Blog Generation using LLAMA

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 from langchain.prompts import PromptTemplate
 from langchain_community.llms import CTransformers
+import time
 
 # List of blog styles
 BLOG_STYLES = [
@@ -34,41 +35,87 @@ def getLLamaResponse(input_text, no_words, blog_style):
     response = llm(prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words))
     return response
 
+# Function to generate topics from LLama 2 model
+def generate_topics_from_llama(input_text):
+    # Initialize the LLama 2 model
+    llm = CTransformers(model='models/llama-2-7b-chat.ggmlv3.q8_0.bin',
+                        model_type='llama',
+                        config={'max_new_tokens': 256, 'temperature': 0.01})
+
+    # Define the prompt template for generating topics
+    topic_template = """
+    Generate a list of blog topics based on the keywords: {input_text}
+    """
+    prompt = PromptTemplate(input_variables=["input_text"], template=topic_template)
+    
+    # Generate the topics from the LLama 2 model
+    topics_response = llm(prompt.format(input_text=input_text))
+    # Split the response into a list of topics
+    topics = topics_response.split('\n')
+    return [topic.strip() for topic in topics if topic.strip()]
+
 # Set up the Streamlit page configuration
-st.set_page_config(page_title="Generate Blogs",
+st.set_page_config(page_title="LLAMA 2 Generate Blogs",
                    page_icon='images/favicon.ico',  # Updated favicon source
                    layout='centered',
                    initial_sidebar_state='collapsed')
+
 # Display image using st.image
-st.image('images/ConcertIDC_Logo_Stack.png', width=50,caption='')
+st.image('images/ConcertIDC_Logo_Stack.png', width=50, caption='')
+
 # Page header with favicon icon
 st.markdown(
     """
     <h1 style="display:flex; align-items:center;">
-        Generate Blogs 
+        LLAMA 2 Generate Blogs 
     </h1>
     """, 
     unsafe_allow_html=True
 )
 
+# Placeholder for topics and selected topic
+if 'topics' not in st.session_state:
+    st.session_state.topics = []
+
+if 'selected_topic' not in st.session_state:
+    st.session_state.selected_topic = None
+
 # Input field for the blog topic
-input_text = st.text_input("Enter the Blog Topic")
+input_text = st.text_input("Enter the Blog Topic Keywords")
 
-# Create two columns for additional input fields
-col1, col2 = st.columns([5, 5])
+# Button to generate topics
+generate_topics = st.button("Generate Topics")
 
-with col1:
-    # Input field for the number of words
-    no_words = st.text_input('No of Words')
+# Generate and display topics
+if generate_topics:
+    with st.spinner('Generating topics...'):
+        st.session_state.topics = generate_topics_from_llama(input_text)
+        time.sleep(2)  # Simulate processing time
+
+# Display generated topics in bullet format
+if st.session_state.topics:
+    # st.markdown("### Suggested Topics")
+    # for topic in st.session_state.topics:
+    #     st.markdown(f"- {topic}")
     
-with col2:
+    # Selection for one of the topics
+    selected_topic = st.selectbox('Select a Topic', st.session_state.topics)
+    st.session_state.selected_topic = selected_topic
+
+    # Optional input field for the number of words
+    no_words = st.text_input('Number of Words (optional)', value='')
+
     # Dropdown selection for the blog style
     blog_style = st.selectbox('Writing the blog for', BLOG_STYLES, index=0)
     
-# Button to generate the blog
-submit = st.button("Generate")
+    # Button to generate the blog content
+    generate_blog = st.button("Generate Blog Content")
 
