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app.py

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+import streamlit as st
+from langchain.prompts import PromptTemplate
+#from langchain.llms import CTransformers
+from langchain import HuggingFaceHub
+
+# Function to get response from Llama 2 model
+
+def getLlamaresponse(input_text, no_words, blog_style):
+    #llm = CTransformers(model = 'models\llama-2-7b-chat.Q8_0.gguf', model_type = 'llama',config  ={'max_new_tokens': 256, 'temperature':0.01})
+    llm = HuggingFaceHub(
+        repo_id='TheBloke/Llama-2-7B-Chat-GGUF',
+        model_kwargs={'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 Response from llama2
+    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")
+
+col1,col2 = st.columns([5,5])
+
+with col1:
+    no_words = st.text_input('No of Words')
+with col2:
+    blog_style = st.selectbox('Writing the blog for ', ('Researchers','Data Scientists','Common People'), index = 0)
+
+submit = st.button('Generate')
+
+if submit:
+    st.write(getLlamaresponse(input_text, no_words, blog_style))

requirements.txt

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