app.py

# import numpy as np
# import streamlit as st
# from openai import OpenAI
# import os
# import sys
# from dotenv import load_dotenv, dotenv_values
# load_dotenv()





# # initialize the client
# client = OpenAI(
#   base_url="https://api-inference.huggingface.co/v1",
#   api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')#"hf_xxx" # Replace with your token
# ) 




# #Create supported models
# model_links ={
#     "Meta-Llama-3-8B":"meta-llama/Meta-Llama-3-8B-Instruct", 
#     "Mistral-7B":"mistralai/Mistral-7B-Instruct-v0.2",
#     "Gemma-7B":"google/gemma-1.1-7b-it",
#     "Gemma-2B":"google/gemma-1.1-2b-it",
#     "Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta",

# }

# #Pull info about the model to display
# model_info ={
#     "Mistral-7B":
#         {'description':"""The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#             \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over  **7 billion parameters.** \n""",
#         'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},
#     "Gemma-7B":        
#         {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#             \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over  **7 billion parameters.** \n""",
#         'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'},
#     "Gemma-2B":        
#     {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#         \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over  **2 billion parameters.** \n""",
#     'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'},
#     "Zephyr-7B":        
#     {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#         \nFrom Huggingface: \n\
#         Zephyr is a series of language models that are trained to act as helpful assistants. \
#         [Zephyr 7B Gemma](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\
#         is the third model in the series, and is a fine-tuned version of google/gemma-7b \
#         that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
#     'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png'},
#     "Zephyr-7B-β":        
#     {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#         \nFrom Huggingface: \n\
#         Zephyr is a series of language models that are trained to act as helpful assistants. \
#         [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\
#         is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 \
#         that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
#     'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png'},
#     "Meta-Llama-3-8B":
#     {'description':"""The Llama (3) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
#         \nIt was created by the [**Meta's AI**](https://llama.meta.com/) team and has over  **8 billion parameters.** \n""",
#     'logo':'Llama_logo.png'},
# }


# #Random dog images for error message
# random_dog = ["0f476473-2d8b-415e-b944-483768418a95.jpg",
#               "1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg",
#               "526590d2-8817-4ff0-8c62-fdcba5306d02.jpg",
#               "1326984c-39b0-492c-a773-f120d747a7e2.jpg",
#               "42a98d03-5ed7-4b3b-af89-7c4876cb14c3.jpg",
#               "8b3317ed-2083-42ac-a575-7ae45f9fdc0d.jpg",
#               "ee17f54a-83ac-44a3-8a35-e89ff7153fb4.jpg",
#               "027eef85-ccc1-4a66-8967-5d74f34c8bb4.jpg",
#               "08f5398d-7f89-47da-a5cd-1ed74967dc1f.jpg",
#               "0fd781ff-ec46-4bdc-a4e8-24f18bf07def.jpg",
#               "0fb4aeee-f949-4c7b-a6d8-05bf0736bdd1.jpg",
#               "6edac66e-c0de-4e69-a9d6-b2e6f6f9001b.jpg",
#               "bfb9e165-c643-4993-9b3a-7e73571672a6.jpg"]



# def reset_conversation():
#     '''
#     Resets Conversation
#     '''
#     st.session_state.conversation = []
#     st.session_state.messages = []
#     return None
    



# # Define the available models
# models =[key for key in model_links.keys()]

# # Create the sidebar with the dropdown for model selection
# selected_model = st.sidebar.selectbox("Select Model", models)

# #Create a temperature slider
# temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))


# #Add reset button to clear conversation
# st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button


# # Create model description
# st.sidebar.write(f"You're now chatting with **{selected_model}**")
# st.sidebar.markdown(model_info[selected_model]['description'])
# st.sidebar.image(model_info[selected_model]['logo'])
# st.sidebar.markdown("*Generated content may be inaccurate or false.*")





# if "prev_option" not in st.session_state:
#     st.session_state.prev_option = selected_model

# if st.session_state.prev_option != selected_model:
#     st.session_state.messages = []
#     # st.write(f"Changed to {selected_model}")
#     st.session_state.prev_option = selected_model
#     reset_conversation()



