Update app.py

app.py

@@ -1,3 +1,9 @@
+"""
+Simple Chatbot
+@author: Nigel Gebodh
+@email: [email protected]
+"""
+
 import streamlit as st
 from openai import OpenAI
 import os
@@ -6,68 +12,64 @@ from dotenv import load_dotenv
 load_dotenv()
 
 # Initialize the client
-client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+client = OpenAI(
+    base_url="https://api-inference.huggingface.co/v1",
+    api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')  # Replace with your token
+)
 
-# Model link for Mistral
 model_link = "mistralai/Mistral-7B-Instruct-v0.2"
 
 def reset_conversation():
-    '''
-    Resets Conversation
-    '''
+    """Resets Conversation"""
+    st.session_state.conversation = []
     st.session_state.messages = []
+    return None
 
 # Set the temperature value directly in the code
 temperature = 0.5
 
-# Add reset button to clear conversation
-st.button('Reset Chat', on_click=reset_conversation)
+# Add a button to clear conversation
+if st.button('Reset Chat'):
+    reset_conversation()
 
 # Initialize chat history
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
+st.title("Mistral-7B Chatbot")
+st.subheader("Ask me anything!")
+
 # 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("Type your message here..."):
+prompt = st.chat_input("Type your message here...")
 
+if 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})
 
-    # Interact with the model
-    try:
-        # Construct the conversation context
-        conversation_context = [{"role": "system", "content": "You are a helpful assistant."}]
-        for msg in st.session_state.messages:
-            conversation_context.append(msg)
-
-        # Send the conversation context to the API
-        response = client.chat.completions.create(
-            model=model_link,
-            messages=conversation_context,
-            temperature=temperature,
-            max_tokens=150  # Adjust the max tokens according to your needs
-        )
-
-        assistant_response = response.choices[0].message["content"]
-
-        # Display assistant response in chat message container
-        with st.chat_message("assistant"):
-            st.markdown(assistant_response)
-
-        # Append the assistant's response to the chat history
-        st.session_state.messages.append({"role": "assistant", "content": assistant_response})
-
-    except Exception as e:
-        # Display error message to user
-        with st.chat_message("assistant"):
-            st.markdown("Sorry, I couldn't process your request. Please try again later.")
-        st.error(f"An error occurred: {e}")
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        try:
+            response = client.chat.completions.create(
+                model=model_link,
+                messages=[
+                    {"role": m["role"], "content": m["content"]}
+                    for m in st.session_state.messages
+                ],
+                temperature=temperature,
+                max_tokens=3000
+            )['choices'][0]['message']['content']
+
+            st.markdown(response)
+            st.session_state.messages.append({"role": "assistant", "content": response})
+
+        except Exception as e:
+            st.markdown("An error occurred. Please try again later.")
+            st.markdown(f"Error details: {e}")