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  1. app.py +56 -0
  2. requirements.txt +8 -0
app.py ADDED
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+ import streamlit as st
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+ from langchain.prompts import PromptTemplate
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+ from langchain_community.llms import CTransformers
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+
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+
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+
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+ def getLLamaResponse(input_text,no_words,blog_style):
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+
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+ # LLma Model
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+ llm = CTransformers(
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+ model="models/llama-2-7b-chat.ggmlv3.q8_0.bin",
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+ model_type="llma",
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+ config={"max_new_tokens": 256, "temperature": 0.01}
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+ )
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+
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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
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+ {input_text} within {no_words} words.
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+ """
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+
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+ prompt = PromptTemplate(input_variables=["blog_style", "input_text","no_words"],
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+ template=template)
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+
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+ # Generate the response from the LLama 2 Model
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+ response = llm(prompt.format(style=blog_style, text=input_text, no_words=no_words))
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+ print(response)
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+ return response
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+
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+
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+
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+ st.set_page_config(page_title = "Generate Blogs",
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+ page_icon = "🤖",
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+ layout = "centered",
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+ initial_sidebar_state = "collapsed")
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+
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+ st.header("Generate Blogs 🤖")
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+
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+ input_text = st.text_input("Enter the Blog Topic")
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+
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+ # Creating 2 more columns for additional 2 fields
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+ col1, col2 = st.columns([5,5])
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+
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+ with col1:
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+ no_words = st.text_input("No of words")
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+
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+ with col2:
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+ blog_style=st.selectbox("Writing the blog for",
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+ ("Researchers","Data Scientist","Common People"),index=0)
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+
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+ submit = st.button("Generate")
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+
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+ # Final Response
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+ if submit:
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+ st.write(getLLamaResponse(input_text,no_words,blog_style))
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+
requirements.txt ADDED
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+ sentence-transformers
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+ uvicorn
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+ ctransformers
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+ fastapi
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+ ipykernel
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+ langchain
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+ python-box
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+ streamlit