app.py

# app.py (updated with no `max_chars` and using correct model initialization)
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import json
import os

# Load the CodeGen-2B-mono model and tokenizer from Hugging Face
model_name = "Salesforce/codegen-2B-mono"  # Best version for CPU-friendly performance in code generation
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Ensure the model runs on CPU (important for Hugging Face Spaces free tier)
device = torch.device("cpu")
model.to(device)

# Cache to store recent prompts and responses with file-based persistence
CACHE_FILE = "cache.json"
cache = {}

# Load cache from file if it exists
if os.path.exists(CACHE_FILE):
    with open(CACHE_FILE, "r") as f:
        cache = json.load(f)

def code_assistant(prompt, language):
    # Input validation with a 1024-character limit
    if not prompt.strip():
        return "⚠️ Error: The input prompt cannot be empty. Please provide a coding question or code snippet."
    if len(prompt) > 1024:
        return "⚠️ Error: The input prompt is too long. Please limit it to 1024 characters."

    # Check if the prompt is in cache
    cache_key = (prompt, language)
    if str(cache_key) in cache:
        return cache[str(cache_key)]

    # Customize the prompt based on language
    if language:
        prompt = f"[{language}] {prompt}"  # Indicate the language for context
    
    # Tokenize the input
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    
    # Generate response with adjusted parameters for faster CPU response
    outputs = model.generate(
        inputs.input_ids,
        max_length=256,        # Shortened max length for quicker response
        temperature=0.1,       # Lower temperature for focused output
        top_p=0.8,             # Slightly reduced top_p for quicker sampling
        do_sample=True
    )
    
    # Decode the generated output
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Store the response in cache (limit cache size to 10 items)
    if len(cache) >= 10:
        cache.pop(next(iter(cache)))  # Remove the oldest item
    cache[str(cache_key)] = generated_text

    # Write the updated cache to file
    with open(CACHE_FILE, "w") as f:
        json.dump(cache, f)

    return generated_text

# Custom CSS styling for animations and colors
css = """
/* Center-align all text in the input and output boxes */
input, textarea, .output_text {
    text-align: center;
}

/* Style the main title */
h1 {
    color: #1e90ff;
    font-family: 'Arial', sans-serif;
    text-align: center;
    font-weight: bold;
}

/* Style the description */
.description {
    color: #555;
    font-family: 'Arial', sans-serif;
    text-align: center;
    margin-bottom: 20px;
}

/* Output box animation */
.output_text {
    color: #1e90ff;
    animation: fadeIn 2s ease-in-out;
}

/* Add fade-in animation */
@keyframes fadeIn {
    0% { opacity: 0; }
    100% { opacity: 1; }
}

/* Hover effect for the submit button */
button {
    background-color: #1e90ff;
    color: white;
    font-weight: bold;
    border: none;
    padding: 10px 20px;
    border-radius: 5px;
    transition: background-color 0.3s ease;
}

button:hover {
    background-color: #104e8b;
    cursor: pointer;
}
"""

# Enhanced title and description with HTML styling
title_html = """
<h1>💻 CodeBand: AI Code Assistant</h1>
"""

description_html = """
<p class="description">An AI-powered assistant for coding queries, debugging, and code generation. 
Choose a programming language for more tailored responses. Limited to 1024 characters.</p>
"""

# Set up Gradio interface with a dropdown for programming language selection
iface = gr.Interface(
    fn=code_assistant,
    inputs=[
        gr.Textbox(lines=5, placeholder="Ask a coding question or paste your code here..."),  # Removed `max_chars`
        gr.Dropdown(choices=["Python", "JavaScript", "Java", "C++", "HTML", "CSS", "SQL", "Other"], label="Programming Language")
    ],
    outputs="text",
    title=title_html,
    description=description_html,
    css=css  # Add custom CSS
)

# Launch the Gradio app
iface.launch()