initial commit

app.py

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+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain_community.llms import CTransformers
+
+# load the model
+
+def getLlamaResponse(input_text, no_words, blog_style):
+    llm=CTransformers(model='models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf',
+        model_type='llama',
+        config={'max_new_tokens':256, 'temperature':0.2})
+
+    # create a prompt
+
+    template="""
+    Write a blog post about the topic: {input_text} in {blog_style} style. The blog should be {no_words} words long.
+    """
+    prompt=PromptTemplate(input_variables=["blog_style", "input_text", "no_words"], 
+                          template=template)
+    
+    # generate the response
+    response=llm.invoke(prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words))
+    
+    return response
+
+# Streamlit UI
+st.set_page_config(page_title="GenBlog Demo", 
+                   page_icon="📚", 
+                   layout="centered",
+                   initial_sidebar_state='collapsed')
+st.header("GenBlog Demo 📚")
+input_text=st.text_input("Enter the Blog Topic")
+col1, col2 = st.columns([5, 5])
+with col1:
+    no_words=st.text_input("Enter the number of words", value=100)
+with col2:
+    blog_style=st.selectbox("Select the Blog Style", ["Personal", "Research", "Story Driven"])
+submit=st.button("Generate Blog")
+if submit:
+    st.write(getLlamaResponse(input_text, no_words, blog_style))

requirements.txt

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