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import gradio as gr
from transformers import pipeline
# Load the model with optimization settings
model = pipeline(
"text-generation",
model="rish13/polymers",
device=0 # Use device=0 for GPU (if available), -1 for CPU
)
def generate_response(prompt):
# Generate text from the model
response = model(prompt, max_length=100, num_return_sequences=1, temperature=0.7)
# Get the generated text from the response
generated_text = response[0]['generated_text']
# Find the position of the first end-of-sentence punctuation
end_punctuation = ['.', '!', '?']
end_position = -1
for punct in end_punctuation:
pos = generated_text.find(punct)
if pos != -1 and (end_position == -1 or pos < end_position):
end_position = pos
# If punctuation is found, truncate the text at that point
if end_position != -1:
generated_text = generated_text[:end_position + 1]
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()
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