app.py

# app.py
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
    if not prompt.strip():
        return "Error: The input prompt cannot be empty. Please provide a coding question or code snippet."
    if len(prompt) > 256:
        return "Error: The input prompt is too long. Please limit it to 256 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=128,        # 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

# 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..."),
        gr.Dropdown(choices=["Python", "JavaScript", "Java", "C++", "HTML", "CSS", "SQL", "Other"], label="Programming Language")
    ],
    outputs="text",
    title="CodeBand",
    description="An AI code assistant to help you with coding queries, debugging, and code generation. Specify the programming language for more accurate responses."
)

# Launch the Gradio app
iface.launch()