Update app.py

This code incorporates the best aspects of the previous responses, providing a well-structured Streamlit app with a dedicated sidebar for navigation and instructions, comprehensive user input handling, and a placeholder for your actual model processing logic. Remember to replace the placeholders and comments with your specific project details and model code. This will create a user-friendly and informative Streamlit app for your Hugging Face Space.

app.py

@@ -1,35 +1,69 @@
 import streamlit as st
 
-# Title and description
-st.title("WhiteRabbitNeo Llama 3 WhiteRabbitNeo 8B V2.0 🚀")
-st.write("This Space showcases WhiteRabbitNeo Llama 3 WhiteRabbitNeo 8B V2.0, a powerful [**insert short description of your project here**].")
+# 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**].")
 
-# User input section with clear instructions
+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")
-user_input = st.text_input("Enter your input here (e.g., text, image, code snippet)", key="user_input")
 
-# Optional: Input validation or guidance based on your project's requirements
-# You can use libraries like validators or custom logic to check input validity
+# 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 (replace with your specific logic)
+# Model processing and results section
 if st.button("Run Model"):
     if user_input:
         # Simulate model processing (replace with actual model call)
-        processing_text = f"Processing your input: {user_input}..."
-        st.info(processing_text)
-        import time
-        time.sleep(2)  # Simulate processing time
+        with st.spinner("Processing..."):
+            import time
+            time.sleep(2)  # Simulate processing time
 
-        # Display model output (replace with your model's output format)
-        output_text = "This is a sample model output based on your input."
-        st.success(output_text)
-    else:
-        st.warning("Please enter some input to proceed.")
+            # 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) 
 
-# Additional sections for visualizations, explanations, or other functionalities (optional)
-# You can use Streamlit charts, images, and text to enhance user experience
+        # Display model output
+        st.success("Model Output:")
+        st.text_area(model_output, height=200)
 
-# Streamlit provides a variety of UI components like sliders, checkboxes, and radio buttons.
-# Choose the ones that best suit your project's interaction needs.
+# 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}"
 
-# Emphasize clear explanations and informative messages throughout the app.
+# Additional sections for visualizations, explanations, or other functionalities (optional)