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app.py

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+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+
+# functio to get response from LLAMA 2 model 
+
+def get_llama_response(input_text,no_words,blog_style):
+
+    ### LLama 2 model 
+    llm = CTransformers(model = 'models\llama-2-7b-chat.ggmlv3.q8_0.bin',
+                        model_type = 'llama',
+                        config = {'max_new_tokens': 256,
+                                'temperature': 0.01})
+
+
+    ## Prompt Template
+    template = """
+    write a blog for {blog_style} job profile for a topic  {input_text}
+    within  {no_words} words
+    """
+
+    prompt = PromptTemplate(input_vairables =['blog_style','input_text','no_words'],
+                            template = template)
+
+    ## Generate the response from LLMA 2 model
+
+    response  = llm(prompt.format(blog_style=blog_style , input_text = input_text , no_words = no_words))
+    print(response)
+    return response 
+
+
+
+st.set_page_config(page_title = 'Generate Blogs',
+                   page_icon = '',
+                   layout = 'centered',
+                   initial_sidebar_state = 'collapsed')
+
+st.header('Generate Blogs ')
+
+input_text = st.text_input('Enter the blog Topic')
+
+## creating two more columns additional 2 fields
+
+col1 , col2 = st.columns([5,5])
+
+with col1 :
+    no_words = st.text_input('No. of words ')
+
+
+with col2 :
+    blog_style = st.selectbox('Wiriting the blog for ',
+    ('Researchers','Data Scientist','Common People'),index=0)
+
+submit = st.button('Generate')
+
+## final response
+
+if submit :
+    st.write(get_llama_response(input_text,no_words,blog_style))

requirements.txt

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+sentence-transformers
+unicorn
+ctransformers
+fastapi
+ipykernel
+langchain
+python-box
+streamlit
+langchain_community