Create app.py

app.py

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+import streamlit as st
+import time
+import random
+import string
+
+# Page Configuration
+st.set_page_config(page_title="AutoTrain Advanced", page_icon="🚀", layout="wide")
+
+# Title
+st.title("AutoTrain Advanced 🚀")
+st.subheader("Train AI models without writing code")
+
+# Sidebar Configuration
+st.sidebar.header("Configuration")
+hf_user = st.sidebar.selectbox("Hugging Face User", ["hennings1984"])
+task = st.sidebar.selectbox(
+    "Select Task",
+    [
+        "LLM SFT", "LLM ORPO", "LLM Generic", "LLM DPO", "LLM Reward",
+        "VLM Captioning", "VLM VQA", "Text Classification", "Seq2Seq",
+        "Token Classification", "Image Classification", "Object Detection"
+    ]
+)
+hardware = st.sidebar.selectbox("Hardware", ["Local/Space"])
+parameter_mode = st.sidebar.radio("Parameter Mode", ["Basic", "Full"])
+
+# Function to Generate a Random Project Name
+def generate_project_name():
+    part1 = ''.join(random.choices(string.ascii_lowercase + string.digits, k=5))
+    part2 = ''.join(random.choices(string.ascii_lowercase + string.digits, k=5))
+    return f"autotrain-{part1}-{part2}"
+
+# Model & Dataset Selection
+st.write("### Model & Dataset Configuration")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    project_name = st.text_input("Project Name", value=generate_project_name())
+    base_model = st.selectbox("Base Model", ["Fetching models..."])
+    if st.button("Fetch Models"):
+        time.sleep(2)  # Simulating API request
+        models = ["GPT-3", "Llama-2", "BERT", "Custom"]
+        base_model = st.selectbox("Base Model", models)
+    if base_model == "Custom":
+        custom_model = st.text_input("Custom Model Name")
+
+with col2:
+    dataset_source = st.selectbox("Dataset Source", ["Local", "Hugging Face Hub"])
+    if dataset_source == "Hugging Face Hub":
+        hub_dataset = st.text_input("Enter Dataset Path (Hugging Face)")
+
+# File Upload Section
+st.write("### Upload Dataset")
+uploaded_train_files = st.file_uploader("Upload Training Data", accept_multiple_files=True)
+uploaded_valid_files = st.file_uploader("Upload Validation Data (Optional)", accept_multiple_files=True)
+
+# Accelerator Polling (Simulated)
+st.write("### Available Accelerators")
+if hardware == "Local/Space":
+    num_accelerators = random.randint(1, 4)  # Simulating response
+    st.write(f"🔧 Accelerators Available: **{num_accelerators}**")
+else:
+    st.write("⚠️ Accelerators available only in local mode.")
+
+# Display Selected Task & Parameters
+st.write("### Selected Parameters")
+st.json({
+    "User": hf_user,
+    "Task": task,
+    "Hardware": hardware,
+    "Parameter Mode": parameter_mode,
+    "Project Name": project_name,
+    "Base Model": base_model if base_model != "Custom" else custom_model,
+    "Dataset Source": dataset_source,
+    "Dataset Path": hub_dataset if dataset_source == "Hugging Face Hub" else "Local Upload"
+})
+
+# Training Buttons
+st.write("### Model Training Control")
+start_train = st.button("Start Training 🚀", key="start_train")
+stop_train = st.button("Stop Training ⛔", key="stop_train")
+
+if start_train:
+    st.success(f"Training started for {task} with {base_model} on {hardware}")
+
+if stop_train:
+    st.warning("Training stopped.")
+
+# Live Logs Simulation
+st.write("### Training Logs (Live Updates)")
+log_area = st.empty()
+if start_train:
+    for i in range(5):
+        log_area.text(f"Training step {i+1}... ✅")
+        time.sleep(1)
+
+# Footer
+st.markdown("---")
+st.markdown("Made with ❤️ by [Hugging Face](https://huggingface.co)")