Update app.py

app.py

@@ -7,164 +7,60 @@ 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()
-
-
-
+from dotenv import load_dotenv
 
+load_dotenv()
 
-# initialize the client
+# 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",
-    #"Meta-Llama-3.1-8B":"meta-llama/Meta-Llama-3.1-8B-Instruct", #TODO: Update when/if Serverless Inference available
-
+    base_url="https://api-inference.huggingface.co/v1",
+    api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')  # Replace with your token
+)
+
+# Define Llama 3 model
+model_link = "meta-llama/Meta-Llama-3-8B-Instruct"
+model_info = {
+    'description': """The Llama (3) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n 
+                      It was created by the [**Meta's AI**](https://llama.meta.com/) team and has over **8 billion parameters.** \n""",
+    'logo': 'Llama_logo.png'
 }
 
-#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'},
-    "Meta-Llama-3.1-8B":
-    {'description':"""The Llama (3.1) 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':'Llama3_1_logo.png'},
-}
-
-
-#Random dog images for error message
+# 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"]
-
-
+              "1326984c-39b0-492c-a773-f120d747a7e2.jpg"]
 
 def reset_conversation():
-    '''
-    Resets 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
+# 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
-
+# 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.write(f"You're now chatting with **Llama 3**")
+st.sidebar.markdown(model_info['description'])
+st.sidebar.image(model_info['logo'])
 st.sidebar.markdown("*Generated content may be inaccurate or false.*")
-st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).")
-st.sidebar.markdown("\nRun into issues? \nTry coming back in a bit, GPU access might be limited or something is down.")
-
-
-
-
-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] 
+st.sidebar.markdown("\nRun into issues? \nTry again later as GPU access might be limited.")
 
 # 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"):
+if prompt := st.chat_input(f"Hi, I'm Llama 3, ask me a question"):
 
     # Display user message in chat message container
     with st.chat_message("user"):
@@ -172,18 +68,17 @@ if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
     # 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],
+                model=model_link,
                 messages=[
                     {"role": m["role"], "content": m["content"]}
                     for m in st.session_state.messages
                 ],
-                temperature=temp_values,#0.5,
+                temperature=temp_values,
                 stream=True,
                 max_tokens=3000,
             )
@@ -191,20 +86,11 @@ if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
             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 🐶:"
+            response = "😵‍💫 Looks like something went wrong! Try again later.\nHere's a random pic of a 🐶:"
             st.write(response)
-            random_dog_pick = 'https://random.dog/'+ random_dog[np.random.randint(len(random_dog))]
+            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})
+    st.session_state.messages.append({"role": "assistant", "content": response})