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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}") |