Create app.py

app.py

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+import streamlit as st
+import os
+from together import Together
+
+
+client = Together(api_key=os.environ["TOGETHER_API_KEY"])
+
+
+def call_llama(prompt: str) -> str:
+    """
+        Send a prompt to the Llama model and return the response.
+        Args:
+            prompt (str): The input prompt to send to the Llama model.
+
+        Returns:
+            str: The response from the Llama model.
+    """
+
+    # Create a completion request with the prompt
+    response = client.chat.completions.create(
+
+        # Use the Llama-3-8b-chat-hf model
+        model="meta-llama/Llama-3-8b-chat-hf",
+
+        # Define the prompt as a user message
+        messages=[
+            {
+                "role": "user",
+                "content": prompt  # Use the input prompt
+            }
+        ],
+    )
+
+    # Return the content of the first response message
+    return response.choices[0].message.content
+
+
+with st.sidebar:
+    with st.expander("Instruction Manual"):
+        st.markdown("""
+            ## Meta Llama3 🦙 Chatbot
+
+            This Streamlit app allows you to chat with Meta's Llama3 model.
+
+            ### How to Use:
+            1. **Input**: Type your prompt into the chat input box labeled "What is up?".
+            2. **Response**: The app will display a response from Llama3.
+            3. **Chat History**: Previous conversations will be shown on the app.
+
+            ### Credits:
+            - **Developer**: Yiqiao Yin | [Site](https://www.y-yin.io/) | [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/) | [YouTube](https://youtube.com/YiqiaoYin/)
+
+            Enjoy chatting with Meta's Llama3 model!
+        """)
+
+    # Add a button to clear the session state
+    if st.button("Clear Session"):
+        st.session_state.messages = []
+        st.experimental_rerun()
+
+
+st.title("Meta Llama3 🦙")
+
+
+# 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"])
+
+
+# React to user input
+if prompt := st.chat_input("What is up?"):
+    # Display user message in chat message container
+    st.chat_message("user").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    response = call_llama(prompt)
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st.markdown(response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})