Update app.py

app.py

@@ -1,164 +1,164 @@
 
-import streamlit as st
-from openai import OpenAI
-import os
-import sys
-from dotenv import load_dotenv, dotenv_values
-load_dotenv()
+# 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
-) 
+# # 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 ={
-    "Mistral-7b":"mistralai/Mistral-7B-Instruct-v0.2",
-    "Mistral-8x7b":"mistralai/Mixtral-8x7B-Instruct-v0.1" 
-    # "Gemma-7B":"google/gemma-7b-it",
-    # "Gemma-2B":"google/gemma-2b-it",
-    # "Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta",
+# #Create supported models
+# model_links ={
+#     "Mistral-7b":"mistralai/Mistral-7B-Instruct-v0.2",
+#     "Mistral-8x7b":"mistralai/Mixtral-8x7B-Instruct-v0.1" 
+#     # "Gemma-7B":"google/gemma-7b-it",
+#     # "Gemma-2B":"google/gemma-2b-it",
+#     # "Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta",
 
-}
+# }
 
-#Pull info about the model to display
-model_info ={
-    "Mistral-7b":
-        {'description':"""The Mistral 7B 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'},
+# #Pull info about the model to display
+# model_info ={
+#     "Mistral-7b":
+#         {'description':"""The Mistral 7B 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'},
 
-        "Mistral-8x7b":
-        {'description':"""The Mistral 8x7B 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-8x7b/) team as has based on MOE arch.** \n""",
-        'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},
+#         "Mistral-8x7b":
+#         {'description':"""The Mistral 8x7B 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-8x7b/) team as has based on MOE arch.** \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'},
-
-}
-
-def reset_conversation():
-    '''
-    Resets Conversation
-    '''
-    st.session_state.conversation = []
-    st.session_state.messages = []
-    return None
+#     # "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'},
+
+# }
+
+# 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()]
+# # 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 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))
+# #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.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? Try the [back-up](https://huggingface.co/spaces/ngebodh/SimpleChatbot-Backup).")
+# # 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.*")
+# # st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).")
+# # st.sidebar.markdown("\nRun into issues? Try the [back-up](https://huggingface.co/spaces/ngebodh/SimpleChatbot-Backup).")
 
 
 
-if "prev_option" not in st.session_state:
-    st.session_state.prev_option = selected_model
+# 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()
+# 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]
+# #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}')
+# 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] 
+# # 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 = []
+# # 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"])
+# # 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"):
+# # 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 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"):
-        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,
-        )
+#     # Display assistant response in chat message container
+#     with st.chat_message("assistant"):
+#         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)
-    st.session_state.messages.append({"role": "assistant", "content": response})
+#         response = st.write_stream(stream)
+#     st.session_state.messages.append({"role": "assistant", "content": response})
 
 
 
@@ -168,54 +168,54 @@ if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
 
 
 
-# from huggingface_hub import InferenceClient
-# import gradio as gr  
-# client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+from huggingface_hub import InferenceClient
+import gradio as gr  
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
-# def format_prompt(message, history):
-#   prompt = "<s>"
-#   for user_prompt, bot_response in history:
-#     prompt += f"[INST] {user_prompt} [/INST]"
-#     prompt += f" {bot_response}</s> "
-#   prompt += f"[INST] {message} [/INST]"
-#   return prompt
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
 
-# def generate(
-#     prompt, history, temperature=0.2, max_new_tokens=3000, top_p=0.95, repetition_penalty=1.0,
-# ):
-#     temperature = float(temperature)
-#     if temperature < 1e-2:
-#         temperature = 1e-2
-#     top_p = float(top_p)
+def generate(
+    prompt, history, temperature=0.2, max_new_tokens=3000, top_p=0.95, repetition_penalty=1.0,
+):
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
 
-#     generate_kwargs = dict(
-#         temperature=temperature,
-#         max_new_tokens=max_new_tokens,
-#         top_p=top_p,
-#         repetition_penalty=repetition_penalty,
-#         do_sample=True,
-#         seed=42,
-#     )
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
 
-#     formatted_prompt = format_prompt(prompt, history)
+    formatted_prompt = format_prompt(prompt, history)
 
-#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-#     output = ""
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
 
-#     for response in stream:
-#         output += response.token.text
-#         yield output
-#     return output
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
 
     
-# mychatbot = gr.Chatbot(
-#     avatar_images=["./user.png", "./bot.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
+mychatbot = gr.Chatbot(
+    avatar_images=["./user.png", "./bot.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
 
-# demo = gr.ChatInterface(fn=generate, 
-#                         chatbot=mychatbot,
-#                         title="Mistral-Chat",
-#                         retry_btn=None,
-#                         undo_btn=None
-#                        )
+demo = gr.ChatInterface(fn=generate, 
+                        chatbot=mychatbot,
+                        title="Mistral-Chat",
+                        retry_btn=None,
+                        undo_btn=None
+                       )
 
-# demo.queue().launch(show_api=False)
+demo.queue().launch(show_api=False)