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| import streamlit as st | |
| import time | |
| # Streamlit App | |
| st.title("AI Model Fine-Tuning 🤖") | |
| # Intro | |
| st.write(""" | |
| Welcome to the AI model fine-tuning! Here, we'll take a vanilla AI model and | |
| follow the fine-tuning process to adapt it for a specific task. Let's get started! | |
| """) | |
| # Select model type | |
| model_type = st.selectbox("Choose a vanilla AI model:", ["BERT", "LLaMa 2", "ResNet", "Transformer"]) | |
| st.write(f"You've selected the {model_type} model!") | |
| # Specify dataset | |
| dataset_name = st.text_input("Enter the name of the dataset for fine-tuning:", "Knowledgebase-Dataset.csv") | |
| if dataset_name: | |
| st.write(f"We will use the {dataset_name} dataset for fine-tuning!") | |
| # Button to start the fine-tuning | |
| if st.button("Start Fine-Tuning"): | |
| st.write("Fine-tuning started... Please wait!") | |
| # Simulate progress bar for fine-tuning | |
| latest_iteration = st.empty() | |
| bar = st.progress(0) | |
| for i in range(100): | |
| # Update the progress bar with each iteration. | |
| latest_iteration.text(f"Fine-tuning progress: {i+1}%") | |
| bar.progress(i + 1) | |
| time.sleep(0.35) | |
| st.write("Fine-tuning completed! Your model is now ready to deploy 🚀") | |
| # Sidebar for additional settings (pretend parameters) | |
| st.sidebar.title("Fine-Tuning Settings") | |
| learning_rate = st.sidebar.slider("Learning Rate:", 0.001, 0.1, 0.01, 0.001) | |
| batch_size = st.sidebar.slider("Batch Size:", 8, 128, 32) | |
| epochs = st.sidebar.slider("Number of Epochs:", 1, 10, 3) | |
| st.sidebar.write(f"Learning Rate: {learning_rate}") | |
| st.sidebar.write(f"Batch Size: {batch_size}") | |
| st.sidebar.write(f"Epochs: {epochs}") |