app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM

# Custom CSS for a Grok/ChatGPT-like look
st.markdown("""
    <style>
    .main { background-color: #f9f9f9; padding: 20px; }
    .stTextArea textarea {
        border: 1px solid #ddd;
        border-radius: 8px;
        padding: 10px;
        font-family: 'Roboto', sans-serif;
        font-size: 16px;
        background-color: #fff;
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    }
    .stButton button {
        background-color: #4a90e2;
        color: white;
        border-radius: 8px;
        padding: 10px 20px;
        font-family: 'Roboto', sans-serif;
        font-size: 14px;
    }
    .stButton button:hover {
        background-color: #357abd;
    }
    .code-output {
        background-color: #2b2b2b;
        color: #f0f0f0;
        padding: 15px;
        border-radius: 8px;
        font-family: 'Courier New', monospace;
        font-size: 14px;
        margin-top: 10px;
    }
    .title {
        font-family: 'Roboto', sans-serif;
        font-size: 28px;
        font-weight: bold;
        color: #333;
        margin-bottom: 10px;
    }
    .subtitle {
        font-family: 'Roboto', sans-serif;
        font-size: 16px;
        color: #666;
        margin-bottom: 20px;
    }
    .chat-message {
        font-family: 'Roboto', sans-serif;
        font-size: 16px;
        color: #333;
        margin-bottom: 5px;
    }
    </style>
""", unsafe_allow_html=True)

# Load model and tokenizer
try:
    checkpoint = "Salesforce/codegen-350M-mono"
    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
    model = AutoModelForCausalLM.from_pretrained(checkpoint)
except Exception as e:
    st.error(f"Error loading model: {e}")
    st.stop()

# Function to generate code
def generate_code(description):
    prompt = f"Generate Python code for the following task: {description}\n"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_length=500,
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id
    )
    code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return code[len(prompt):].strip()

# Initialize chat history in session state
if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

# UI Layout
st.markdown('<div class="title">Code Generation Bot</div>', unsafe_allow_html=True)
st.markdown('<div class="subtitle">Describe your task, and I’ll generate Python code for you!</div>', unsafe_allow_html=True)

with st.container():
    # Input area
    description = st.text_area(
        "Enter your description here",
        placeholder="e.g., Write a function to calculate the factorial of a number",
        height=150
    )
    
    col1, col2 = st.columns([1, 5])
    with col1:
        if st.button("Generate"):
            if description.strip():
                with st.spinner("Thinking..."):
                    generated_code = generate_code(description)
                # Append to chat history
                st.session_state.chat_history.append({"input": description, "output": generated_code})
            else:
                st.warning("Please enter a description first!")
    with col2:
        st.empty()  # Spacer

    # Display chat history
    if st.session_state.chat_history:
        st.write("### Chat History")
        for chat in st.session_state.chat_history:
            st.markdown(f'<div class="chat-message"><strong>You:</strong> {chat["input"]}</div>', unsafe_allow_html=True)
            st.markdown(f'<div class="code-output">{chat["output"]}</div>', unsafe_allow_html=True)
            st.markdown("---")  # Separator for readability

# Optional tip at the bottom
st.info("Tip: Check the generated code for accuracy before using it!")