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import gradio as gr
from transformers import pipeline
import torch
# Check if a GPU is available
device = 0 if torch.cuda.is_available() else -1
# Load the text-generation pipeline with the appropriate device
model = pipeline(
"text-generation",
model="rish13/polymers",
device=device # Automatically use GPU if available, otherwise CPU
)
def generate_response(prompt):
# Generate text from the model
response = model(
prompt,
max_length=50, # Adjusted to generate shorter text
num_return_sequences=1,
temperature=0.5, # Lowered to reduce randomness
top_k=50, # Limiting the next word selection
top_p=0.9 # Cumulative probability threshold
)
# Get the generated text from the response
generated_text = response[0]['generated_text']
return generated_text
# Define the Gradio interface
interface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(
lines=2,
placeholder="Enter your prompt here...",
label="Prompt",
elem_id="input-textbox" # Custom styling ID for input textbox
),
outputs=gr.Textbox(
label="Generated Text",
elem_id="output-textbox" # Custom styling ID for output textbox
),
title="Polymer Knowledge Model",
description=(
"This application uses a fine-tuned model to generate text related to polymers. "
"Enter a prompt to get started, and the model will generate relevant text."
),
theme="huggingface", # Apply a theme for consistent styling
layout="horizontal", # Arrange input and output side by side
live=True # Update the output live as the user types
)
# Launch the interface
interface.launch()
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