Spaces:
Sleeping
Sleeping
Commit ·
fe7ebd6
0
Parent(s):
blog generation initial setup
Browse files- .gitignore +3 -0
- README.md +6 -0
- app.py +52 -0
- requirements.txt +9 -0
.gitignore
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.env
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.vscode
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venv
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README.md
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---
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title: BlogGeneration
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app_file: app.py
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sdk: streamlit
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sdk_version: 1.25.0
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---
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app.py
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import streamlit as st
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import os
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from langchain.prompts import PromptTemplate
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from langchain_community.chat_models import ChatOpenAI
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from dotenv import load_dotenv
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load_dotenv()
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def getBlogContent(topic, number_of_words, field, points):
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llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"),
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model_name="gpt-3.5-turbo-1106", temperature=0)
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template = """
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You are an expert in "{field}". Write a blog to express your opinion on the following topic "{topic}".
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Here are few instructions to follow for writing the blog:
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* Use {number_of_words} words.
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* Use simple english.
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* Be succinct.
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Here are few points on the topic "{topic}" to consider to get you started:
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{points}
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"""
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prompt = PromptTemplate(
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template = template,
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input_variables = ['field', 'topic', 'number_of_words', 'points']
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)
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final_prompt = prompt.format(field=field, topic=topic, number_of_words=number_of_words, points=points)
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response = llm.invoke(final_prompt)
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print(response)
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return response.content
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st.set_page_config(page_title="Generate Blog", page_icon=":robot:",
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layout='centered', initial_sidebar_state='collapsed')
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st.header("Generate Blogs ")
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topic_text_field = st.text_input("Enter topic to generate blog")
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points_text_area = st.text_area("Enter points to consider")
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col1, col2 = st.columns([5,5])
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with col1:
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no_words = st.number_input("Number of words", value=100)
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with col2:
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expert_field = st.selectbox('Expertise Field',
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['Ruby On Rails', 'React', 'DevOps', 'Go Lang', 'Flutter Flow', 'NextJS'], index=0)
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submit_button = st.button("Generate")
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if submit_button:
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st.write(getBlogContent(topic_text_field, no_words, expert_field, points_text_area))
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requirements.txt
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sentence-transformers
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uvicorn
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langchain
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langchain-openai
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langchain-community
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huggingface_hub
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python-box
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streamlit
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python-dotenv
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