Update app.py

app.py

@@ -1,306 +1,105 @@
+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
 
-# import numpy as np
-# import streamlit as st
+# 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"
 
-
-# # 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
+# 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)
-#     # 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})
+#         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})