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app.py

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+# download model from here : https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main
+# model file name : llama-2-7b-chat.ggmlv3.q8_0.bin
+# Tut Ref : https://www.youtube.com/watch?v=_kYJZP1G9mE&t=754s
+# Github Link : https://github.com/SharathRaju489/Email-Generator-App-Langchain-LLAMA2-LLM/tree/main
+
+# Credit : https://github.com/SharathRaju489
+
+import streamlit as st
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+
+#Function to get the response back
+def getLLMResponse(form_input,email_sender,email_recipient,email_style):
+    #llm = OpenAI(temperature=.9, model="text-davinci-003")
+
+    # Wrapper for Llama-2-7B-Chat, Running Llama 2 on CPU
+
+    #Quantization is reducing model precision by converting weights from 16-bit floats to 8-bit integers, 
+    #enabling efficient deployment on resource-limited devices, reducing model size, and maintaining performance.
+
+    #C Transformers offers support for various open-source models, 
+    #among them popular ones like Llama, GPT4All-J, MPT, and Falcon.
+
+
+    #C Transformers is the Python library that provides bindings for transformer models implemented in C/C++ using the GGML library
+
+    llm = CTransformers(model='model/llama-2-7b-chat.ggmlv3.q8_0.bin',     #https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main
+                    model_type='llama',
+                    config={'max_new_tokens': 256,
+                            'temperature': 0.01})
+    
+    
+    #Template for building the PROMPT
+    template = """
+    Write a email with {style} style and includes topic :{email_topic}.\n\nSender: {sender}\nRecipient: {recipient}
+    \n\nEmail Text:
+    
+    """
+
+    #Creating the final PROMPT
+    prompt = PromptTemplate(
+    input_variables=["style","email_topic","sender","recipient"],
+    template=template,)
+
+  
+    #Generating the response using LLM
+    response=llm(prompt.format(email_topic=form_input,sender=email_sender,recipient=email_recipient,style=email_style))
+    print(response)
+
+    return response
+
+
+st.set_page_config(page_title="Generate Emails",
+                    page_icon='📧',
+                    layout='centered',
+                    initial_sidebar_state='collapsed')
+st.header("Generate Emails 📧")
+
+form_input = st.text_area('Enter the email topic', height=275)
+
+#Creating columns for the UI - To receive inputs from user
+col1, col2, col3 = st.columns([10, 10, 5])
+with col1:
+    email_sender = st.text_input('Sender Name')
+with col2:
+    email_recipient = st.text_input('Recipient Name')
+with col3:
+    email_style = st.selectbox('Writing Style',
+                                    ('Formal', 'Appreciating', 'Not Satisfied', 'Neutral'),
+                                       index=0)
+
+
+submit = st.button("Generate")
+
+#When 'Generate' button is clicked, execute the below code
+if submit:
+    st.write(getLLMResponse(form_input,email_sender,email_recipient,email_style))

blogGen.py

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+import os
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+
+model_path = 'model/llama-2-7b-chat.ggmlv3.q8_0.bin'
+
+def get_llama_response(input_text, no_words, blog_style):
+    # Ensure the model file exists
+    if not os.path.exists(model_path)
+        print(f"Error: Model file '{model_path}' not found.")
+        return None
+
+    # Initialize the LLM
+    try:
+        llm = CTransformers(model=model_path, model_type='llama', config={'max_new_tokens': 256, 'temperature': 0.01})
+    except RuntimeError as e:
+        print(f"Error: Could not create model. Error: {e}")
+        return None
+
+    print("Model file found & loaded...")
+
+    # Prepare the prompt template
+    template = f"""Write a blog for {blog_style} job profile on the topic '{input_text}' within {no_words} words."""
+
+    # Generate the response from the LLama 2 model
+    try:
+        response = llm(template)
+        return response
+    except Exception as e:
+        print(f"Error: Could not generate response. Error: {e}")
+        return None
+
+# Example usage
+input_text = "large language model"
+no_words = 50
+blog_style = 'Researchers'
+
+model_response = get_llama_response(input_text, no_words, blog_style)
+print(model_response)

details.txt

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+pip install sentence-transformers
+pip install uvicorn
+pip install ctransformers
+pip install langchain
+pip install python-box
+#!pip install streamlit
+
+# !pip install -r requirements.txt
+
+
+!pip install --upgrade ctransformers langchain
+
+pip show sentence-transformers uvicorn ctransformers langchain python-box

requirements.txt

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