main.py.save

import gradio as grimport os
import keras
import keras_nlp

import os

os.environ["KERAS_BACKEND"] = "jax"  
# Avoid memory fragmentation on JAX backend.
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00"

import os

# Set Kaggle API credentials
os.environ["KAGGLE_USERNAME"] = "rogerkorantenng"
os.environ["KAGGLE_KEY"] = "9a33b6e88bcb6058b1281d777fa6808d"

# Load environment variables
load_dotenv()

# Replace this with the path or method to load your local model
gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")

def generate_response(message, history):
    # Format the conversation history for the local model
    formatted_history = []
    for user, assistant in history:
        formatted_history.append(f"Instruction:\n{user}\n\nResponse:\n{assistant}")

    # Add the latest user message to the history
    formatted_history.append(f"Instruction:\n{message}\n\nResponse:\n")

    # Join formatted history into a single string for input
    input_text = "\n".join(formatted_history)

    # Generate response from the local model
    # Make sure to adjust this part according to your model's API
    response = gemma_lm.generate(input_text, max_length=256)
    
    # Extract the response text
    # Adjust the response extraction based on the actual structure of your model's output
    return response[0]  # Change this line if necessary

# Create the Gradio interface
gr.ChatInterface(
    generate_response,
    chatbot=gr.Chatbot(height=300),
    textbox=gr.Textbox(placeholder="You can ask me anything", container=False, scale=7),
    title="Local Model Chat Bot",
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear"
).launch(share=True)