import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load GraphCodeBERT model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/graphcodebert-base")
model = AutoModelForCausalLM.from_pretrained("microsoft/graphcodebert-base")

# Define input and output interfaces
input = gr.Textbox(lines=5, label="Input")
output = gr.Textbox(label="Output")

# Define function to use GraphCodeBERT
def use_graphcodebert(input):
  # Encode input
  input_ids = tokenizer.encode(input, return_tensors="pt")
  # Generate output
  output_ids = model.generate(input_ids, max_length=50)
  # Decode output
  output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
  # Return output
  return output

# Create and launch Gradio interface
# Create and launch Gradio interface
iface = gr.Interface.from_pretrained( # Use from_pretrained instead of from_pipeline
    fn=use_graphcodebert, 
    inputs=input, 
    outputs=output,
    model=model, # Pass the model as a parameter
    tokenizer=tokenizer, # Pass the tokenizer as a parameter
    title="GraphCodeBERT Code Synthesis", # Add a title for the web app
    description="Enter a natural language query and get a code snippet generated by GraphCodeBERT.", # Add a description for the web app
    examples=[["create a function that returns the sum of two numbers"], ["sort a list of numbers in ascending order"]], # Add some examples for the input
    theme="huggingface" # Choose a theme for the web app
)
iface.launch()