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# app.py
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import json
import os
# Load the model and tokenizer from Hugging Face
model_name = "bigcode/starcoder" # Use StarCoder for code-related tasks
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Ensure the model runs on CPU for Hugging Face Spaces free tier
device = torch.device("cuda" if torch.cuda.is_available() else "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 responses
outputs = model.generate(
inputs.input_ids,
max_length=128, # Shortened max length for quicker response
temperature=0.1, # Lower temperature for more 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="Code Assistant with StarCoder",
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()
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