# import streamlit as st
# from transformers import GPT2LMHeadModel, GPT2Tokenizer

# # Load the GPT-2 model and tokenizer
# @st.cache_resource
# def load_model():
#     model_name = "gpt2"
#     tokenizer = GPT2Tokenizer.from_pretrained(model_name)
#     model = GPT2LMHeadModel.from_pretrained(model_name)
#     return model, tokenizer

# # Function to generate a response from GPT-2
# def generate_response(input_text, model, tokenizer):
#     inputs = tokenizer.encode(input_text, return_tensors="pt")
#     outputs = model.generate(inputs, max_length=150, do_sample=True, top_p=0.9, top_k=50)
#     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
#     return response

# # Streamlit UI setup
# def main():
#     st.title("GPT-2 Chatbot")

#     # Chat history
#     if 'history' not in st.session_state:
#         st.session_state['history'] = []

#     user_input = st.text_input("You:", "")
    
#     # Generate and display response
#     if user_input:
#         model, tokenizer = load_model()
#         response = generate_response(user_input, model, tokenizer)
#         st.session_state['history'].append({"user": user_input, "bot": response})
    
#     # Display chat history
#     for chat in st.session_state['history']:
#         st.write(f"You: {chat['user']}")
#         st.write(f"Bot: {chat['bot']}")

# if __name__ == "__main__":
#     main()
import streamlit as st
from transformers import pipeline

# Configure the Hugging Face API key
HUGGINGFACE_API_KEY = st.secrets['huggingface_api_key']

# Initialize the Hugging Face text-generation model (DialoGPT or other conversational models)
chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium", api_key=HUGGINGFACE_API_KEY)

# Function to get response from the Hugging Face model
def get_chatbot_response(user_input):
    try:
        response = chatbot(user_input, max_length=1000, pad_token_id=50256)  # Generate response
        return response[0]['generated_text']  # Extract the generated response
    except Exception as e:
        return f"Error: {str(e)}"

# Streamlit interface
st.set_page_config(page_title="Smart ChatBot", layout="centered")

# Custom CSS for chat bubbles with full width and emojis
st.markdown("""
    <style>
    .chat-container {
        display: flex;
        flex-direction: column;
        width: 100%;
    }
    .chat-bubble {
        width: 100%;
        padding: 15px;
        margin: 10px 0;
        border-radius: 10px;
        font-size: 18px;
        color: white;
        display: inline-block;
        line-height: 1.5;
    }
    .user-bubble {
        background: #6a82fb; /* Soft blue */
        align-self: flex-end;
        border-radius: 10px 10px 10px 10px;
    }
    .bot-bubble {
        background: #fc5c7d; /* Soft pink */
        align-self: flex-start;
        border-radius: 10px 10px 10px 10px;
    }
    .chat-header {
        # text-align: center;
        font-size: 35px;
        font-weight: bold;
        margin-bottom: 20px;
        color: #3d3d3d;
    }
    .emoji {
        font-size: 22px;
        margin-right: 10px;
    }
    </style>
""", unsafe_allow_html=True)

st.markdown('<div class="chat-header">Hugging Face Chatbot-Your AI Companion 💻</div>', unsafe_allow_html=True)
st.write("Powered by Hugging Face for smart, engaging conversations. 🤖")

if "history" not in st.session_state:
    st.session_state["history"] = []

with st.form(key="chat_form", clear_on_submit=True):
    user_input = st.text_input("Your message here... ✍️", max_chars=2000, label_visibility="collapsed")
    submit_button = st.form_submit_button("Send 🚀")

    if submit_button:
        if user_input:
            response = get_chatbot_response(user_input)
            st.session_state.history.append((user_input, response))
        else:
            st.warning("Please Enter A Prompt 😅")

if st.session_state["history"]:
    st.markdown('<div class="chat-container">', unsafe_allow_html=True)
    for user_input, response in st.session_state["history"]:
        st.markdown(f'<div class="chat-bubble user-bubble"><span class="emoji">👤</span>You: {user_input}</div>', unsafe_allow_html=True)
        st.markdown(f'<div class="chat-bubble bot-bubble"><span class="emoji">🤖</span>Bot: {response}</div>', unsafe_allow_html=True)
    st.markdown('</div>', unsafe_allow_html=True)