app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

class CodeGenerator:
    def __init__(self, model_name="Salesforce/codet5-base", device=None):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
        if device:
            self.model = self.model.to(device)

    def generate_code(self, prompt, max_length=100):
        try:
            input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
            output = self.model.generate(input_ids, max_length=max_length, num_return_sequences=1)
            return self.tokenizer.decode(output[0], skip_special_tokens=True)
        except Exception as e:
            return f"Error generating code: {str(e)}"

class ChatHandler:
    def __init__(self, code_generator):
        self.history = []
        self.code_generator = code_generator  # Store the generator reference

    def handle_message(self, message):
        if not message.strip():
            return "", self.history
        response = self.code_generator.generate_code(message)
        self.history.append((message, response))
        return "", self.history

    def clear_history(self):
        self.history = []
        return []

def create_gradio_interface():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    code_generator = CodeGenerator(device=device)
    chat_handler = ChatHandler(code_generator)

    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("# S-Dreamer Salesforce/codet5-base Chat Interface")

        with gr.Row():
            with gr.Column(scale=3):
                chatbot = gr.Chatbot(height=400)
                message_input = gr.Textbox(label="Enter your code-related query", placeholder="Type your message here...")
                submit_button = gr.Button("Submit")

            with gr.Column(scale=1):
                gr.Markdown("## Features")
                features = ["Code generation", "Code completion", "Code explanation", "Error correction"]
                for feature in features:
                    gr.Markdown(f"- {feature}")
                clear_button = gr.Button("Clear Chat")

        submit_button.click(chat_handler.handle_message, inputs=message_input, outputs=[message_input, chatbot])
        clear_button.click(lambda: (None, chat_handler.clear_history()), inputs=[], outputs=[message_input, chatbot])

    demo.launch()

if __name__ == "__main__":
    create_gradio_interface()