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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load CodeGen model and tokenizer
model_name = "Salesforce/codegen-2B-mono"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
def generate_response(input_text, max_length=250, temperature=0.7, top_p=0.9, top_k=50):
"""
Generate response using the CodeGen model based on user input and selected parameters.
"""
try:
# Encode input and prepare input tensor
inputs = tokenizer(input_text, return_tensors="pt").to(device)
# Generate text based on model output
outputs = model.generate(
inputs.input_ids,
max_length=max_length,
temperature=temperature,
top_p=top_p,
top_k=top_k,
do_sample=True,
num_return_sequences=1,
no_repeat_ngram_size=2
)
# Decode and return the generated text
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
except Exception as e:
return f"Error generating response: {str(e)}"
# Create Gradio interface
with gr.Blocks() as codegen_app:
gr.Markdown("# CodeGen-powered Text Generation")
# Input box for user prompt
input_text = gr.Textbox(
label="Input Text",
placeholder="Type your question or prompt here",
lines=3
)
# Sliders for customization
max_length = gr.Slider(
label="Max Length",
minimum=50,
maximum=1024,
step=10,
value=250
)
temperature = gr.Slider(
label="Temperature",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.7
)
top_p = gr.Slider(
label="Top-p (Nucleus Sampling)",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.9
)
top_k = gr.Slider(
label="Top-k (Sampling Limit)",
minimum=0,
maximum=100,
step=5,
value=50
)
# Output box
output_text = gr.Textbox(
label="Generated Response",
placeholder="The model's response will appear here",
lines=15
)
# Generate button
generate_button = gr.Button("Generate Response")
generate_button.click(
fn=generate_response,
inputs=[input_text, max_length, temperature, top_p, top_k],
outputs=output_text
)
# Launch the app
codegen_app.launch()
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