fangedvampire24/codegeneration_salesforcemodel

1import gradio as gr #Web interface2from transformers import AutoModelForCausalLM, AutoTokenizer #For loading the model and making the input into tokens3model_name="Salesforce/codegen-350M-multi"4 5#Initialize the tokenizer and model6tokenizer=AutoTokenizer.from_pretrained(model_name)7model=AutoModelForCausalLM.from_pretrained(model_name)8 9def generate_code(prompt, max_length=100, temperature=0.7, top_p=0.95):10    inputs=tokenizer(prompt,return_tensors='pt') 11    outputs=model.generate(**inputs, max_length=max_length, temperature=temperature, top_p=top_p, do_sample=True) #input: input_id, weight_number12 13    generated_code=tokenizer.decode(outputs[0],skip_special_tokens=True)14    return generated_code15 16#Gradio interface17with gr.Blocks() as demo:18    gr.Markdown("## CODE GENERATION WITH CODEGEN MODEL")19 20    #input box to add prompt21    prompt=gr.Textbox(lines=10, label='Enter your prompt for code generation')22    max_length=gr.Slider(50,500, value=100, label='Max Length')23    temperature=gr.Slider(0.1,0.9, value=0.7, label='Temperature')24    top_p=gr.Slider(0.1,1.0, value=0.95, label='Top P value')25 26    output_box=gr.Textbox(lines=20, label='Generated Code')27 28    generate_button=gr.Button('Generate code')29    generate_button.click(fn=generate_code,30                         inputs=[prompt,max_length,temperature,top_p],31                         outputs=output_box)32 33    demo.launch()34