vmshankar86/Python_Code_Generator_Using_LLM_Model

1import gradio as gr2from transformers import AutoModelForCausalLM, AutoTokenizer3 4# Select a model5model_name = "Salesforce/codegen-2B-mono"  # Ensure this model is available6tokenizer = AutoTokenizer.from_pretrained(model_name)7model = AutoModelForCausalLM.from_pretrained(model_name)8 9# Function to generate code based on a prompt10def generate_code(prompt):11    # Adjust parameters to improve output quality12    inputs = tokenizer(prompt, return_tensors="pt")13    outputs = model.generate(14        **inputs,15        max_new_tokens=100,       # Adjust as needed for code length16        temperature=0.3,          # Lower temperature for more deterministic output17        top_p=0.9,                # Top-p filtering to focus on more likely completions18        repetition_penalty=1.2,   # Penalizes repetitive phrases19        do_sample=True            # Enables sampling for a creative touch20    )21    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)22    return generated_code23 24# Create a Gradio interface25iface = gr.Interface(26    fn=generate_code,27    inputs=gr.Textbox(lines=5, label="Enter your prompt"),28    outputs=gr.Code(language="python", label="Generated Code"),29    title="Python Code Generator",30    description="Enter a description of the Python code you want to generate."31)32 33# Launch the interface34iface.launch()