app.py

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=5000)
 # Decode output
 output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
 # Return output
 return output

# Create and launch Gradio interface
iface = gr.Interface( # Use from_pretrained instead of from_pipeline
 fn=use_graphcodebert, 
 inputs=input, 
 outputs=output,
 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
)
iface.launch()