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Upload app.py
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app.py
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# -*- coding: utf-8 -*-
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"""
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/1q7EXhcR6gncrcwySFbN7u9fOIwTc4LtD
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##*** Note : *** You will be NOT be charged for this exercise. Everything is OpenSource
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### This notebook presents how to make a simple conversational chatbot using Open Source language model that we will download from hugging-face hub
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pip install -qq -U langchain transformers sentence-transformers bitsandbytes accelerate
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!pip install
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!pip install transformers -U
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"""### Import the necessary libraries"""
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low_cpu_mem_usage=True)
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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"""### Setup
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"""
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response = llm_chain.run({"question":query,"chat_history":chat_history})
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return response
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"""###
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#### **Activity : ** Try the following things
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1. Search on the internet regarding the context length of the model
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Check when does the model reach its context limit and stops responding ?
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2. Can you do anything in the prompt to fit more conversations in the context length ?
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3. Can you programtically increase the context window ?
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4. Can you programatically make the memory management better ?
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"""
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chat_history = []
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# -*- coding: utf-8 -*-
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"""SimpleChatBot_OpenSourceModel_WithUI.ipynb
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/1q7EXhcR6gncrcwySFbN7u9fOIwTc4LtD
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##*** Note : *** You will be NOT be charged for this exercise. Everything is 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### This notebook presents how to make a simple conversational chatbot using Open Source language model that we will download from hugging-face hub
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pip install -qq -U langchain transformers sentence-transformers bitsandbytes accelerate
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!pip install --upgrade fastapi typing-extensions
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!pip install --upgrade gradio
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"""### Import the necessary libraries"""
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low_cpu_mem_usage=True)
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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"""### Setup a text generation pipeline
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"""
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response = llm_chain.run({"question":query,"chat_history":chat_history})
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return response
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"""### Create a UI for more interactive conversation!
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#### **Excercise **
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1. Can you make the UI look better ?
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2. Can you include more customizations (for example, setting up something like OpenAI playground ? Check out this link for how it looks like -> https://platform.openai.com/playground?mode=chat)
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3. Host your application by creating space on HuggingFaceHub
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"""
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import gradio as gr
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# Initialize chat history
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chat_history = []
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def chat_interface(query):
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global chat_history
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if query: # Ensure the query is not empty
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response = ask_me_chat_completions(query, chat_history, llm_chain)
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formatted_response = f"Q: {query}\nA: {response}" # Format the response
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chat_history.append(formatted_response) # Append the formatted response to the history
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return formatted_response # Return only the latest Q&A for display
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return "" # Return an empty string if the query is empty
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# Create the Gradio interface
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with gr.Blocks() as demo:
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with gr.Tab("Chat"):
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query_input = gr.Textbox(label="Ask your questions here", placeholder="Type your question and press submit...")
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submit_button = gr.Button("Submit")
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response_output = gr.Textbox(label="Response", interactive=False, lines=6, value="", placeholder="Your answer will appear here...")
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submit_button.click(chat_interface, inputs=query_input, outputs=response_output)
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# Launch the Gradio app
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demo.launch()
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