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from __future__ import annotations
import os, openai
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from typing import Any
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
import gradio as gr
# OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
OPENAI_API_KEY='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'
prompt_file = "prompt_template.txt"
class ProductDescGen(LLMChain):
"""LLM Chain specifically for generating multi paragraph rich text product description using emojis."""
@classmethod
def from_llm(
cls, llm: BaseLanguageModel, prompt: str, **kwargs: Any
) -> ProductDescGen:
"""Load ProductDescGen Chain from LLM."""
return cls(llm=llm, prompt=prompt, **kwargs)
def product_desc_generator(product_name, keywords):
with open(prompt_file, "r") as file:
prompt_template = file.read()
PROMPT = PromptTemplate(
input_variables=["product_name", "keywords"], template=prompt_template
)
llm = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0.7,
openai_api_key=OPENAI_API_KEY,
)
ProductDescGen_chain = ProductDescGen.from_llm(llm=llm, prompt=PROMPT)
ProductDescGen_query = ProductDescGen_chain.apply_and_parse(
[{"product_name": product_name, "keywords": keywords}]
)
return ProductDescGen_query[0]["text"]
with gr.Blocks() as demo:
gr.HTML("""<h1>Product Description Enhancer</h1>""")
gr.Markdown(
"Generate Product Description for your products instantly!<br>"
"Provide product name and keywords related to that product. Click on 'Generate Description' button and multi-paragraph rich text product description will be genrated instantly.<br>"
"Note: Generated product description is SEO compliant and can be used to populate product information."
)
with gr.Tab("Generate Product Description!"):
product_name = gr.Textbox(
label="Product Name",
placeholder="Nike Shoes",
)
keywords = gr.Textbox(
label="Keywords (separated by commas)",
placeholder="black shoes, leather shoes for men, water resistant",
)
product_description = gr.Textbox(label="Product Description")
click_button = gr.Button(value="Generate Description!")
click_button.click(
product_desc_generator, [product_name, keywords], product_description
)
demo.launch()
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