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pages/Fine Tune.py

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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}")

pages/Host & Deploy.py

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+import streamlit as st
+import time
+
+# Streamlit App
+st.title("AI Model Deployment 🚀")
+
+# Intro
+st.write("""
+Welcome to the AI model deployment flow! Here, we'll follow the process of deploying 
+your fine-tuned AI model to one of the cloud instances. Let's begin!
+""")
+
+# Select cloud provider
+cloud_provider = st.selectbox("Choose a cloud provider:", ["AWS EC2", "Google Cloud VM", "Azure VM"])
+st.write(f"You've selected {cloud_provider}!")
+
+# Specify model details
+model_name = st.text_input("Enter your AI model name:", "MySpecialModel")
+if model_name:
+    st.write(f"We'll deploy the model named: {model_name}")
+
+# Button to start the deployment
+if st.button("Start Deployment"):
+    st.write("Deployment started... Please wait!")
+    
+    # Simulate progress bar for deployment
+    latest_iteration = st.empty()
+    bar = st.progress(0)
+    for i in range(100):
+        # Update the progress bar with each iteration.
+        latest_iteration.text(f"Deployment progress: {i+1}%")
+        bar.progress(i + 1)
+        time.sleep(0.05)
+
+    st.write(f"Deployment completed! Your model {model_name} is now live on {cloud_provider} 🌐")
+
+# Sidebar for additional settings (pretend configurations)
+st.sidebar.title("Deployment Settings")
+instance_type = st.sidebar.selectbox("Instance Type:", ["Standard", "High Memory", "High CPU", "GPU"])
+storage_option = st.sidebar.slider("Storage Size (in GB):", 10, 500, 50)
+st.sidebar.write(f"Instance Type: {instance_type}")
+st.sidebar.write(f"Storage Size: {storage_option} GB")