Update app.py

app.py

@@ -1,203 +1,203 @@
 
-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
+# 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()]
+# # 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.*")
+# # 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 "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"):
+#     # 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,
-            )
+#         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)
+#             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)
+#         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})
+#     st.session_state.messages.append({"role": "assistant", "content": response})
 
 # import gradio as gr
 # from huggingface_hub import InferenceClient
@@ -267,29 +267,36 @@ if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
 
 # gr.load("models/meta-llama/Meta-Llama-3.1-70B-Instruct").launch()
 ########################################
-# import streamlit as st
-# from transformers import AutoTokenizer, AutoModelForCausalLM
+from openai import OpenAI
+import streamlit as st
 
-# # Load model directly
-# tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
-# model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
+st.title("ChatGPT-like clone")
 
-# # Initialize chat history
-# if "chat_history" not in st.session_state:
-#     st.session_state.chat_history = []
-
-# # Display chat history
-# for chat in st.session_state.chat_history:
-#     st.write(f"User: {chat['user']}")
-#     st.write(f"Response: {chat['response']}")
-
-# # Get user input
-# user_input = st.text_input("Enter your message:")
-
-# # Generate response
-# if st.button("Send"):
-#     inputs = tokenizer(user_input, return_tensors="pt")
-#     outputs = model.generate(**inputs)
-#     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-#     st.session_state.chat_history.append({"user": user_input, "response": response})
-#     st.write(f"Response: {response}")
+client = OpenAI(api_key=st.secrets["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})