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app.py

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+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+
+
+### Function to get response from the llama model
+
+def getLLamaresponse(input_text, no_words, blog_style):
+
+
+
+    #llama 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_variables=['blog_style', 'input_text', 'no_words'], template=template)
+
+    #Generate the response
+    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="Blog Generation",
+    page_icon="", 
+    layout="centered", 
+    initial_sidebar_state="collapsed")
+
+st.header("Blog Generation")
+
+input_text = st.text_input("Enter the text you want to generate a blog on") 
+
+## Creating 2 more field
+## 1. Number of words to generate
+## 2. Number of blogs to generate
+col_1, col_2 = st.columns([5,5])
+with col_1:
+    num_words = st.number_input("Enter the number of words to generate", min_value=10, max_value=1000, value=100)
+with col_2:
+    blog_style = st.selectbox("Select the blog style", ["researchers", "layman", "technical_audience"], index=0)
+
+submit = st.button("Generate Blog")
+
+if submit:
+    st.write(getLLamaresponse(input_text, num_words, blog_style))

requirements.txt

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+sentence-transformers
+uvicorn
+streamlit
+langchain
+python-box