-# Display the generated blog response
-if submit:
-    response = getLLamaResponse(input_text, no_words, blog_style)
-    st.write(response)
+    # Display the generated blog response
+    if generate_blog:
+        with st.spinner('Generating blog content...'):
+            if no_words == '':
+                no_words = '500'  # Default to 500 words if not provided
+            response = getLLamaResponse(st.session_state.selected_topic, no_words, blog_style)
+            time.sleep(2)  # Simulate processing time
+            st.write(response)

app1.py

@@ -1,121 +0,0 @@
-import streamlit as st
-from langchain.prompts import PromptTemplate
-from langchain_community.llms import CTransformers
-import time
-
-# List of blog styles
-BLOG_STYLES = [
-    'Researchers', 
-    'Data Scientist', 
-    'Common People', 
-    'Software Engineers', 
-    'Product Managers', 
-    'Healthcare Professionals', 
-    'Teachers', 
-    'Entrepreneurs', 
-    'Marketers', 
-    'Students'
-]
-
-# Function to get response from LLama 2 model
-def getLLamaResponse(input_text, no_words, blog_style):
-    # Initialize the LLama 2 model
-    llm = CTransformers(model='models/llama-2-7b-chat.ggmlv3.q8_0.bin',
-                        model_type='llama',
-                        config={'max_new_tokens': 256, 'temperature': 0.01})
-
-    # Define the 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 from the LLama 2 model
-    response = llm(prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words))
-    return response
-
-# Function to generate topics from LLama 2 model
-def generate_topics_from_llama(input_text):
-    # Initialize the LLama 2 model
-    llm = CTransformers(model='models/llama-2-7b-chat.ggmlv3.q8_0.bin',
-                        model_type='llama',
-                        config={'max_new_tokens': 256, 'temperature': 0.01})
-
-    # Define the prompt template for generating topics
-    topic_template = """
-    Generate a list of blog topics based on the keywords: {input_text}
-    """
-    prompt = PromptTemplate(input_variables=["input_text"], template=topic_template)
-    
-    # Generate the topics from the LLama 2 model
-    topics_response = llm(prompt.format(input_text=input_text))
-    # Split the response into a list of topics
-    topics = topics_response.split('\n')
-    return [topic.strip() for topic in topics if topic.strip()]
-
-# Set up the Streamlit page configuration
-st.set_page_config(page_title="LLAMA 2 Generate Blogs",
-                   page_icon='images/favicon.ico',  # Updated favicon source
-                   layout='centered',
-                   initial_sidebar_state='collapsed')
-
-# Display image using st.image
-st.image('images/ConcertIDC_Logo_Stack.png', width=50, caption='')
-
-# Page header with favicon icon
-st.markdown(
-    """
-    <h1 style="display:flex; align-items:center;">
-        LLAMA 2 Generate Blogs 
-    </h1>
-    """, 
-    unsafe_allow_html=True
-)
-
-# Placeholder for topics and selected topic
-if 'topics' not in st.session_state:
-    st.session_state.topics = []
-
-if 'selected_topic' not in st.session_state:
-    st.session_state.selected_topic = None
-
-# Input field for the blog topic
-input_text = st.text_input("Enter the Blog Topic Keywords")
-
-# Button to generate topics
-generate_topics = st.button("Generate Topics")
-
-# Generate and display topics
-if generate_topics:
-    with st.spinner('Generating topics...'):
-        st.session_state.topics = generate_topics_from_llama(input_text)
-        time.sleep(2)  # Simulate processing time
-
-# Display generated topics in bullet format
-if st.session_state.topics:
-    # st.markdown("### Suggested Topics")
-    # for topic in st.session_state.topics:
-    #     st.markdown(f"- {topic}")
-    
-    # Selection for one of the topics
-    selected_topic = st.selectbox('Select a Topic', st.session_state.topics)
-    st.session_state.selected_topic = selected_topic
-
-    # Optional input field for the number of words
-    no_words = st.text_input('Number of Words (optional)', value='')
-
-    # Dropdown selection for the blog style
-    blog_style = st.selectbox('Writing the blog for', BLOG_STYLES, index=0)
-    
-    # Button to generate the blog content
-    generate_blog = st.button("Generate Blog Content")
-
-    # Display the generated blog response
-    if generate_blog:
-        with st.spinner('Generating blog content...'):
-            if no_words == '':
-                no_words = '500'  # Default to 500 words if not provided
-            response = getLLamaResponse(st.session_state.selected_topic, no_words, blog_style)
-            time.sleep(2)  # Simulate processing time
-            st.write(response)

Llama-Blog-Generation.webm → models/llama-2-7b-chat.ggmlv3.q8_0.bin

@@ -1,3 +1,3 @@
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