app.py

import streamlit as st
from streamlit_chat import message
import time
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

# Set page configuration
st.set_page_config(
    page_title="ChatGPT-Style Chatbot",
    page_icon="🤖",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for styling with beautiful colors
st.markdown("""
    <style>
        .stApp {
            background-image: linear-gradient(135deg, #ffcccc 0%, #ff9999 100%);
        }
        .sidebar .sidebar-content {
            background-image: linear-gradient(135deg, #6B73FF 0%, #000DFF 100%);
            color: black;
        }
        .stTextInput>div>div>input {
            border-radius: 20px;
            padding: 10px 15px;
            border: 1px solid #d1d5db;
        }
        .stButton>button {
            border-radius: 20px;
            padding: 10px 25px;
            background-image: linear-gradient(to right, #6B73FF 0%, #000DFF 100%);
            color: black;
            border: none;
            font-weight: 500;
            transition: all 0.3s ease;
        }
        .stButton>button:hover {
            background-image: linear-gradient(to right, #000DFF 0%, #6B73FF 100%);
            transform: translateY(-2px);
            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
        }
        .chat-container {
            background-color: rgba(255, 230, 230, 0.95);
            border-radius: 15px;
            padding: 20px;
            box-shadow: 0 4px 15px rgba(0, 0, 0, 0.05);
            border: 1px solid #e5e7eb;
        }
        .title {
            color: #2d3748;
            text-align: center;
            margin-bottom: 30px;
            font-weight: 600;
        }
        .stSelectbox>div>div>select {
            border-radius: 12px;
            padding: 8px 12px;
        }
        .stSlider>div>div>div>div {
            background-color: #6B73FF;
        }
        .st-expander {
            border-radius: 12px;
            border: 1px solid #e5e7eb;
        }
        .stMarkdown h1 {
            color: #2d3748;
        }
    </style>
""", unsafe_allow_html=True)

# Sidebar
with st.sidebar:
    st.title("⚙️ Chatbot Settings")
    st.markdown("""
    ### ✨ About
    This is a ChatGPT-style chatbot powered by a fine-tuned LLM.
    """)
    
    # Model selection
    model_name = st.selectbox(
        "Choose a model",
        ["gpt2", "microsoft/DialoGPT-medium", "facebook/blenderbot-400M-distill"],
        index=1
    )
    
    # Advanced settings
    with st.expander("🔧 Advanced Settings"):
        max_length = st.slider("Max response length", 50, 500, 100)
        temperature = st.slider("Temperature", 0.1, 1.0, 0.7)
        top_p = st.slider("Top-p", 0.1, 1.0, 0.9)
    
    st.markdown("---")
    st.markdown("🚀 Built with ❤️ using [Streamlit](https://streamlit.io/) and [Hugging Face](https://huggingface.co/)")

# Initialize chat history
if 'generated' not in st.session_state:
    st.session_state['generated'] = []

if 'past' not in st.session_state:
    st.session_state['past'] = []

if 'model' not in st.session_state:
    st.session_state['model'] = None

if 'tokenizer' not in st.session_state:
    st.session_state['tokenizer'] = None

# Load model
@st.cache_resource
def load_model(model_name):
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForCausalLM.from_pretrained(model_name)
        return model, tokenizer
    except Exception as e:
        st.error(f"Error loading model: {e}")
        return None, None

# Generate response
def generate_response(prompt):
    if st.session_state['model'] is None or st.session_state['tokenizer'] is None:
        return "Model not loaded. Please try again."
    
    try:
        # Create conversation history context
        history = "\n".join([f"User: {p}\nBot: {g}" for p, g in zip(st.session_state['past'], st.session_state['generated'])])
        full_prompt = f"{history}\nUser: {prompt}\nBot:"
        
        # Generate response
        inputs = st.session_state['tokenizer'].encode(full_prompt, return_tensors="pt")
        outputs = st.session_state['model'].generate(
            inputs,
            max_length=max_length + len(inputs[0]),
            temperature=temperature,
            top_p=top_p,
            pad_token_id=st.session_state['tokenizer'].eos_token_id
        )
        response = st.session_state['tokenizer'].decode(outputs[0], skip_special_tokens=True)
        
        # Extract only the new response
        return response.split("Bot:")[-1].strip()
    except Exception as e:
        return f"Error generating response: {e}"

# Main app
st.title("💬 ChatGPT-Style Chatbot")
st.markdown("""
    <div class='title'>
        Experience a conversation with our fine-tuned LLM chatbot
    </div>
""", unsafe_allow_html=True)

# Container for chat
chat_container = st.container()

# Load model button
if st.button("🚀 Load Model"):
    with st.spinner(f"Loading {model_name}..."):
        st.session_state['model'], st.session_state['tokenizer'] = load_model(model_name)
    st.success(f"Model {model_name} loaded successfully!")

# Display chat
with chat_container:
    if st.session_state['generated']:
        for i in range(len(st.session_state['generated'])):
            message(st.session_state['past'][i], is_user=True, key=str(i) + '_user', avatar_style="identicon")
            message(st.session_state['generated'][i], key=str(i), avatar_style="bottts")

# User input
with st.form(key='chat_form', clear_on_submit=True):
    user_input = st.text_input("You:", key='input', placeholder="Type your message here...")
    submit_button = st.form_submit_button(label='Send ➤')

if submit_button and user_input:
    if st.session_state['model'] is None or st.session_state['tokenizer'] is None:
        st.warning("⚠️ Please load the model first!")
    else:
        # Add user message to chat history
        st.session_state['past'].append(user_input)
        
        # Generate response
        with st.spinner("🤔 Thinking..."):
            response = generate_response(user_input)
        
        # Add bot response to chat history
        st.session_state['generated'].append(response)
        
        # Rerun to update the chat display
        st.experimental_rerun()