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Browse files- app.py +52 -0
- requirements.txt +5 -0
app.py
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import streamlit as st
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from langchain.prompts import PromptTemplate
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from langchain.llms import CTransformers
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### Function to get response from the llama model
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def getLLamaresponse(input_text, no_words, blog_style):
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#llama Model
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llm = CTransformers(model="models\llama-2-7b-chat.ggmlv3.q8_0.bin",model_type="llama",
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config={"max_new_tokens":256, "temperature":0.01})
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#Prompt Template
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template="""
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write a blog for {blog_style} job profile for a topic {input_text} within {no_words} words.
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"""
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prompt = PromptTemplate(input_variables=['blog_style', 'input_text', 'no_words'], template=template)
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#Generate the response
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response = llm(prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words))
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print(response)
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return response
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st.set_page_config(
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page_title="Blog Generation",
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page_icon="",
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layout="centered",
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initial_sidebar_state="collapsed")
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st.header("Blog Generation")
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input_text = st.text_input("Enter the text you want to generate a blog on")
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## Creating 2 more field
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## 1. Number of words to generate
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## 2. Number of blogs to generate
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col_1, col_2 = st.columns([5,5])
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with col_1:
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num_words = st.number_input("Enter the number of words to generate", min_value=10, max_value=1000, value=100)
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with col_2:
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blog_style = st.selectbox("Select the blog style", ["researchers", "layman", "technical_audience"], index=0)
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submit = st.button("Generate Blog")
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if submit:
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st.write(getLLamaresponse(input_text, num_words, blog_style))
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requirements.txt
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sentence-transformers
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uvicorn
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streamlit
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langchain
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python-box
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