app.py

import streamlit as st

# Sidebar with navigation and instructions
st.sidebar.title("WhiteRabbitNeo Llama 3 WhiteRabbitNeo 8B V2.0")
st.sidebar.markdown("**Welcome!** This Space showcases a powerful [**insert short description of your project here**].")

st.sidebar.header("Instructions")
st.sidebar.markdown("""
* **Enter your input** in the text area or upload a file.
* **Adjust parameters** (if applicable) like temperature and max tokens.
* **Click "Run Model"** to generate output.
""")

st.sidebar.header("About")
st.sidebar.markdown("""
* **Model Type:** [Specify the type of model (e.g., NLP, Computer Vision)]
* **Framework:** [Name of the deep learning framework used (e.g., TensorFlow, PyTorch)]
* **Size:** [Indicate the model size (e.g., parameters, FLOPs)]
""")

# Main content area
st.title("Interact with the Model")

# User input section with enhanced features
st.header("Interact with the Model")

# 1. Multiple Input Types
user_input_text = st.text_area("Enter your text input here:", height=150)
user_input_file = st.file_uploader("Upload a file (optional)", type=["txt", "pdf"]) 

if user_input_file is not None:
    user_input = user_input_file.getvalue().decode("utf-8") 
else:
    user_input = user_input_text

# 2. Input Validation and Guidance
if not user_input:
    st.warning("Please enter some input.")

# 3. Parameter Control (if applicable)
model_temperature = st.slider("Model Temperature", 0.0, 1.0, 0.7, 0.1) 
max_tokens = st.number_input("Max Tokens", min_value=10, max_value=1000, value=50)

# Model processing and results section
if st.button("Run Model"):
    if user_input:
        # Simulate model processing (replace with actual model call)
        with st.spinner("Processing..."):
            import time
            time.sleep(2)  # Simulate processing time

            # Example: Incorporate parameters into model call
            # (Replace with your actual model logic)
            model_output = process_input(user_input, temperature=model_temperature, max_tokens=max_tokens) 

        # Display model output
        st.success("Model Output:")
        st.text_area(model_output, height=200)

# Helper function for model processing (replace with your actual model logic)
def process_input(input_text, temperature, max_tokens):
    # This is a placeholder. 
    # Replace with your actual model interaction logic here.
    # Example: 
    #   import your model
    #   generate output using model.generate(input_text, temperature=temperature, max_tokens=max_tokens) 
    return f"This is a sample output based on: {input_text}, temperature: {temperature}, max_tokens: {max_tokens}"

# Additional sections for visualizations, explanations, or other functionalities (optional)