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import gradio as gr
#gr.Interface.load("models/nitinbhayana/Llama-2-7b-chat-hf-review-phrases-sentiments-v2").launch()
from transformers import pipeline
#pipeline = pipeline("text-generation", model="nitinbhayana/Llama-2-7b-chat-hf-review-phrases-sentiments-v2")
pipeline = pipeline("text-generation", model="nitinbhayana/TinyLlama-1.1B-Chat-v1.0-title-suggestion-v1.0")
def predict(title):
prompt=f"""<s>[INST] <<SYS>> You are a helpful assistant that provides accurate and concise responses. <</SYS>>
Create a new, easy to read, and error free title for a given Ecommerce product title.
[Title] {title} [/Title]
[/INST]
### Suggested Title:"""
# prompt=f"""<s>[INST] <<SYS>>
# You are a helpful, respectful, and honest assistant for product feature mapping.
# <</SYS>>
# [INST]
# For a given e-commerce product title.
# [TITLE]
# {title}
# [/TITLE]
# Create a new, easy-to-read, error-free title.
# [/INST]
# Suggested Title:
# """
predictions = pipeline(prompt)
output=((predictions[0]['generated_text']).split('Suggested Title:')[-1]).strip()
return (output)
gr.Interface(
predict,
inputs='text',
outputs='text',
title="Title Suggestion",
).launch() |