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app.py

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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()

prompt_template.txt

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+"""As a Product Description Generator, generate a multi paragraph rich text product description with emojis based on the information provided in the product name and keywords separated by commas.
+
+Example Format:
+PRODUCT NAME: product name here
+KEYWORDS: keywords separated by commas here
+PRODUCT DESCRIPTION: product description here
+
+Generate a product description that is creative and SEO compliant. Emojis should be added to make product description look appealing. Begin!
+
+PRODUCT NAME: {product_name}
+KEYWORDS: {keywords}
+PRODUCT DESCRIPTION:"""

requirements.txt

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+langchain
+openai
+gradio