Update pages/descriptionGen.py

pages/descriptionGen.py

@@ -1,50 +1,65 @@
+from __future__ import annotations
 import streamlit as st
 from langchain.prompts import PromptTemplate
 from langchain.chat_models import ChatOpenAI
 from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
+from typing import Any
 import openai
 import os
 
-# Set up the API key (you can also use st.secrets for security)
-OPENAI_API_KEY = st.text_input("OPENAI_API_KEY", type="password")
-
+# OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
 prompt_file = "prompt_template.txt"
 
+
 class ProductDescGen(LLMChain):
-    """LLM Chain specifically for generating multi-paragraph rich text product description using emojis."""
+    """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:
+    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, api_key):
+
+def product_desc_generator(product_name, keywords, OPENAI_API):
     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=api_key)
+    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,
+        openai_api_key=OPENAI_API,
+    )
 
     ProductDescGen_chain = ProductDescGen.from_llm(llm=llm, prompt=PROMPT)
-    ProductDescGen_query = ProductDescGen_chain.apply_and_parse([{"product_name": product_name, "keywords": keywords}])
+    ProductDescGen_query = ProductDescGen_chain.apply_and_parse(
+        [{"product_name": product_name, "keywords": keywords}]
+    )
     return ProductDescGen_query[0]["text"]
 
-st.title("Product Description Generator")
-
-st.write(
-    "Generate multi-paragraph rich text product descriptions for your products instantly!"
-    " Provide the product name and keywords related to the product."
-)
-
-product_name = st.text_input("Product Name", "Nike Shoes")
-keywords = st.text_input("Keywords (separated by commas)", "black shoes, leather shoes for men, water resistant")
+def main():
+    st.title("Product Description Generator")
+    st.write(
+        "Generate multi-paragraph rich text product descriptions for your products instantly!"
+        " Provide the product name and keywords related to the product."
+    )
+    OPENAI_API = st.text_input("Open Ai Key", "yut_86876vv87tbyuttvubyurvt")
+    product_name = st.text_input("Product Name", "Nike Shoes")
+    keywords = st.text_input("Keywords (separated by commas)", "black shoes, leather shoes for men, water resistant")
 
-if st.button("Generate Description"):
-    if OPENAI_API_KEY:
-        description = product_desc_generator(product_name, keywords, OPENAI_API_KEY)
-        st.subheader("Product Description:")
-        st.text(description)
-    else:
-        st.warning("Please provide your OpenAI API Key.")
+    if st.button("Generate Description"):
+        OPENAI_API_KEY:
+            description = product_desc_generator(product_name, keywords, OPENAI_API)
+            st.subheader("Product Description:")
+            st.text(description)
+        else:
+            st.warning("Please provide your OpenAI API Key.")
 
+if __name__ == "__main__":
+    main()