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Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd user
+
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD [ "uvicorn","app:app","--host","0.0.0.0","--port","7860" ]

app.py

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+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+
+def getLLamaresponse(input_text, no_words, blog_style):
+    # Initialize the language model with the specified configuration
+    # Use a pipeline as a high-level helper
+    from transformers import pipeline
+    llm = pipeline("text-generation", model="TheBloke/Llama-2-7B-Chat-GGML")
+    # Define the template for the prompt
+    template = """
+    Write a blog in {blog_style} job profile for the topic "{input_text}" within {no_words} words.
+    """
+    
+    # Create the prompt using the template
+    prompt = PromptTemplate(input_variables=['blog_style', 'input_text', 'no_words'], template=template)
+    
+    # Format the prompt with the provided parameters
+    formatted_prompt = prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words)
+    
+    # Generate the response using the language model
+    response = llm(formatted_prompt)
+    
+    return response
+
+# Configure the Streamlit app
+st.set_page_config(page_title="Generate Blog",
+                   layout='centered',
+                   initial_sidebar_state='collapsed')
+
+# App header
+st.header("Generate Blog")
+
+# Input for the blog topic
+input_text = st.text_input("Enter the blog topic")
+
+# Columns for additional inputs
+col1, col2 = st.columns([5, 5])
+with col1:
+    no_words = st.text_input('Number of words')
+
+with col2:
+    blog_style = st.selectbox('Writing the blog for', ('researchers', 'data scientists', 'common people'), index=0)
+
+# Generate button
+submit = st.button("Generate")
+
+# Display the generated response
+if submit:
+    response = getLLamaresponse(input_text, no_words, blog_style)
+    st.write(response)

requirement.txt

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