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Update app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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pipeline = pipeline("text-generation", model="nitinbhayana/Llama-2-7b-chat-hf-keyword-category-brand-v1")
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def predict(search_term):
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You are a helpful assistant that provides accurate and concise responses. Do not hallucinate.
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<</SYS>>
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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Analyze the following keyword searched on amazon with intent of shopping. Identify the product category from the list ['Baby Products', 'Bags, Wallets and Luggage', 'Beauty', 'Books', 'Car & Motorbike', 'Clothing & Accessories', 'Computers & Accessories', 'Electronics', 'Garden & Outdoors', 'Gift Cards', 'Grocery & Gourmet Foods', 'Health & Personal Care', 'Home & Kitchen', 'Home Improvement', 'Industrial & Scientific', 'Jewellery', 'Kindle Store', 'Movies & TV Shows', 'Music', 'Musical Instruments', 'Office Products', 'Pet Supplies', 'Shoes & Handbags', 'Software', 'Sports, Fitness & Outdoors', 'Toys & Games', 'Video Games', 'Watches']. Extract the brand from keyword related to brand loyalty intent.\nOutput in JSON with keyword, product category, brand as keys.
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### Input:
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{search_term}
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[/INST]"""
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gr.Interface(
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).launch()
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import gradio as gr
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gr.Interface.load("models/nitinbhayana/Llama-2-7b-chat-hf-keyword-category-brand-v1").launch()
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# from transformers import pipeline
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# pipeline = pipeline("text-generation", model="nitinbhayana/Llama-2-7b-chat-hf-keyword-category-brand-v1")
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# def predict(search_term):
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# prompt=f"""[INST] <<SYS>>
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# You are a helpful assistant that provides accurate and concise responses. Do not hallucinate.
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# <</SYS>>
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# Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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# ### Instruction:
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# Analyze the following keyword searched on amazon with intent of shopping. Identify the product category from the list ['Baby Products', 'Bags, Wallets and Luggage', 'Beauty', 'Books', 'Car & Motorbike', 'Clothing & Accessories', 'Computers & Accessories', 'Electronics', 'Garden & Outdoors', 'Gift Cards', 'Grocery & Gourmet Foods', 'Health & Personal Care', 'Home & Kitchen', 'Home Improvement', 'Industrial & Scientific', 'Jewellery', 'Kindle Store', 'Movies & TV Shows', 'Music', 'Musical Instruments', 'Office Products', 'Pet Supplies', 'Shoes & Handbags', 'Software', 'Sports, Fitness & Outdoors', 'Toys & Games', 'Video Games', 'Watches']. Extract the brand from keyword related to brand loyalty intent.\nOutput in JSON with keyword, product category, brand as keys.
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# ### Input:
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# {search_term}
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# [/INST]"""
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# predictions = pipeline(prompt)
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# return (predictions)
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# gr.Interface(
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# predict,
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# inputs='text',
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# outputs='text',
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# title="Keyword-Category-Brand-Mapping",
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# ).launch()
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