Update app.py

app.py

@@ -1,40 +1,80 @@
-""" Simple Chatbot
-@author: Nigel Gebodh
-@email: nigel.gebodh@gmail.com
-"""
-
 import streamlit as st
-import os
 import requests
 import json
+import os
+from dotenv import load_dotenv
 
-entire_assistant_response = ""
+load_dotenv()
 
-def get_streamed_response(message, history, model):
-    all_message = []
+def reset_conversation():
+    '''
+    Resets Conversation
+    '''
+    st.session_state.conversation = []
+    st.session_state.messages = []
+    return None
 
-    if not history:  # If history is empty
-        all_message.append({"role": "user", "content": ""})
-        history = [("", "")]  # Add dummy values to prevent unpacking error
+# Define model links for Hugging Face models
+model_links = {
+    "Mistral": "mistralai/Mistral-7B-Instruct-v0.2",
+    "Gemma-7B": "google/gemma-7b-it",
+    "Gemma-2B": "google/gemma-2b-it",
+    "Zephyr-7B-β": "HuggingFaceH4/zephyr-7b-beta",
+    "Nous-Hermes-2-Yi-34B": "NousResearch/Nous-Hermes-2-Yi-34B"
+}
 
-    for human, assistant in history:
-        all_message.append({"role": "user", "content": human})
-        all_message.append({"role": "assistant", "content": assistant})
+# Define model info for all models
+model_info = {
+    "Mistral": {
+        'description': "The Mistral model is a Large Language Model (LLM) developed by Mistral AI.",
+        'logo': 'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'
+    },
+    "Gemma-7B": {
+        'description': "The Gemma-7B model is a Large Language Model (LLM) developed by Google with 7 billion parameters.",
+        'logo': 'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'
+    },
+    "Gemma-2B": {
+        'description': "The Gemma-2B model is a Large Language Model (LLM) developed by Google with 2 billion parameters.",
+        'logo': 'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'
+    },
+    "Zephyr-7B-β": {
+        'description': "The Zephyr-7B-β model is a Large Language Model (LLM) developed by HuggingFace.",
+        'logo': 'https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png'
+    },
+    "Nous-Hermes-2-Yi-34B": {
+        'description': "The Nous Hermes model is a Large Language Model (LLM) developed by Nous Research with 34 billion parameters.",
+        'logo': 'https://example.com/nous_hermes_logo.png'
+    }
+}
 
-    global entire_assistant_response
-    entire_assistant_response = ""  # Reset the entire assistant response
+# Function to interact with Hugging Face models
+def interact_with_huggingface_model(messages, model):
+    # Add your code here to interact with the Hugging Face model
+    pass
 
-    all_message.append({"role": "user", "content": message})
+# Function to interact with the Together API model
+def interact_with_together_api(messages):
+    all_messages = []
+
+    if not messages:  # If history is empty
+        all_messages.append({"role": "user", "content": ""})
+        history = [("", "")]  # Add dummy values to prevent unpacking error
+
+    for human, assistant in messages:
+        all_messages.append({"role": "user", "content": human})
+        all_messages.append({"role": "assistant", "content": assistant})
+
+    all_messages.append({"role": "user", "content": messages[-1][1]})
 
     url = "https://api.together.xyz/v1/chat/completions"
     payload = {
-        "model": model,
+        "model": "NousResearch/Nous-Hermes-2-Yi-34B",
         "temperature": 1.05,
         "top_p": 0.9,
         "top_k": 50,
         "repetition_penalty": 1,
         "n": 1,
-        "messages": all_message,
+        "messages": all_messages,
         "stream_tokens": True,
     }
 
@@ -73,62 +113,46 @@ def get_streamed_response(message, history, model):
                 print(f"KeyError encountered: {e}")
                 continue
 
-    print(entire_assistant_response)
-    all_message.append({"role": "assistant", "content": entire_assistant_response})
-
-
-# Initialize Streamlit app
-st.title("Simple Chatbot")
+# Create sidebar with model selection dropdown and temperature slider
+selected_model = st.sidebar.selectbox("Select Model", list(model_links.keys()))
+temperature = st.sidebar.slider('Select Temperature', 0.0, 1.0, 0.5)
+st.sidebar.button('Reset Chat', on_click=reset_conversation)
 
-# Initialize session state if not present
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Define available models
-models = {
-    "Mistral": "mistralai/Mistral-7B-Instruct-v0.2",
-    "Gemma-7B": "google/gemma-7b-it",
-    "Gemma-2B": "google/gemma-2b-it",
-    "Zephyr-7B-β": "HuggingFaceH4/zephyr-7b-beta",
-    "BibleLearnerAI": "NousResearch/Nous-Hermes-2-Yi-34B"
-}
-
-# Allow user to select a model
-selected_model = st.sidebar.selectbox("Select Model", list(models.keys()))
-
-# Create model description
+# Display model description and logo
 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 st.session_state.prev_option != selected_model:
+# Initialize chat history
+if "messages" not in st.session_state:
     st.session_state.messages = []
-    st.session_state.prev_option = selected_model
 
-#Pull in the model we want to use
-repo_id = models[selected_model]
-
-st.subheader(f'AI - {selected_model}')
+# 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.text_input(f"Hi I'm {selected_model}, ask me a question"):
-
-    # Display user message
-    with st.spinner("AI is typing..."):
-        st.session_state.messages.append({"role": "user", "content": prompt})
-
-        # Call selected model to get response
-        response_stream = get_streamed_response(prompt, [(m["content"] for m in st.session_state.messages[:-1])], repo_id)
-        for response in response_stream:
-            st.session_state.messages.append({"role": "assistant", "content": response})
-
-# Display chat history
-for message in st.session_state.messages:
-    if message["role"] == "user":
-        st.text_input("You:", value=message["content"], disabled=True)
+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(("user", prompt))
+
+    # Interact with selected model
+    if selected_model == "Nous-Hermes-2-Yi-34B":
+        stream = interact_with_together_api(st.session_state.messages)
     else:
-        st.text_input(f"{selected_model}:", value=message["content"], disabled=True)
+        interact_with_huggingface_model(st.session_state.messages, model_links[selected_model])
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        response = ""
+        for chunk in stream:
+            response = chunk
+            st.markdown(response)
+    st.session_state.messages.append(("assistant", response))