Merge pull request #2 from koji/feat_add-base-chat-app

app.py

@@ -1,4 +1,132 @@
 import streamlit as st
+from cerebras.cloud.sdk import Cerebras
+import openai
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Set page configuration
+st.set_page_config(page_icon="🤖", layout="wide", page_title="Cerebras")
+
+
+def icon(emoji: str):
+    """Shows an emoji as a Notion-style page icon."""
+    st.write(
+        f'<span style="font-size: 78px; line-height: 1">{emoji}</span>',
+        unsafe_allow_html=True,
+    )
+
+
+# Display header
+icon("🧠")
+st.title("ChatBot with Cerebras API")
+st.subheader("Deploying Cerebras on Streamlit", divider="orange", anchor=False)
+
+# Define model details
+models = {
+    "llama3.1-8b": {"name": "Llama3.1-8b", "tokens": 8192, "developer": "Meta"},
+    "llama-3.3-70b": {"name": "Llama-3.3-70b", "tokens": 8192, "developer": "Meta"}
+}
+
+BASE_URL = "http://localhost:8000/v1"
+
+# Sidebar configuration
+with st.sidebar:
+    st.title("Settings")
+    st.markdown("### :red[Enter your Cerebras API Key below]")
+    api_key = st.text_input("Cerebras API Key:", type="password")
+
+    # Model selection
+    model_option = st.selectbox(
+        "Choose a model:",
+        options=list(models.keys()),
+        format_func=lambda x: models[x]["name"],
+        key="model_select"
+    )
+
+    # Max tokens slider
+    max_tokens_range = models[model_option]["tokens"]
+    max_tokens = st.slider(
+        "Max Tokens:",
+        min_value=512,
+        max_value=max_tokens_range,
+        value=max_tokens_range,
+        step=512,
+        help="Select the maximum number of tokens (words) for the model's response."
+    )
+
+    use_optillm = st.toggle("Use Optillm", value=False)
+
+# Check for API key before proceeding
+if not api_key:
+    st.markdown("""
+    ## Cerebras API x Streamlit Demo!
+
+    This simple chatbot app demonstrates how to use Cerebras with Streamlit.
+
+    To get started:
+    1. :red[Enter your Cerebras API Key in the sidebar.]
+    2. Chat away, powered by Cerebras.
+    """)
+    st.stop()
+
+# Initialize Cerebras client
+# client = Cerebras(api_key=api_key)
+
+if use_optillm:
+    client = openai.OpenAI(
+        base_url="http://localhost:8000/v1",
+        api_key=api_key
+    )
+else:
+    client = Cerebras(api_key=api_key)
+
+
+# Chat history management
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "selected_model" not in st.session_state:
+    st.session_state.selected_model = None
+
+# Clear history if model changes
+if st.session_state.selected_model != model_option:
+    st.session_state.messages = []
+    st.session_state.selected_model = model_option
+
+# Display chat messages
+for message in st.session_state.messages:
+    avatar = '🤖' if message["role"] == "assistant" else '🦔'
+    with st.chat_message(message["role"], avatar=avatar):
+        st.markdown(message["content"])
+
+# Chat input and processing
+if prompt := st.chat_input("Enter your prompt here..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    with st.chat_message("user", avatar='🦔'):
+        st.markdown(prompt)
+
+    try:
+        # Create empty container for streaming response
+        with st.chat_message("assistant", avatar="🤖"):
+            response_placeholder = st.empty()
+            full_response = ""
+
+            # Stream the response
+            for chunk in client.chat.completions.create(
+                model=model_option,
+                messages=[{"role": "user", "content": prompt}],
+                max_tokens=max_tokens,
+                stream=True,  # Ensure Cerebras API supports streaming
+                # base_url=BASE_URL
+            ):
+                if chunk.choices[0].delta.content:
+                    chunk_content = chunk.choices[0].delta.content
+                    full_response += chunk_content
+                    response_placeholder.markdown(full_response + "▌")
+
+            # Update the final response without cursor
+            response_placeholder.markdown(full_response)
+            st.session_state.messages.append(
+                {"role": "assistant", "content": full_response})
+
+    except Exception as e:
+        st.error(f"Error generating response: {str(e)}", icon="🚨")

requirements.txt

@@ -0,0 +1,2 @@
+cerebras_cloud_sdk
+openai