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app.py

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+import streamlit as st
+from langchain.llms import CTransformers
+from langchain.prompts import PromptTemplate
+
+
+## Function to get response form the LLama2 Model
+def getLLamaResponse(input_text, no_of_words, blog_style):
+    llm = CTransformers(model='/Users/karthiksagar/BlogGeneration/llama-2-7b-chat.bin',
+                        model_type='llama',
+                        config={"max_new_tokens": 256,
+                                'temperature': 0.01})
+    template = """
+        write a blog for {blog_style} job profile for a topic {input_text}
+        within {no_of_words} words.
+            """
+    prompt = PromptTemplate(input_variables=["blog_style", "input_text", "no_of_words"],
+                            template=template)
+    response = llm(prompt.format(blog_style=blog_style, input_text=input_text, no_of_words=no_of_words))
+    st.write(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 columns for additional two fields
+
+col1, col2 = st.columns([5,5])
+
+with col1:
+    no_of_words = st.text_input("Enter the No. of words")
+with col2:
+    blog_style = st.selectbox("writing the blog for",
+                              ('Researchers', 'Data Scientists', 'ML Engineers', 'Common People'),
+                              index=0)
+submit = st.button("Generate")
+
+#final respponse
+if submit:
+    st.write(getLLamaResponse(input_text, no_of_words, blog_style))

requirements.txt

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