app.py

pip install transformers
pip install transformers
pip install gradio

from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import logging

# Setup logging for better debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def load_model_and_tokenizer():
    try:
        # Load the tokenizer and model
        tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-multi")
        model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi")
        return tokenizer, model
    except Exception as e:
        logger.error(f"Failed to load model or tokenizer: {e}")
        raise

# Initialize tokenizer and model
tokenizer, model = load_model_and_tokenizer()

def generate_code(prompt):
    try:
        # Tokenize the input text
        input_ids = tokenizer(prompt, return_tensors="pt").input_ids

        # Generate code based on the input text
        generated_ids = model.generate(
            input_ids,
            max_length=200,  # Adjust as needed
            num_return_sequences=1,  # Number of generated sequences to return
            pad_token_id=tokenizer.eos_token_id  # Handle padding tokens
        )

        # Decode the generated tokens to text
        generated_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
        return generated_code
    except Exception as e:
        logger.error(f"Error during code generation: {e}")
        return "Error generating code. Please check the logs."

# Define the Gradio interface
iface = gr.Interface(
    fn=generate_code,
    inputs=gr.Textbox(lines=2, placeholder="Enter your code prompt here..."),
    outputs="text",
    title="Code Generator",
    description="Generate code snippets using the Salesforce CodeGen model."
)

# Launch the Gradio app
if __name__ == "__main__":
    try:
        iface.launch()
    except Exception as e:
        logger.error(f"Error launching the Gradio app: {e}")