app.py

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"),
    outputs="text",
    title="Polymer Knowledge Model",
    description="A model fine-tuned for generating text related to polymers."
)

# Launch the interface
interface.launch()