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Update app.py
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app.py
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### app.py code is taken from https://huggingface.co/spaces/ngebodh/SimpleChatbot-Backup/blob/main/app.py
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### https://medium.com/@nigelgebodh/large-language-models-chatting-with-ai-chatbots-from-google-mistral-ai-and-hugging-face-b33efedea38d
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""" Simple Chatbot
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@author: Sagar Padhiyar
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@email: spadhiyar230595@gmail.com
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"""
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import streamlit as st
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from
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import os
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import sys
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from dotenv import load_dotenv, dotenv_values
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from huggingface_hub import InferenceClient
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load_dotenv()
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# client = OpenAI(
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# base_url="https://api-inference.huggingface.co/v1",
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# api_key=os.environ.get('HUGGINGFACE_API')#"hf_xxx" # Replace with your token
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# )
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base_url="https://api-inference.huggingface.co/v1"
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API_KEY = os.environ.get('HUGGINGFACE_API')
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#Create supported models
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model_links ={
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"Mistral":base_url+"mistralai/Mistral-7B-Instruct-v0.2",
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"Gemma-7B":base_url+"google/gemma-7b-it",
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# "Llama-2":"meta-llama/Llama-2-7b-chat-hf"
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}
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headers = {"Authorization":"Bearer "+API_KEY}
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#Pull info about the model to display
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model_info ={
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"Mistral":
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}
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def reset_conversation():
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'''
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Resets Conversation
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st.session_state.conversation = []
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st.session_state.messages = []
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return None
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def format_promt(message, custom_instructions=None):
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if custom_instructions:
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promt += f"[INST] {custom_instructions} [/INST]"
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promt += f"[INST] {message} [/INST]"
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return promt
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# Define the available models
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models =[key for key in model_links.keys()]
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# Create the sidebar with the dropdown for model selection
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#Create a temperature slider
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temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
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#Add reset button to clear conversation
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st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button
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# Create model description
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st.sidebar.write(f"You're now chatting with **{selected_model}**")
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st.sidebar.markdown(model_info[selected_model]['description'])
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st.sidebar.markdown("*Generated content may be inaccurate or false.*")
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st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).")
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if "prev_option" not in st.session_state:
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st.session_state.prev_option = selected_model
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st.session_state.prev_option = selected_model
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reset_conversation()
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#Pull in the model we want to use
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repo_id = model_links[selected_model]
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st.subheader(f'AI - {selected_model}')
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# st.title(f'ChatBot Using {selected_model}')
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# Set a default model
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if selected_model not in st.session_state:
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st.session_state[selected_model] = model_links[selected_model]
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Accept user input
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if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
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custom_instruction = "Act like a Human in conversation"
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formated_text = format_promt(prompt, custom_instruction)
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# Display user message in chat message container
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with st.chat_message("user"):
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st.markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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# Display assistant response in chat message container
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with st.chat_message("assistant"):
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output = client.text_generation(
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formated_text,
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temperature=temp_values,#0.5
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)
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response = st.write_stream(output)
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st.session_state.messages.append({"role": "assistant", "content": response})
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import streamlit as st
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from huggingface_hub import InferenceClient
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import os
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import sys
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st.title("ChatGPT-like Chat boat")
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base_url="https://api-inference.huggingface.co/models/"
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API_KEY = os.environ.get('HUGGINGFACE_API')
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headers = {"Authorization":"Bearer "+API_KEY}
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model_links ={
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"Mistral":base_url+"mistralai/Mistral-7B-Instruct-v0.2",
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"Gemma-7B":base_url+"google/gemma-7b-it",
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# "Llama-2":"meta-llama/Llama-2-7b-chat-hf"
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}
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#Pull info about the model to display
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model_info ={
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"Mistral":
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}
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def format_promt(message, custom_instructions=None):
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prompt = ""
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if custom_instructions:
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prompt += f"[INST] {custom_instructions} [/INST]"
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prompt += f"[INST] {message} [/INST]"
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return prompt
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def reset_conversation():
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'''
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Resets Conversation
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st.session_state.conversation = []
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st.session_state.messages = []
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return None
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models =[key for key in model_links.keys()]
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# Create the sidebar with the dropdown for model selection
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#Create a temperature slider
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temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
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#Add reset button to clear conversation
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st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button
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# Create model description
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st.sidebar.write(f"You're now chatting with **{selected_model}**")
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st.sidebar.markdown(model_info[selected_model]['description'])
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st.sidebar.markdown("*Generated content may be inaccurate or false.*")
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st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).")
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if "prev_option" not in st.session_state:
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st.session_state.prev_option = selected_model
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st.session_state.prev_option = selected_model
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reset_conversation()
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#Pull in the model we want to use
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repo_id = model_links[selected_model]
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st.subheader(f'AI - {selected_model}')
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# st.title(f'ChatBot Using {selected_model}')
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Accept user input
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if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
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custom_instruction = "Act like a Human in conversation"
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# Display user message in chat message container
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with st.chat_message("user"):
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st.markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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formated_text = format_promt(prompt, custom_instruction)
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# Display assistant response in chat message container
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with st.chat_message("assistant"):
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output = client.text_generation(
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formated_text,
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temperature=temp_values,#0.5
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max_new_tokens=3000,
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stream=True
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)
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response = st.write_stream(output)
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st.session_state.messages.append({"role": "assistant", "content": response})
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