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
import os
import requests

# 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   """""""""""""""""""""""""""""""""""""""""""""

# Groq API Configuration
api_key = os.environ.get("LawersGuideAPIKey")  # Ensure GROQ_API_KEY is set in your environment variables
base_url = "https://api.groq.com/openai/v1/models/google/gemma-2-9b-it/completions"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

# Function to query Groq model
@st.cache_resource
def query_groq_model(prompt, max_tokens=100, temperature=0.7):
    try:
        payload = {
            "prompt": prompt,
            "max_tokens": max_tokens,
            "temperature": temperature,
            "top_p": 1.0,
            "frequency_penalty": 0.0,
            "presence_penalty": 0.0,
            "n": 1
        }
        response = requests.post(base_url, headers=headers, json=payload)
        response.raise_for_status()
        result = response.json()
        return result["choices"][0]["text"].strip()
    except Exception as e:
        return f"Error querying the model: {e}"

# 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 the Groq `google/gemma-2-9b-it` model.
""")

# Load example dataset for user exploration (optional)
@st.cache_resource
def load_counseling_dataset():
    from datasets import load_dataset
    return load_dataset("Amod/mental_health_counseling_conversations")

dataset = load_counseling_dataset()

# Display example questions and answers from dataset
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:
            # Query Groq model
            prompt = f"User: {user_input}\nCounselor:"
            counselor_reply = query_groq_model(prompt, max_tokens=150, temperature=0.7)
            st.subheader("Counselor's Response:")
            st.write(counselor_reply)
        except Exception as e:
            st.error(f"An error occurred while querying the model: {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.")