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import streamlit as st
from huggingface_hub import InferenceClient
# Initialize the Inference client with the model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Function to generate a response
def respond(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
# Make the API call and stream the response
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
# Streamlit app layout
st.title("Zephyr Chatbot")
# Textbox for user input
user_message = st.text_input("Your message:")
# Text area for displaying chat history
history = st.session_state.get("history", [])
# System message (initialization)
system_message = st.text_area("System message", value="You are a friendly Chatbot.")
# Sliders for max tokens, temperature, and top-p
max_tokens = st.slider("Max new tokens", min_value=1, max_value=2048, value=512, step=1)
temperature = st.slider("Temperature", min_value=0.1, max_value=4.0, value=0.7, step=0.1)
top_p = st.slider("Top-p (nucleus sampling)", min_value=0.1, max_value=1.0, value=0.95, step=0.05)
# Button to send the message
if st.button("Send"):
# Get the response from the model
response_text = ""
for text in respond(user_message, history, system_message, max_tokens, temperature, top_p):
response_text = text
# Update chat history in session state
history.append((user_message, response_text))
st.session_state["history"] = history
# Display chat history
for user_msg, assistant_msg in history:
st.write(f"**You:** {user_msg}")
st.write(f"**Bot:** {assistant_msg}")
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