# #Pull in the model we want to use
# repo_id = model_links[selected_model]


# st.subheader(f'AI - {selected_model}')
# # st.title(f'ChatBot Using {selected_model}')

# # Set a default model
# if selected_model not in st.session_state:
#     st.session_state[selected_model] = model_links[selected_model] 

# # Initialize chat history
# if "messages" not in st.session_state:
#     st.session_state.messages = []


# # Display chat messages from history on app rerun
# for message in st.session_state.messages:
#     with st.chat_message(message["role"]):
#         st.markdown(message["content"])



# # Accept user input
# if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):

#     # Display user message in chat message container
#     with st.chat_message("user"):
#         st.markdown(prompt)
#     # Add user message to chat history
#     st.session_state.messages.append({"role": "user", "content": prompt})


#     # Display assistant response in chat message container
#     with st.chat_message("assistant"):

#         try:
#             stream = client.chat.completions.create(
#                 model=model_links[selected_model],
#                 messages=[
#                     {"role": m["role"], "content": m["content"]}
#                     for m in st.session_state.messages
#                 ],
#                 temperature=temp_values,#0.5,
#                 stream=True,
#                 max_tokens=3000,
#             )
    
#             response = st.write_stream(stream)

#         except Exception as e:
#             # st.empty()
#             response = "😵‍💫 Looks like someone unplugged something!\
#                     \n Either the model space is being updated or something is down.\
#                     \n\
#                     \n Try again later. \
#                     \n\
#                     \n Here's a random pic of a 🐶:"
#             st.write(response)
#             random_dog_pick = 'https://random.dog/'+ random_dog[np.random.randint(len(random_dog))]
#             st.image(random_dog_pick)
#             st.write("This was the error message:")
#             st.write(e)



        
#     st.session_state.messages.append({"role": "assistant", "content": response})

# import gradio as gr
# from huggingface_hub import InferenceClient

# """
# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# """
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


# def respond(
#     message,
#     history: list[tuple[str, str]],
#     system_message,
#     max_tokens,
#     temperature,
#     top_p,
# ):
#     messages = [{"role": "system", "content": system_message}]

#     for val in history:
#         if val[0]:
#             messages.append({"role": "user", "content": val[0]})
#         if val[1]:
#             messages.append({"role": "assistant", "content": val[1]})

#     messages.append({"role": "user", "content": message})

#     response = ""

#     for message in client.chat_completion(
#         messages,
#         max_tokens=max_tokens,
#         stream=True,
#         temperature=temperature,
#         top_p=top_p,
#     ):
#         token = message.choices[0].delta.content

#         response += token
#         yield response

# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# demo = gr.ChatInterface(
#     respond,
#     additional_inputs=[
#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
#         gr.Slider(
#             minimum=0.1,
#             maximum=1.0,
#             value=0.95,
#             step=0.05,
#             label="Top-p (nucleus sampling)",
#         ),
#     ],
# )


# if __name__ == "__main__":
#     demo.launch()
#####################################
# import gradio as gr

# gr.load("models/meta-llama/Meta-Llama-3.1-70B-Instruct").launch()
########################################
from openai import OpenAI
import streamlit as st
import os
import sys
from dotenv import load_dotenv, dotenv_values
load_dotenv()

st.title("ChatGPT-like clone")

client = OpenAI(api_key=os.environ.get["OPENAI_API_KEY"])

if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = "gpt-3.5-turbo"

if "messages" not in st.session_state:
    st.session_state.messages = []

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

if prompt := st.chat_input("What is up?"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    with st.chat_message("assistant"):
        stream = client.chat.completions.create(
            model=st.session_state["openai_model"],
            messages=[
                {"role": m["role"], "content": m["content"]}
                for m in st.session_state.messages
            ],
            stream=True,
        )
        response = st.write_stream(stream)
    st.session_state.messages.append({"role": "assistant", "content": response})