            counselor_reply = response[0]["generated_text"].strip()
            st.subheader("Counselor's Response:")
            st.write(counselor_reply)
        except Exception as e:
            st.error(f"An error occurred while generating the response: {e}")
    else:
        st.error("Please enter a question or concern to receive a response.")

# Sidebar resources
st.sidebar.header("Additional Mental Health Resources")
st.sidebar.markdown("""
- [Mental Health Foundation](https://www.mentalhealth.org)
- [Mind](https://www.mind.org.uk)
- [National Suicide Prevention Lifeline](https://suicidepreventionlifeline.org)
""")
st.sidebar.info("This application is not a replacement for professional counseling. If you are in crisis, please seek professional help immediately.")