app.py

import streamlit as st
from huggingface_hub import InferenceClient

# Initialize the HuggingFace inference client
client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta", token=st.secrets["HUGGINGFACE_API_KEY"])

# Set up the Streamlit app
st.title("DarijaBot")

# Function to interact with the HuggingFace model
def darijabot_response(user_message):
    system_message = "Aidez à répondre à la question suivante en darija marocain."
    response = client.chat_completion(
        [{"role": "system", "content": system_message}, {"role": "user", "content": user_message}],
        max_tokens=512,
        temperature=0.7,
        top_p=0.95
    ).choices[0].message.content
    return response

# Input for user message
user_message = st.text_input("You:", "")

# Check if the user has entered a message
if user_message:
    try:
        bot_response = darijabot_response(user_message)
        st.text_area("DarijaBot:", bot_response, height=200)
    except Exception as e:
        st.error(f"Error: Unable to fetch response from the API.\nDetails: {e}")

# Instructions for users
st.write("""
## Instructions
- Enter your message in Darija (Moroccan Arabic) using the Latin alphabet.
- The chatbot will respond to your messages.
""")