import gradio as gr
from transformers import CodeT5ForConditionalGeneration, CodeT5Tokenizer

# Initialize the CodeT5 model and tokenizer
model = CodeT5ForConditionalGeneration.from_pretrained(" Salesforce/code-t5-base")
tokenizer = CodeT5Tokenizer.from_pretrained("Salesforce/code-t5-base")

# Define the Gradio interface
demo = gr.Interface(
    fn=lambda input_code, upload_file, temperature, max_length: generate_code(input_code, upload_file, temperature, max_length),
    inputs=[
________gr.Textbox(label="Input_Code/Prompt"),
________gr.File(label="Upload_Code_File"),
________gr.Slider(label="Temperature",_minimum=0,_maximum=1,_default=0.5),
________gr.Slider(label="Max_Length",_minimum=10,_maximum=512,_default=256)
____],
    outputs=[
________gr.Code(label="Generated_Code"),
________gr.Textbox(label="Conversation_History")
____],
    title="CodeT5 Code Generation Builder",
    description="Generate code snippets using CodeT5 and interact with the AI model through a simple web interface."
)

def generate_code(input_code, upload_file, temperature, max_length):
    # Preprocess the input code and uploaded file
    if upload_file is not None:
        with open(upload_file.name, 'r') as file:
            input_code = file.read()

    # Tokenize the input code
    input_ids = tokenizer.encode(input_code, return_tensors='pt')

    # Generate code using CodeT5
    output = model.generate(input_ids, temperature=temperature, max_length=max_length)

    # Convert the output to a string
    generated_code = tokenizer.decode(output[0], skip_special_tokens=True)

    # Update the conversation history
    conversation_history = f"Input Code: {input_code}\nGenerated Code: {generated_code}"

    return generated_code, conversation_history

# Launch the Gradio interface
demo.launch()