Update app.py

app.py

@@ -1,4 +1,145 @@
 import streamlit as st
+from huggingface_hub import InferenceClient
+import os
+import sys
+
+st.title("ChatGPT-like Chatbot")
+
+base_url="https://api-inference.huggingface.co/models/"
+
+API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
+headers = {"Authorization":"Bearer "+API_KEY}
+
+model_links ={
+    "Mistral-7B":base_url+"mistralai/Mistral-7B-Instruct-v0.2",
+    "Mistral-22B":base_url+"mistral-community/Mixtral-8x22B-v0.1",
+    # "Gemma-2B":base_url+"google/gemma-2b-it",
+    # "Zephyr-7B-β":base_url+"HuggingFaceH4/zephyr-7b-beta",
+    # "Llama-2":"meta-llama/Llama-2-7b-chat-hf"
+}
+
+#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'},
+    #"Mistral-22B":
+    #    {'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-22b/) team as has over  **22 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'},
+
+}
+
+def format_promt(message, custom_instructions=None):
+    prompt = ""
+    if custom_instructions:
+        prompt += f"[INST] {custom_instructions} [/INST]"
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def reset_conversation():
+    '''
+    Resets Conversation
+    '''
+    st.session_state.conversation = []
+    st.session_state.messages = []
+    return None
+
+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.*")
+st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).")
+
+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}')
+
+# 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"):
+
+    custom_instruction = "Act like a Human in conversation"
+
+    # 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})
+
+    formated_text = format_promt(prompt, custom_instruction)
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        client = InferenceClient(
+            model=model_links[selected_model],
+            headers=headers)
+
+        output = client.text_generation(
+            formated_text,
+            temperature=temp_values,#0.5
+            max_new_tokens=3000,
+            stream=True
+        )
+
+        response = st.write_stream(output)
+    st.session_state.messages.append({"role": "assistant", "content": response})
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)